[FreeAllCourse.Com] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

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[FreeAllCourse.Com] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp

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文件列表

  • 16 Statistics - Practical Example Descriptive Statistics/093 Practical Example Descriptive Statistics.mp4 168.2 MB
  • 12 Probability - Distributions/066 A Practical Example of Probability Distributions.mp4 165.5 MB
  • 11 Probability - Bayesian Inference/051 A Practical Example of Bayesian Inference.mp4 152.2 MB
  • 40 Part 6 Mathematics/283 Why is Linear Algebra Useful.mp4 151.3 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/011 Techniques for Working with Traditional Data.mp4 145.0 MB
  • 10 Probability - Combinatorics/039 A Practical Example of Combinatorics.mp4 140.8 MB
  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.mp4 133.0 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/020 Types of Machine Learning.mp4 131.2 MB
  • 56 Software Integration/406 Taking a Closer Look at APIs.mp4 121.2 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/017 Techniques for Working with Traditional Methods.mp4 117.1 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/007 Continuing with BI ML and AI.mp4 114.3 MB
  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.mp4 109.1 MB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.mp4 108.5 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/395 Business Case Preprocessing.mp4 108.4 MB
  • 19 Statistics - Practical Example Inferential Statistics/118 Practical Example Inferential Statistics.mp4 107.6 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.mp4 104.1 MB
  • 13 Probability - Probability in Other Fields/067 Probability in Finance.mp4 103.9 MB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/226 Practical Example Linear Regression (Part 1).mp4 101.8 MB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.mp4 96.5 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.mp4 94.3 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/392 Business Case Getting Acquainted with the Dataset.mp4 91.9 MB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.mp4 90.7 MB
  • 09 Part 2 Probability/025 The Basic Probability Formula.mp4 90.1 MB
  • 51 Deep Learning - Business Case Example/356 Business Case Preprocessing the Data.mp4 88.4 MB
  • 12 Probability - Distributions/059 Characteristics of Continuous Distributions.mp4 88.2 MB
  • 20 Statistics - Hypothesis Testing/122 Rejection Region and Significance Level.mp4 86.6 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.mp4 85.4 MB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.mp4 85.1 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/422 Obtaining Dummies from a Single Feature.mp4 85.0 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/104 Confidence Intervals Population Variance Known Z-score.mp4 82.0 MB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.mp4 81.0 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/397 Creating a Data Provider.mp4 80.1 MB
  • 09 Part 2 Probability/026 Computing Expected Values.mp4 79.4 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.mp4 79.2 MB
  • 22 Part 4 Introduction to Python/138 Why Python.mp4 78.7 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/427 Classifying the Various Reasons for Absence.mp4 78.2 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.mp4 78.1 MB
  • 12 Probability - Distributions/052 Fundamentals of Probability Distributions.mp4 77.0 MB
  • 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.mp4 76.4 MB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.mp4 76.0 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.mp4 75.0 MB
  • 12 Probability - Distributions/053 Types of Probability Distributions.mp4 74.5 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.mp4 73.9 MB
  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.mp4 72.9 MB
  • 56 Software Integration/404 What are Data Servers Clients Requests and Responses.mp4 72.4 MB
  • 12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.mp4 72.2 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.mp4 71.0 MB
  • 51 Deep Learning - Business Case Example/353 Business Case Exploring the Dataset and Identifying Predictors.mp4 69.5 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/006 Business Analytics Data Analytics and Data Science An Introduction.mp4 67.6 MB
  • 56 Software Integration/408 Software Integration - Explained.mp4 66.8 MB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.mp4 66.6 MB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.mp4 65.9 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.mp4 65.8 MB
  • 01 Part 1 Introduction/002 What Does the Course Cover.mp4 65.3 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.mp4 64.9 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/418 Dropping a Column from a DataFrame in Python.mp4 64.8 MB
  • 09 Part 2 Probability/027 Frequency.mp4 64.7 MB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.mp4 64.6 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/300 Basic NN Example (Part 4).mp4 64.1 MB
  • 56 Software Integration/407 Communication between Software Products through Text Files.mp4 63.3 MB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.mp4 62.2 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB
  • 09 Part 2 Probability/028 Events and Their Complements.mp4 62.0 MB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.mp4 61.6 MB
  • 22 Part 4 Introduction to Python/137 Introduction to Programming.mp4 61.4 MB
  • 14 Part 3 Statistics/070 Population and Sample.mp4 60.9 MB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/233 Practical Example Linear Regression (Part 5).mp4 60.7 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.mp4 60.2 MB
  • 10 Probability - Combinatorics/034 Solving Combinations.mp4 60.1 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/437 Analyzing the Dates from the Initial Data Set.mp4 60.1 MB
  • 11 Probability - Bayesian Inference/043 Union of Sets.mp4 60.0 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/106 Confidence Interval Clarifications.mp4 59.8 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/466 Analyzing Age vs Probability in Tableau.mp4 59.3 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.mp4 59.1 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).mp4 58.8 MB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/231 Practical Example Linear Regression (Part 4).mp4 58.8 MB
  • 20 Statistics - Hypothesis Testing/126 p-value.mp4 58.6 MB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.mp4 58.5 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Dealing with Categorical Data - Dummy Variables.mp4 58.4 MB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.mp4 58.3 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.mp4 57.5 MB
  • 15 Statistics - Descriptive Statistics/072 Levels of Measurement.mp4 57.0 MB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.mp4 57.0 MB
  • 60 Case Study - Loading the absenteeism_module/463 Deploying the absenteeism_module - Part II.mp4 56.9 MB
  • 20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.mp4 56.9 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.mp4 56.2 MB
  • 11 Probability - Bayesian Inference/040 Sets and Events.mp4 56.1 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.mp4 56.0 MB
  • 26 Python - Conditional Statements/158 The ELIF Statement.mp4 55.9 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.mp4 55.7 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.mp4 55.3 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/452 Interpreting the Coefficients for Our Problem.mp4 54.9 MB
  • 57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.mp4 54.8 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.mp4 54.3 MB
  • 22 Part 4 Introduction to Python/140 Installing Python and Jupyter.mp4 53.5 MB
  • 49 Deep Learning - Preprocessing/338 Standardization.mp4 53.5 MB
  • 15 Statistics - Descriptive Statistics/085 Variance.mp4 53.4 MB
  • 23 Python - Variables and Data Types/146 Python Strings.mp4 53.1 MB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.mp4 52.8 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/103 What are Confidence Intervals.mp4 52.4 MB
  • 11 Probability - Bayesian Inference/050 Bayes Law.mp4 52.4 MB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.mp4 52.3 MB
  • 51 Deep Learning - Business Case Example/361 Business Case Setting an Early Stopping Mechanism.mp4 52.2 MB
  • 40 Part 6 Mathematics/275 Linear Algebra and Geometry.mp4 52.2 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/191 Decomposition of Variability.mp4 52.1 MB
  • 40 Part 6 Mathematics/282 Dot Product of Matrices.mp4 51.8 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.mp4 51.6 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.mp4 51.4 MB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp4 51.4 MB
  • 11 Probability - Bayesian Inference/049 The Multiplication Law.mp4 51.4 MB
  • 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.mp4 50.6 MB
  • 12 Probability - Distributions/061 Continuous Distributions The Standard Normal Distribution.mp4 50.2 MB
  • 17 Statistics - Inferential Statistics Fundamentals/102 Estimators and Estimates.mp4 50.1 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/438 Extracting the Month Value from the Date Column.mp4 50.1 MB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/374 TensorFlow Intro.mp4 50.0 MB
  • 11 Probability - Bayesian Inference/041 Ways Sets Can Interact.mp4 49.7 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.mp4 49.5 MB
  • 12 Probability - Distributions/065 Continuous Distributions The Logistic Distribution.mp4 49.3 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/388 MNIST Learning.mp4 49.0 MB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).mp4 48.2 MB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.mp4 48.1 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/446 Creating the Targets for the Logistic Regression.mp4 48.0 MB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.mp4 47.3 MB
  • 42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.mp4 47.3 MB
  • 52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.mp4 46.9 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.mp4 46.8 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.mp4 46.7 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.mp4 46.7 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.mp4 46.6 MB
  • 22 Part 4 Introduction to Python/139 Why Jupyter.mp4 46.5 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.mp4 46.3 MB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.mp4 46.1 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/386 Calculating the Accuracy of the Model.mp4 46.0 MB
  • 10 Probability - Combinatorics/033 Solving Variations without Repetition.mp4 45.2 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).mp4 45.1 MB
  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.mp4 45.0 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.mp4 44.9 MB
  • 10 Probability - Combinatorics/030 Permutations and How to Use Them.mp4 44.8 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.mp4 44.8 MB
  • 28 Python - Sequences/171 Dictionaries.mp4 43.7 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.mp4 43.6 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.mp4 43.5 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.mp4 43.5 MB
  • 10 Probability - Combinatorics/037 Combinatorics in Real-Life The Lottery.mp4 43.3 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 43.2 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.mp4 43.0 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.mp4 43.0 MB
  • 57 Case Study - Whats Next in the Course/411 Introducing the Data Set.mp4 42.8 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.mp4 42.6 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.mp4 42.6 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.mp4 42.5 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/454 Interpreting the Coefficients of the Logistic Regression.mp4 42.4 MB
  • 10 Probability - Combinatorics/035 Symmetry of Combinations.mp4 42.3 MB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.mp4 42.2 MB
  • 12 Probability - Distributions/064 Continuous Distributions The Exponential Distribution.mp4 42.2 MB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.mp4 41.7 MB
  • 52 Deep Learning - Conclusion/365 Summary on What Youve Learned.mp4 41.7 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.mp4 41.5 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/455 Backward Elimination or How to Simplify Your Model.mp4 41.5 MB
  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.mp4 41.3 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.mp4 41.3 MB
  • 57 Case Study - Whats Next in the Course/410 The Business Task.mp4 41.1 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).mp4 41.0 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.mp4 40.6 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/428 Using .concat() in Python.mp4 40.6 MB
  • 10 Probability - Combinatorics/038 A Recap of Combinatorics.mp4 40.4 MB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.mp4 40.4 MB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.mp4 40.3 MB
  • 42 Deep Learning - Introduction to Neural Networks/290 The Linear model with Multiple Inputs and Multiple Outputs.mp4 40.2 MB
  • 27 Python - Python Functions/161 How to Create a Function with a Parameter.mp4 40.0 MB
  • 40 Part 6 Mathematics/280 Transpose of a Matrix.mp4 39.9 MB
  • 28 Python - Sequences/167 Lists.mp4 39.6 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/262 Pros and Cons of K-Means Clustering.mp4 39.5 MB
  • 28 Python - Sequences/168 Using Methods.mp4 39.4 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.mp4 39.3 MB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.mp4 39.2 MB
  • 42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.mp4 39.0 MB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.mp4 38.9 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/021 Real Life Examples of Machine Learning (ML).mp4 38.6 MB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.mp4 38.4 MB
  • 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).mp4 38.2 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/402 Business Case A Comment on the Homework.mp4 38.1 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/253 Difference between Classification and Clustering.mp4 37.9 MB
  • 10 Probability - Combinatorics/031 Simple Operations with Factorials.mp4 37.9 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A2 No Endogeneity.mp4 37.4 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/107 Students T Distribution.mp4 37.2 MB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.mp4 36.6 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).mp4 36.6 MB
  • 11 Probability - Bayesian Inference/047 The Law of Total Probability.mp4 36.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.mp4 36.6 MB
  • 11 Probability - Bayesian Inference/045 Dependence and Independence of Sets.mp4 36.5 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.mp4 36.5 MB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.mp4 36.4 MB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.mp4 36.4 MB
  • 12 Probability - Distributions/056 Discrete Distributions The Bernoulli Distribution.mp4 35.8 MB
  • 10 Probability - Combinatorics/032 Solving Variations with Repetition.mp4 35.7 MB
  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).mp4 35.6 MB
  • 40 Part 6 Mathematics/274 Scalars and Vectors.mp4 35.5 MB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.mp4 35.2 MB
  • 40 Part 6 Mathematics/273 What is a Matrix.mp4 35.2 MB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/303 TensorFlow Outline and Comparison with Other Libraries.mp4 35.1 MB
  • 10 Probability - Combinatorics/036 Solving Combinations with Separate Sample Spaces.mp4 34.8 MB
  • 36 Advanced Statistical Methods - Logistic Regression/246 Calculating the Accuracy of the Model.mp4 34.5 MB
  • 46 Deep Learning - Overfitting/322 What is Validation.mp4 34.3 MB
  • 40 Part 6 Mathematics/278 Addition and Subtraction of Matrices.mp4 34.2 MB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.mp4 34.1 MB
  • 36 Advanced Statistical Methods - Logistic Regression/243 What do the Odds Actually Mean.mp4 33.8 MB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.mp4 33.8 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.mp4 33.8 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 33.6 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.mp4 33.0 MB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.mp4 32.7 MB
  • 41 Part 7 Deep Learning/284 What to Expect from this Part.mp4 32.6 MB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.mp4 32.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.mp4 32.4 MB
  • 28 Python - Sequences/169 List Slicing.mp4 32.3 MB
  • 22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.mp4 32.1 MB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.mp4 32.0 MB
  • 51 Deep Learning - Business Case Example/355 Business Case Balancing the Dataset.mp4 31.9 MB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.mp4 31.7 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.mp4 31.6 MB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.mp4 31.5 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/012 Real Life Examples of Traditional Data.mp4 31.4 MB
  • 29 Python - Iterations/177 How to Iterate over Dictionaries.mp4 31.1 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.mp4 31.1 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/016 Real Life Examples of Business Intelligence (BI).mp4 31.0 MB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/312 What is a Deep Net.mp4 31.0 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.mp4 31.0 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).mp4 30.9 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/441 Analyzing Several Straightforward Columns for this Exercise.mp4 30.9 MB
  • 28 Python - Sequences/170 Tuples.mp4 30.9 MB
  • 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.mp4 30.8 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.mp4 30.5 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.mp4 30.5 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 30.5 MB
  • 49 Deep Learning - Preprocessing/340 Binary and One-Hot Encoding.mp4 30.3 MB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).mp4 30.2 MB
  • 42 Deep Learning - Introduction to Neural Networks/286 Training the Model.mp4 30.1 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 A5 No Multicollinearity.mp4 30.1 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.mp4 30.1 MB
  • 42 Deep Learning - Introduction to Neural Networks/288 The Linear Model (Linear Algebraic Version).mp4 29.8 MB
  • 29 Python - Iterations/173 While Loops and Incrementing.mp4 29.8 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/192 What is the OLS.mp4 29.7 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.mp4 29.6 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/439 Extracting the Day of the Week from the Date Column.mp4 29.3 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/415 Introduction to Terms with Multiple Meanings.mp4 29.2 MB
  • 49 Deep Learning - Preprocessing/336 Preprocessing Introduction.mp4 29.1 MB
  • 29 Python - Iterations/175 Conditional Statements and Loops.mp4 29.1 MB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/314 Non-Linearities and their Purpose.mp4 29.0 MB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.mp4 28.9 MB
  • 15 Statistics - Descriptive Statistics/089 Covariance.mp4 28.8 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.mp4 28.6 MB
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  • 12 Probability - Distributions/062 Continuous Distributions The Students T Distribution.mp4 28.5 MB
  • 36 Advanced Statistical Methods - Logistic Regression/235 Introduction to Logistic Regression.mp4 28.4 MB
  • 11 Probability - Bayesian Inference/048 The Additive Rule.mp4 28.3 MB
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  • 18 Statistics - Inferential Statistics Confidence Intervals/115 Confidence intervals. Two means. Independent Samples (Part 2).mp4 28.1 MB
  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.mp4 28.0 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 27.6 MB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.mp4 27.2 MB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/316 Activation Functions Softmax Activation.mp4 27.2 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/385 MNIST Loss and Optimization Algorithm.mp4 27.1 MB
  • 15 Statistics - Descriptive Statistics/075 Numerical Variables - Frequency Distribution Table.mp4 27.1 MB
  • 29 Python - Iterations/174 Lists with the range() Function.mp4 27.0 MB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/400 Business Case Interpretation.mp4 27.0 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/434 Creating Checkpoints while Coding in Jupyter.mp4 26.9 MB
  • 60 Case Study - Loading the absenteeism_module/462 Deploying the absenteeism_module - Part I.mp4 26.7 MB
  • 11 Probability - Bayesian Inference/044 Mutually Exclusive Sets.mp4 26.6 MB
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  • 52 Deep Learning - Conclusion/369 An Overview of RNNs.mp4 26.5 MB
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  • 42 Deep Learning - Introduction to Neural Networks/289 The Linear Model with Multiple Inputs.mp4 26.3 MB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/206 Making Predictions with the Linear Regression.mp4 25.9 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).mp4 25.6 MB
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  • 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.mp4 25.3 MB
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  • 42 Deep Learning - Introduction to Neural Networks/291 Graphical Representation of Simple Neural Networks.mp4 23.7 MB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/382 MNIST How to Tackle the MNIST.mp4 23.7 MB
  • 40 Part 6 Mathematics/277 What is a Tensor.mp4 23.6 MB
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  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/335 Adam (Adaptive Moment Estimation).mp4 23.4 MB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.mp4 23.4 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/014 Real Life Examples of Big Data.mp4 23.1 MB
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  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/376 Types of File Formats supporting Tensors.mp4 21.3 MB
  • 58 Case Study - Preprocessing the Absenteeism_data/417 Using a Statistical Approach towards the Solution to the Exercise.mp4 21.2 MB
  • 52 Deep Learning - Conclusion/366 Whats Further out there in terms of Machine Learning.mp4 21.1 MB
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  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/311 What is a Layer.mp4 13.1 MB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.es.srt 10.7 kB
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  • 56 Software Integration/406 Taking a Closer Look at APIs.en.srt 10.6 kB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.pt.srt 10.6 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/427 Classifying the Various Reasons for Absence.pl.srt 10.6 kB
  • 13 Probability - Probability in Other Fields/067 Probability in Finance.pl.srt 10.5 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.it.srt 10.5 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.ro.srt 10.5 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.id.srt 10.5 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/104 Confidence Intervals Population Variance Known Z-score.es.srt 10.5 kB
  • 13 Probability - Probability in Other Fields/067 Probability in Finance.es.srt 10.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).fr.srt 10.5 kB
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  • 58 Case Study - Preprocessing the Absenteeism_data/422 Obtaining Dummies from a Single Feature.en.srt 10.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.ro.srt 10.4 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/388 MNIST Learning.en.srt 10.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.it.srt 10.4 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).de.srt 10.4 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.pl.srt 10.4 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.it.srt 10.3 kB
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  • 58 Case Study - Preprocessing the Absenteeism_data/427 Classifying the Various Reasons for Absence.en.srt 10.3 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/466 Analyzing Age vs Probability in Tableau.en.srt 10.3 kB
  • 12 Probability - Distributions/053 Types of Probability Distributions.es.srt 10.2 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.fr.srt 10.2 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.id.srt 10.2 kB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.pl.srt 10.1 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.id.srt 10.0 kB
  • 22 Part 4 Introduction to Python/140 Installing Python and Jupyter.fr.srt 10.0 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/104 Confidence Intervals Population Variance Known Z-score.en.srt 10.0 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/116 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 10.0 kB
  • 20 Statistics - Hypothesis Testing/129 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.pl.srt 10.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).ro.srt 10.0 kB
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  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.es.srt 10.0 kB
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  • 12 Probability - Distributions/066 Customers-Membership.xlsx 9.9 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.it.srt 9.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).id.srt 9.9 kB
  • 20 Statistics - Hypothesis Testing/131 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB
  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.fr.srt 9.8 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.es.srt 9.8 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.en.srt 9.8 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/437 Analyzing the Dates from the Initial Data Set.fr.srt 9.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.en.srt 9.8 kB
  • 27 Python - Python Functions/161 How to Create a Function with a Parameter.es.srt 9.8 kB
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  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.fr.srt 9.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).it.srt 9.8 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.en.srt 9.8 kB
  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.pl.srt 9.8 kB
  • 22 Part 4 Introduction to Python/140 Installing Python and Jupyter.de.srt 9.8 kB
  • 12 Probability - Distributions/066 Daily-Views.xlsx 9.8 kB
  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.fr.srt 9.8 kB
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  • 18 Statistics - Inferential Statistics Confidence Intervals/115 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB
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  • 05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.ro.srt 9.7 kB
  • 15 Statistics - Descriptive Statistics/084 2.8.Skewness-exercise.xlsx 9.7 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.pt.srt 9.7 kB
  • 09 Part 2 Probability/025 The Basic Probability Formula.pl.srt 9.7 kB
  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.de.srt 9.7 kB
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  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.es.srt 9.7 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/446 Creating the Targets for the Logistic Regression.fr.srt 9.7 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.pl.srt 9.6 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.ro.srt 9.6 kB
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  • 27 Python - Python Functions/161 How to Create a Function with a Parameter.pt.srt 9.6 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.fr.srt 9.6 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.fr.srt 9.6 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).pl.srt 9.6 kB
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  • 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.es.srt 9.5 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.en.srt 9.5 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.ro.srt 9.5 kB
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  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.ro.srt 9.5 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.de.srt 9.4 kB
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  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.fr.srt 9.4 kB
  • 27 Python - Python Functions/161 How to Create a Function with a Parameter.pl.srt 9.4 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.es.srt 9.4 kB
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  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.it.srt 9.4 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/116 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB
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  • 35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).fr.srt 9.4 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.de.srt 9.4 kB
  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.es.srt 9.4 kB
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  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.es.srt 9.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.fr.srt 9.3 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.en.srt 9.3 kB
  • 12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.fr.srt 9.3 kB
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  • 22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.fr.srt 9.3 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.id.srt 9.3 kB
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  • 22 Part 4 Introduction to Python/140 Installing Python and Jupyter.pt.srt 9.2 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.it.srt 9.2 kB
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  • 28 Python - Sequences/171 Dictionaries.es.srt 9.2 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.es.srt 9.2 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.fr.srt 9.2 kB
  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.en.srt 9.2 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Dealing with Categorical Data - Dummy Variables.fr.srt 9.2 kB
  • 12 Probability - Distributions/059 Characteristics of Continuous Distributions.pt.srt 9.2 kB
  • 27 Python - Python Functions/161 How to Create a Function with a Parameter.en.srt 9.2 kB
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  • 18 Statistics - Inferential Statistics Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.fr.srt 9.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.de.srt 9.2 kB
  • 12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.de.srt 9.2 kB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).de.srt 9.2 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.de.srt 9.2 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.pt.srt 9.2 kB
  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.id.srt 9.2 kB
  • 20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.de.srt 9.1 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.ro.srt 9.1 kB
  • 28 Python - Sequences/168 Using Methods.es.srt 9.1 kB
  • 12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.es.srt 9.1 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.pt.srt 9.1 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.it.srt 9.1 kB
  • 28 Python - Sequences/168 Using Methods.it.srt 9.1 kB
  • 09 Part 2 Probability/025 The Basic Probability Formula.en.srt 9.1 kB
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  • 21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.pl.srt 9.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/446 Creating the Targets for the Logistic Regression.it.srt 9.1 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.pl.srt 9.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.fr.srt 9.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.de.srt 9.1 kB
  • 12 Probability - Distributions/059 Characteristics of Continuous Distributions.id.srt 9.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Dealing with Categorical Data - Dummy Variables.de.srt 9.1 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.fr.srt 9.1 kB
  • 22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.de.srt 9.1 kB
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  • 58 Case Study - Preprocessing the Absenteeism_data/438 Extracting the Month Value from the Date Column.es.srt 8.7 kB
  • 56 Software Integration/405 What are Data Connectivity APIs and Endpoints.en.srt 8.7 kB
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  • 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.es.srt 8.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).de.srt 8.7 kB
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  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.en.srt 8.7 kB
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  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.es.srt 8.7 kB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.fr.srt 8.6 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.en.srt 8.6 kB
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  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.fr.srt 8.6 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).de.srt 8.5 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/418 Dropping a Column from a DataFrame in Python.pt.srt 8.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/250 Bank-data-testing.csv 8.5 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/257 Countries-exercise.csv 8.5 kB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).it.srt 8.4 kB
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  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.fr.srt 8.4 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).ro.srt 8.4 kB
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  • 11 Probability - Bayesian Inference/050 Bayes Law.de.srt 8.4 kB
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  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.en.srt 8.3 kB
  • 29 Python - Iterations/174 Lists with the range() Function.de.srt 8.3 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.pl.srt 8.3 kB
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  • 18 Statistics - Inferential Statistics Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.en.srt 8.2 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.fr.srt 8.2 kB
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).en.srt 8.2 kB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.es.srt 8.2 kB
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  • 60 Case Study - Loading the absenteeism_module/463 Deploying the absenteeism_module - Part II.pt.srt 8.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.pl.srt 8.2 kB
  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.ro.srt 8.2 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.en.srt 8.1 kB
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  • 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.fr.srt 8.1 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.en.srt 8.1 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.fr.srt 8.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.de.srt 8.1 kB
  • 29 Python - Iterations/177 How to Iterate over Dictionaries.en.srt 8.1 kB
  • 26 Python - Conditional Statements/156 The IF Statement.es.srt 8.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/454 Interpreting the Coefficients of the Logistic Regression.ro.srt 8.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.ro.srt 8.1 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.pt.srt 8.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.en.srt 8.1 kB
  • 29 Python - Iterations/175 Conditional Statements and Loops.es.srt 8.1 kB
  • 12 Probability - Distributions/052 Fundamentals of Probability Distributions.id.srt 8.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.pt.srt 8.1 kB
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  • 11 Probability - Bayesian Inference/050 Bayes Law.fr.srt 8.1 kB
  • 22 Part 4 Introduction to Python/138 Why Python.fr.srt 8.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/452 Interpreting the Coefficients for Our Problem.en.srt 8.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).id.srt 8.1 kB
  • 15 Statistics - Descriptive Statistics/085 Variance.it.srt 8.1 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.de.srt 8.1 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.fr.srt 8.1 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.es.srt 8.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.pt.srt 8.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.pt.srt 8.1 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.es.srt 8.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.es.srt 8.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.es.srt 8.0 kB
  • 15 Statistics - Descriptive Statistics/085 Variance.pt.srt 8.0 kB
  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.id.srt 8.0 kB
  • 15 Statistics - Descriptive Statistics/085 Variance.es.srt 8.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.en.srt 8.0 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.it.srt 8.0 kB
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  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.id.srt 8.0 kB
  • 51 Deep Learning - Business Case Example/361 Business Case Setting an Early Stopping Mechanism.en.srt 8.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.id.srt 8.0 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.id.srt 8.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.it.srt 8.0 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.id.srt 8.0 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.de.srt 8.0 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/418 Dropping a Column from a DataFrame in Python.en.srt 8.0 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/454 Interpreting the Coefficients of the Logistic Regression.es.srt 8.0 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.de.srt 8.0 kB
  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.pt.srt 8.0 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.pl.srt 8.0 kB
  • 29 Python - Iterations/175 Conditional Statements and Loops.it.srt 8.0 kB
  • 22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.en.srt 8.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.it.srt 8.0 kB
  • 26 Python - Conditional Statements/156 The IF Statement.it.srt 8.0 kB
  • 26 Python - Conditional Statements/156 The IF Statement.pt.srt 8.0 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).pl.srt 8.0 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.pt.srt 8.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.id.srt 8.0 kB
  • 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.de.srt 8.0 kB
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  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.pt.srt 7.9 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.ro.srt 7.9 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/397 Creating a Data Provider.en.srt 7.9 kB
  • 29 Python - Iterations/175 Conditional Statements and Loops.pl.srt 7.9 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.es.srt 7.9 kB
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  • 11 Probability - Bayesian Inference/050 Bayes Law.es.srt 7.9 kB
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  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.pl.srt 7.9 kB
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  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.es.srt 7.9 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.fr.srt 7.9 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.fr.srt 7.9 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.id.srt 7.9 kB
  • 11 Probability - Bayesian Inference/050 Bayes Law.pl.srt 7.9 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.de.srt 7.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).en.srt 7.9 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.it.srt 7.9 kB
  • 09 Part 2 Probability/028 Events and Their Complements.de.srt 7.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.fr.srt 7.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.it.srt 7.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.pl.srt 7.8 kB
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  • 22 Part 4 Introduction to Python/138 Why Python.es.srt 7.8 kB
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  • 11 Probability - Bayesian Inference/050 Bayes Law.id.srt 7.8 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.es.srt 7.8 kB
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  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).fr.srt 7.8 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.pl.srt 7.8 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).de.srt 7.7 kB
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  • 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.es.srt 7.7 kB
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  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.ro.srt 7.7 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.it.srt 7.7 kB
  • 12 Probability - Distributions/052 Fundamentals of Probability Distributions.en.srt 7.7 kB
  • 09 Part 2 Probability/026 Computing Expected Values.de.srt 7.7 kB
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  • 11 Probability - Bayesian Inference/050 Bayes Law.pt.srt 7.7 kB
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  • 15 Statistics - Descriptive Statistics/085 Variance.en.srt 7.7 kB
  • 22 Part 4 Introduction to Python/137 Introduction to Programming.de.srt 7.7 kB
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  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.pt.srt 7.7 kB
  • 09 Part 2 Probability/028 Events and Their Complements.fr.srt 7.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.en.srt 7.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).en.srt 7.7 kB
  • 42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.en.srt 7.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.id.srt 7.7 kB
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  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.it.srt 7.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.fr.srt 7.7 kB
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  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.de.srt 7.7 kB
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  • 09 Part 2 Probability/026 Computing Expected Values.fr.srt 7.6 kB
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  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).fr.srt 7.6 kB
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  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.id.srt 7.6 kB
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  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.ro.srt 7.6 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.de.srt 7.6 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.ro.srt 7.6 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.fr.srt 7.6 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.fr.srt 7.5 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.en.srt 7.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.fr.srt 7.5 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.de.srt 7.5 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.en.srt 7.5 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.en.srt 7.5 kB
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  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.de.srt 7.5 kB
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  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.ro.srt 7.5 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.fr.srt 7.5 kB
  • 56 Software Integration/408 Software Integration - Explained.de.srt 7.5 kB
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  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.de.srt 7.5 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.es.srt 7.5 kB
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  • 29 Python - Iterations/172 For Loops.de.srt 7.5 kB
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  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.fr.srt 7.5 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.fr.srt 7.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.it.srt 7.5 kB
  • 28 Python - Sequences/170 Tuples.it.srt 7.5 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.fr.srt 7.5 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.en.srt 7.5 kB
  • 22 Part 4 Introduction to Python/138 Why Python.id.srt 7.5 kB
  • 09 Part 2 Probability/027 Frequency.fr.srt 7.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.de.srt 7.5 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.es.srt 7.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.ro.srt 7.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.fr.srt 7.5 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.pl.srt 7.5 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.pl.srt 7.4 kB
  • 29 Python - Iterations/172 For Loops.fr.srt 7.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.en.srt 7.4 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.fr.srt 7.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).es.srt 7.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.fr.srt 7.4 kB
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  • 22 Part 4 Introduction to Python/137 Introduction to Programming.it.srt 7.4 kB
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  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.es.srt 7.4 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.pl.srt 7.4 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.es.srt 7.4 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.it.srt 7.4 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.it.srt 7.4 kB
  • 56 Software Integration/408 Software Integration - Explained.es.srt 7.4 kB
  • 09 Part 2 Probability/027 Frequency.de.srt 7.4 kB
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  • 09 Part 2 Probability/028 Events and Their Complements.id.srt 7.4 kB
  • 09 Part 2 Probability/026 Computing Expected Values.id.srt 7.4 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.en.srt 7.4 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.es.srt 7.4 kB
  • 11 Probability - Bayesian Inference/050 Bayes Law.en.srt 7.4 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).ro.srt 7.4 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.de.srt 7.4 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.ro.srt 7.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.es.srt 7.4 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.es.srt 7.4 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.fr.srt 7.4 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.it.srt 7.4 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.es.srt 7.4 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.id.srt 7.4 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.pt.srt 7.4 kB
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  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.pt.srt 7.3 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.es.srt 7.3 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).de.srt 7.3 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.es.srt 7.3 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.pt.srt 7.3 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.de.srt 7.3 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.it.srt 7.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.pt.srt 7.3 kB
  • 09 Part 2 Probability/026 Computing Expected Values.pl.srt 7.3 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.it.srt 7.3 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).it.srt 7.3 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.pl.srt 7.3 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.it.srt 7.3 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).es.srt 7.3 kB
  • 09 Part 2 Probability/028 Events and Their Complements.es.srt 7.3 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.fr.srt 7.3 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.id.srt 7.3 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.ro.srt 7.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.de.srt 7.3 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.de.srt 7.3 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.ro.srt 7.3 kB
  • 56 Software Integration/408 Software Integration - Explained.pl.srt 7.3 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.de.srt 7.3 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.es.srt 7.3 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.fr.srt 7.3 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.ro.srt 7.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.es.srt 7.3 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.es.srt 7.3 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).pt.srt 7.3 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.it.srt 7.3 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).it.srt 7.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.ro.srt 7.3 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.pt.srt 7.3 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.es.srt 7.3 kB
  • 56 Software Integration/408 Software Integration - Explained.id.srt 7.3 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.de.srt 7.3 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.fr.srt 7.3 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.pl.srt 7.3 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.it.srt 7.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.de.srt 7.2 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.pt.srt 7.2 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.ro.srt 7.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.pl.srt 7.2 kB
  • 56 Software Integration/408 Software Integration - Explained.it.srt 7.2 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.pt.srt 7.2 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.en.srt 7.2 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.de.srt 7.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.ro.srt 7.2 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.it.srt 7.2 kB
  • 56 Software Integration/408 Software Integration - Explained.pt.srt 7.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.en.srt 7.2 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.fr.srt 7.2 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.es.srt 7.2 kB
  • 09 Part 2 Probability/026 Computing Expected Values.es.srt 7.2 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.ro.srt 7.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).pt.srt 7.2 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.pl.srt 7.2 kB
  • 29 Python - Iterations/172 For Loops.es.srt 7.2 kB
  • 29 Python - Iterations/172 For Loops.it.srt 7.2 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.fr.srt 7.2 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.id.srt 7.2 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.id.srt 7.2 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.fr.srt 7.2 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.id.srt 7.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.es.srt 7.2 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).id.srt 7.2 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.id.srt 7.2 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.fr.srt 7.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.fr.srt 7.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.id.srt 7.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.pl.srt 7.2 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.pt.srt 7.2 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.de.srt 7.2 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.it.srt 7.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.it.srt 7.2 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.pt.srt 7.1 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.de.srt 7.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.id.srt 7.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.es.srt 7.1 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.it.srt 7.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.id.srt 7.1 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.es.srt 7.1 kB
  • 09 Part 2 Probability/028 Events and Their Complements.it.srt 7.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.pl.srt 7.1 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.en.srt 7.1 kB
  • 22 Part 4 Introduction to Python/138 Why Python.en.srt 7.1 kB
  • 09 Part 2 Probability/028 Events and Their Complements.pt.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.it.srt 7.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.pl.srt 7.1 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.es.srt 7.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.pl.srt 7.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.ro.srt 7.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.pl.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.pt.srt 7.1 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.id.srt 7.1 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.en.srt 7.1 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.it.srt 7.1 kB
  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.fr.srt 7.1 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).pl.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.pl.srt 7.1 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.es.srt 7.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.id.srt 7.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).id.srt 7.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.pl.srt 7.1 kB
  • 09 Part 2 Probability/026 Computing Expected Values.it.srt 7.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.de.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.id.srt 7.1 kB
  • 28 Python - Sequences/170 Tuples.en.srt 7.1 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.it.srt 7.1 kB
  • 09 Part 2 Probability/026 Computing Expected Values.pt.srt 7.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.pt.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.es.srt 7.1 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.fr.srt 7.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.es.srt 7.1 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.de.srt 7.1 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.pt.srt 7.1 kB
  • 22 Part 4 Introduction to Python/137 Introduction to Programming.en.srt 7.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.id.srt 7.1 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.pt.srt 7.1 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.pt.srt 7.1 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.it.srt 7.1 kB
  • 09 Part 2 Probability/027 Frequency.pl.srt 7.0 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.id.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.pt.srt 7.0 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.pt.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.id.srt 7.0 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.pl.srt 7.0 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.de.srt 7.0 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.pt.srt 7.0 kB
  • 29 Python - Iterations/172 For Loops.pt.srt 7.0 kB
  • 09 Part 2 Probability/027 Frequency.es.srt 7.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.es.srt 7.0 kB
  • 46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.en.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.pt.srt 7.0 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.it.srt 7.0 kB
  • 09 Part 2 Probability/027 Frequency.it.srt 7.0 kB
  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.de.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.it.srt 7.0 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.fr.srt 7.0 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.it.srt 7.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.ro.srt 7.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.pl.srt 7.0 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.fr.srt 7.0 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.de.srt 7.0 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.id.srt 7.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.de.srt 7.0 kB
  • 26 Python - Conditional Statements/157 The ELSE Statement.fr.srt 7.0 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.it.srt 7.0 kB
  • 09 Part 2 Probability/027 Frequency.id.srt 7.0 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.id.srt 7.0 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.pt.srt 7.0 kB
  • 26 Python - Conditional Statements/159 A Note on Boolean Values.fr.srt 7.0 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.de.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.es.srt 7.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.pt.srt 7.0 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.it.srt 7.0 kB
  • 29 Python - Iterations/172 For Loops.pl.srt 7.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.it.srt 7.0 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.fr.srt 7.0 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.id.srt 7.0 kB
  • 29 Python - Iterations/172 For Loops.id.srt 7.0 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.pl.srt 7.0 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.pl.srt 7.0 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.pl.srt 7.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).pl.srt 7.0 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.pt.srt 7.0 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.id.srt 7.0 kB
  • 26 Python - Conditional Statements/157 The ELSE Statement.de.srt 7.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.pt.srt 7.0 kB
  • 12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.id.srt 7.0 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.it.srt 7.0 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).en.srt 7.0 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.pl.srt 6.9 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.pt.srt 6.9 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.es.srt 6.9 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.es.srt 6.9 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.fr.srt 6.9 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.de.srt 6.9 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).fr.srt 6.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.id.srt 6.9 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.pl.srt 6.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.it.srt 6.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.de.srt 6.9 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.ro.srt 6.9 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.id.srt 6.9 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.es.srt 6.9 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.pt.srt 6.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.es.srt 6.9 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.it.srt 6.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.id.srt 6.9 kB
  • 52 Deep Learning - Conclusion/368 An overview of CNNs.id.srt 6.9 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.pl.srt 6.9 kB
  • 09 Part 2 Probability/028 Events and Their Complements.en.srt 6.9 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).es.srt 6.9 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.ro.srt 6.9 kB
  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.fr.srt 6.9 kB
  • 09 Part 2 Probability/027 Frequency.pt.srt 6.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.de.srt 6.9 kB
  • 56 Software Integration/408 Software Integration - Explained.en.srt 6.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.it.srt 6.9 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.es.srt 6.9 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.es.srt 6.9 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.de.srt 6.9 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.en.srt 6.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.en.srt 6.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.pl.srt 6.9 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.es.srt 6.9 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).de.srt 6.8 kB
  • 09 Part 2 Probability/026 Computing Expected Values.en.srt 6.8 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.en.srt 6.8 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.ro.srt 6.8 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.fr.srt 6.8 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.en.srt 6.8 kB
  • 26 Python - Conditional Statements/159 A Note on Boolean Values.de.srt 6.8 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.fr.srt 6.8 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.id.srt 6.8 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).en.srt 6.8 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.it.srt 6.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.en.srt 6.8 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.it.srt 6.8 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.id.srt 6.8 kB
  • 23 Python - Variables and Data Types/144 Variables.fr.srt 6.8 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.pl.srt 6.8 kB
  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.ro.srt 6.8 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.en.srt 6.8 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.it.srt 6.8 kB
  • 26 Python - Conditional Statements/157 The ELSE Statement.id.srt 6.8 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.id.srt 6.8 kB
  • 23 Python - Variables and Data Types/144 Variables.de.srt 6.8 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.ro.srt 6.8 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.pt.srt 6.8 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.id.srt 6.8 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.en.srt 6.8 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.pl.srt 6.8 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.pt.srt 6.8 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.it.srt 6.8 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.en.srt 6.8 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.de.srt 6.8 kB
  • 56 Software Integration/404 What are Data Servers Clients Requests and Responses.fr.srt 6.8 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.en.srt 6.8 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.pt.srt 6.8 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.en.srt 6.8 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.de.srt 6.7 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.it.srt 6.7 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.es.srt 6.7 kB
  • 26 Python - Conditional Statements/157 The ELSE Statement.it.srt 6.7 kB
  • 29 Python - Iterations/172 For Loops.en.srt 6.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.fr.srt 6.7 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.en.srt 6.7 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.pl.srt 6.7 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.it.srt 6.7 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.fr.srt 6.7 kB
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  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.id.srt 6.7 kB
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  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.en.srt 6.7 kB
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  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.pl.srt 6.6 kB
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  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.de.srt 6.6 kB
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  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.pl.srt 6.6 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.pl.srt 6.6 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.pt.srt 6.6 kB
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  • 52 Deep Learning - Conclusion/368 An overview of CNNs.en.srt 6.6 kB
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  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.pl.srt 6.6 kB
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  • 09 Part 2 Probability/027 Frequency.en.srt 6.6 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.fr.srt 6.6 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.pt.srt 6.6 kB
  • 23 Python - Variables and Data Types/144 Variables.pt.srt 6.6 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.id.srt 6.6 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.pl.srt 6.6 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.fr.srt 6.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.en.srt 6.5 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.it.srt 6.5 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.de.srt 6.5 kB
  • 26 Python - Conditional Statements/159 A Note on Boolean Values.id.srt 6.5 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.pl.srt 6.5 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.en.srt 6.5 kB
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  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.ro.srt 6.5 kB
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  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.es.srt 6.5 kB
  • 01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.en.srt 6.5 kB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.de.srt 6.5 kB
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  • 15 Statistics - Descriptive Statistics/071 Types of Data.pl.srt 6.5 kB
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  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.de.srt 6.5 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).it.srt 6.5 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.en.srt 6.5 kB
  • 14 Part 3 Statistics/070 Population and Sample.de.srt 6.5 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.fr.srt 6.5 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.it.srt 6.5 kB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.de.srt 6.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.de.srt 6.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.de.srt 6.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.fr.srt 6.5 kB
  • 23 Python - Variables and Data Types/144 Variables.pl.srt 6.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.fr.srt 6.5 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.pt.srt 6.5 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.ro.srt 6.5 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.pl.srt 6.5 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.de.srt 6.5 kB
  • 56 Software Integration/404 What are Data Servers Clients Requests and Responses.it.srt 6.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.en.srt 6.5 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.en.srt 6.5 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.ro.srt 6.4 kB
  • 26 Python - Conditional Statements/157 The ELSE Statement.en.srt 6.4 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.de.srt 6.4 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.ro.srt 6.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.es.srt 6.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.it.srt 6.4 kB
  • 11 Probability - Bayesian Inference/043 Union of Sets.de.srt 6.4 kB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.pl.srt 6.4 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.es.srt 6.4 kB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.fr.srt 6.4 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.es.srt 6.4 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.en.srt 6.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.en.srt 6.4 kB
  • 51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.en.srt 6.4 kB
  • 56 Software Integration/404 What are Data Servers Clients Requests and Responses.pl.srt 6.4 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.en.srt 6.4 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.pl.srt 6.4 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.fr.srt 6.4 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).pl.srt 6.4 kB
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  • 25 Python - Other Python Operators/155 Logical and Identity Operators.id.srt 6.4 kB
  • 27 Python - Python Functions/162 Defining a Function in Python - Part II.pl.srt 6.4 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.fr.srt 6.4 kB
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  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.es.srt 6.4 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.en.srt 6.4 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.fr.srt 6.4 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.en.srt 6.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.es.srt 6.4 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.pt.srt 6.4 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.de.srt 6.4 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.ro.srt 6.4 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.id.srt 6.4 kB
  • 26 Python - Conditional Statements/159 A Note on Boolean Values.en.srt 6.4 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.id.srt 6.4 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.es.srt 6.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/239 Example-bank-data.csv 6.4 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.de.srt 6.4 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.pt.srt 6.3 kB
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  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.id.srt 6.3 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.ro.srt 6.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.ro.srt 6.3 kB
  • 56 Software Integration/404 What are Data Servers Clients Requests and Responses.id.srt 6.3 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/303 TensorFlow Outline and Comparison with Other Libraries.fr.srt 6.3 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.it.srt 6.3 kB
  • 11 Probability - Bayesian Inference/043 Union of Sets.fr.srt 6.3 kB
  • 28 Python - Sequences/169 List Slicing.de.srt 6.3 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.it.srt 6.3 kB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.ro.srt 6.3 kB
  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.pl.srt 6.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/455 Backward Elimination or How to Simplify Your Model.fr.srt 6.3 kB
  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.pt.srt 6.3 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.pt.srt 6.3 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.it.srt 6.3 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.en.srt 6.3 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.es.srt 6.3 kB
  • 56 Software Integration/407 Communication between Software Products through Text Files.fr.srt 6.3 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.fr.srt 6.3 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.pt.srt 6.3 kB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.es.srt 6.3 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.pt.srt 6.3 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.es.srt 6.3 kB
  • 40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.en.srt 6.3 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.de.srt 6.3 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.id.srt 6.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.de.srt 6.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.fr.srt 6.3 kB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.es.srt 6.3 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.pl.srt 6.3 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.ro.srt 6.3 kB
  • 29 Python - Iterations/173 While Loops and Incrementing.pl.srt 6.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.de.srt 6.3 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.de.srt 6.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.ro.srt 6.3 kB
  • 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.fr.srt 6.3 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.ro.srt 6.3 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/402 Business Case A Comment on the Homework.fr.srt 6.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.it.srt 6.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.es.srt 6.3 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.es.srt 6.3 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.it.srt 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.it.srt 6.2 kB
  • 30 Python - Advanced Python Tools/178 Object Oriented Programming.en.srt 6.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.es.srt 6.2 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).id.srt 6.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.id.srt 6.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.ro.srt 6.2 kB
  • 25 Python - Other Python Operators/155 Logical and Identity Operators.pt.srt 6.2 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.it.srt 6.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.it.srt 6.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.de.srt 6.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.fr.srt 6.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.pt.srt 6.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.it.srt 6.2 kB
  • 14 Part 3 Statistics/070 Population and Sample.ro.srt 6.2 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.es.srt 6.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.id.srt 6.2 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).en.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.ro.srt 6.2 kB
  • 42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.id.srt 6.2 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.es.srt 6.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.it.srt 6.2 kB
  • 52 Deep Learning - Conclusion/365 Summary on What Youve Learned.fr.srt 6.2 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.ro.srt 6.2 kB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.it.srt 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.pl.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).fr.srt 6.2 kB
  • 23 Python - Variables and Data Types/144 Variables.en.srt 6.2 kB
  • 28 Python - Sequences/169 List Slicing.fr.srt 6.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.id.srt 6.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.pl.srt 6.2 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.pl.srt 6.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.id.srt 6.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/374 TensorFlow Intro.fr.srt 6.2 kB
  • 42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.fr.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.pt.srt 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.pt.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.pt.srt 6.2 kB
  • 57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.de.srt 6.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.pl.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.id.srt 6.2 kB
  • 40 Part 6 Mathematics/280 Transpose of a Matrix.fr.srt 6.2 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.en.srt 6.2 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.pt.srt 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.id.srt 6.2 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.it.srt 6.2 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.pl.srt 6.2 kB
  • 15 Statistics - Descriptive Statistics/081 Mean median and mode.pt.srt 6.1 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.pl.srt 6.1 kB
  • 57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.fr.srt 6.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.pt.srt 6.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.es.srt 6.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.id.srt 6.1 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.pl.srt 6.1 kB
  • 10 Probability - Combinatorics/034 Solving Combinations.de.srt 6.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.pt.srt 6.1 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.pt.srt 6.1 kB
  • 49 Deep Learning - Preprocessing/338 Standardization.en.srt 6.1 kB
  • 10 Probability - Combinatorics/034 Solving Combinations.fr.srt 6.1 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/106 Confidence Interval Clarifications.de.srt 6.1 kB
  • 11 Probability - Bayesian Inference/043 Union of Sets.es.srt 6.1 kB
  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).de.srt 6.1 kB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.ro.srt 6.1 kB
  • 46 Deep Learning - Overfitting/320 What is Overfitting.id.srt 6.1 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.it.srt 6.1 kB
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  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).en.srt 5.6 kB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.de.srt 5.6 kB
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  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.de.srt 5.6 kB
  • 52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.pt.srt 5.6 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).es.srt 5.6 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.it.srt 5.6 kB
  • 42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.pt.srt 5.6 kB
  • 56 Software Integration/407 Communication between Software Products through Text Files.en.srt 5.6 kB
  • 14 Part 3 Statistics/070 Population and Sample.en.srt 5.6 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.fr.srt 5.6 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/386 Calculating the Accuracy of the Model.id.srt 5.6 kB
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  • 42 Deep Learning - Introduction to Neural Networks/290 The Linear model with Multiple Inputs and Multiple Outputs.en.srt 5.6 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.ro.srt 5.6 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).pl.srt 5.6 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.ro.srt 5.6 kB
  • 57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.en.srt 5.6 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).pl.srt 5.6 kB
  • 12 Probability - Distributions/061 Continuous Distributions The Standard Normal Distribution.pl.srt 5.6 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.es.srt 5.6 kB
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  • 11 Probability - Bayesian Inference/040 Sets and Events.es.srt 5.6 kB
  • 52 Deep Learning - Conclusion/365 Summary on What Youve Learned.pl.srt 5.6 kB
  • 42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.pl.srt 5.6 kB
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  • 01 Part 1 Introduction/002 What Does the Course Cover.id.srt 5.6 kB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.de.srt 5.6 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.de.srt 5.6 kB
  • 42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.pl.srt 5.6 kB
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  • 15 Statistics - Descriptive Statistics/089 Covariance.de.srt 5.5 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/428 Using .concat() in Python.fr.srt 5.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.en.srt 5.5 kB
  • 01 Part 1 Introduction/002 What Does the Course Cover.pt.srt 5.5 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/315 Activation Functions.pl.srt 5.5 kB
  • 52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.it.srt 5.5 kB
  • 01 Part 1 Introduction/002 What Does the Course Cover.it.srt 5.5 kB
  • 11 Probability - Bayesian Inference/040 Sets and Events.pt.srt 5.5 kB
  • 18 Statistics - Inferential Statistics Confidence Intervals/106 Confidence Interval Clarifications.en.srt 5.5 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/386 Calculating the Accuracy of the Model.pl.srt 5.5 kB
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  • 20 Statistics - Hypothesis Testing/126 p-value.es.srt 5.5 kB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.fr.srt 5.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/243 What do the Odds Actually Mean.fr.srt 5.5 kB
  • 27 Python - Python Functions/160 Defining a Function in Python.pl.srt 5.5 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/315 Activation Functions.id.srt 5.5 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.id.srt 5.5 kB
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  • 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).pt.srt 5.5 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.es.srt 5.5 kB
  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.de.srt 5.5 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).id.srt 5.5 kB
  • 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.fr.srt 5.5 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.it.srt 5.5 kB
  • 40 Part 6 Mathematics/280 Transpose of a Matrix.en.srt 5.5 kB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.fr.srt 5.5 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.fr.srt 5.5 kB
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  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.pl.srt 5.5 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.fr.srt 5.5 kB
  • 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).it.srt 5.5 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.es.srt 5.5 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.de.srt 5.5 kB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.de.srt 5.5 kB
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  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.it.srt 5.4 kB
  • 20 Statistics - Hypothesis Testing/126 p-value.it.srt 5.4 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.pt.srt 5.4 kB
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  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.fr.srt 5.4 kB
  • 11 Probability - Bayesian Inference/040 Sets and Events.it.srt 5.4 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.pt.srt 5.4 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/402 Business Case A Comment on the Homework.en.srt 5.4 kB
  • 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.en.srt 5.4 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.pt.srt 5.4 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.ro.srt 5.4 kB
  • 51 Deep Learning - Business Case Example/358 Business Case Load the Preprocessed Data.de.srt 5.4 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.it.srt 5.4 kB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.es.srt 5.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.pl.srt 5.4 kB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.ro.srt 5.4 kB
  • 20 Statistics - Hypothesis Testing/126 p-value.pt.srt 5.4 kB
  • 49 Deep Learning - Preprocessing/340 Binary and One-Hot Encoding.fr.srt 5.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.ro.srt 5.4 kB
  • 12 Probability - Distributions/061 Continuous Distributions The Standard Normal Distribution.en.srt 5.4 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.es.srt 5.4 kB
  • 20 Statistics - Hypothesis Testing/126 p-value.id.srt 5.4 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.es.srt 5.4 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.fr.srt 5.4 kB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.it.srt 5.4 kB
  • 42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.en.srt 5.4 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/315 Activation Functions.en.srt 5.4 kB
  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.ro.srt 5.4 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.fr.srt 5.4 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.ro.srt 5.4 kB
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  • 60 Case Study - Loading the absenteeism_module/462 Deploying the absenteeism_module - Part I.fr.srt 5.4 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A2 No Endogeneity.en.srt 5.4 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/455 Backward Elimination or How to Simplify Your Model.en.srt 5.4 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/303 TensorFlow Outline and Comparison with Other Libraries.en.srt 5.4 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.es.srt 5.4 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.fr.srt 5.4 kB
  • 42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.en.srt 5.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.pl.srt 5.4 kB
  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.es.srt 5.4 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Basic NN Example (Part 1).fr.srt 5.3 kB
  • 51 Deep Learning - Business Case Example/358 Business Case Load the Preprocessed Data.es.srt 5.3 kB
  • 52 Deep Learning - Conclusion/365 Summary on What Youve Learned.en.srt 5.3 kB
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  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/374 TensorFlow Intro.en.srt 5.3 kB
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  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.it.srt 5.3 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/243 What do the Odds Actually Mean.ro.srt 5.3 kB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.it.srt 5.3 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.id.srt 5.3 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.de.srt 5.3 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.id.srt 5.3 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.ro.srt 5.3 kB
  • 11 Probability - Bayesian Inference/049 The Multiplication Law.de.srt 5.3 kB
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  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.id.srt 5.3 kB
  • 41 Part 7 Deep Learning/284 What to Expect from this Part.fr.srt 5.3 kB
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  • 15 Statistics - Descriptive Statistics/089 Covariance.it.srt 5.3 kB
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  • 22 Part 4 Introduction to Python/139 Why Jupyter.fr.srt 5.3 kB
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  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).fr.srt 5.2 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.it.srt 5.2 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/262 Pros and Cons of K-Means Clustering.de.srt 5.2 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.pt.srt 5.2 kB
  • 52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.en.srt 5.2 kB
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  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.pt.srt 5.2 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.id.srt 5.2 kB
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  • 51 Deep Learning - Business Case Example/358 Business Case Load the Preprocessed Data.id.srt 5.2 kB
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  • 30 Python - Advanced Python Tools/181 Importing Modules in Python.pl.srt 5.2 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.fr.srt 5.2 kB
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  • 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.en.srt 5.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.pt.srt 5.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.pl.srt 5.2 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.id.srt 5.2 kB
  • 46 Deep Learning - Overfitting/322 What is Validation.pl.srt 5.2 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.de.srt 5.2 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.pt.srt 5.2 kB
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  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).de.srt 5.2 kB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.ro.srt 5.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.id.srt 5.2 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.de.srt 5.2 kB
  • 01 Part 1 Introduction/002 What Does the Course Cover.en.srt 5.2 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.id.srt 5.2 kB
  • 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.de.srt 5.2 kB
  • 24 Python - Basic Python Syntax/153 Structuring with Indentation.fr.srt 5.2 kB
  • 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.es.srt 5.2 kB
  • 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.de.srt 5.2 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.en.srt 5.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/428 Using .concat() in Python.en.srt 5.2 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.de.srt 5.2 kB
  • 10 Probability - Combinatorics/033 Solving Variations without Repetition.fr.srt 5.2 kB
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  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).ro.srt 5.2 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.ro.srt 5.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.de.srt 5.2 kB
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  • 24 Python - Basic Python Syntax/153 Structuring with Indentation.de.srt 5.2 kB
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  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.pl.srt 5.2 kB
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  • 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.ro.srt 5.2 kB
  • 20 Statistics - Hypothesis Testing/126 p-value.en.srt 5.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/439 Extracting the Day of the Week from the Date Column.de.srt 5.2 kB
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).en.srt 5.1 kB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.es.srt 5.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/206 Making Predictions with the Linear Regression.fr.srt 5.1 kB
  • 41 Part 7 Deep Learning/284 What to Expect from this Part.de.srt 5.1 kB
  • 15 Statistics - Descriptive Statistics/072 Levels of Measurement.de.srt 5.1 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.id.srt 5.1 kB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 A5 No Multicollinearity.de.srt 5.1 kB
  • 60 Case Study - Loading the absenteeism_module/462 Deploying the absenteeism_module - Part I.es.srt 5.1 kB
  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.ro.srt 5.1 kB
  • 12 Probability - Distributions/065 Continuous Distributions The Logistic Distribution.en.srt 5.1 kB
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  • 22 Part 4 Introduction to Python/139 Why Jupyter.ro.srt 5.1 kB
  • 11 Probability - Bayesian Inference/041 Ways Sets Can Interact.de.srt 5.1 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.es.srt 5.1 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.pt.srt 5.1 kB
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.fr.srt 5.1 kB
  • 12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.id.srt 5.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 A5 No Multicollinearity.ro.srt 5.1 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/381 MNIST What is the MNIST Dataset.fr.srt 4.2 kB
  • 62 Bonus Lecture/471 Bonus Lecture Next Steps.html 4.2 kB
  • 44 Deep Learning - TensorFlow 2.0 Introduction/304 TensorFlow 1 vs TensorFlow 2.es.srt 4.2 kB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/341 MNIST The Dataset.fr.srt 4.2 kB
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  • 10 Probability - Combinatorics/030 Permutations and How to Use Them.en.srt 4.2 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.fr.srt 4.2 kB
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  • 45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/314 Non-Linearities and their Purpose.pl.srt 4.2 kB
  • 53 Appendix Deep Learning - TensorFlow 1 Introduction/376 Types of File Formats supporting Tensors.fr.srt 4.2 kB
  • 58 Case Study - Preprocessing the Absenteeism_data/434 Creating Checkpoints while Coding in Jupyter.fr.srt 4.2 kB
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  • 12 Probability - Distributions/056 Discrete Distributions The Bernoulli Distribution.pt.srt 4.1 kB
  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/382 MNIST How to Tackle the MNIST.fr.srt 4.1 kB
  • 46 Deep Learning - Overfitting/323 Training Validation and Test Datasets.de.srt 4.1 kB
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  • 52 Deep Learning - Conclusion/369 An Overview of RNNs.id.srt 4.1 kB
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  • 47 Deep Learning - Initialization/327 Types of Simple Initializations.ro.srt 4.1 kB
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  • 54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/382 MNIST How to Tackle the MNIST.de.srt 4.1 kB
  • 57 Case Study - Whats Next in the Course/410 The Business Task.ro.srt 4.1 kB
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  • 38 Advanced Statistical Methods - K-Means Clustering/259 Clustering Categorical Data - Exercise.html 966 Bytes
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/212 Simple Linear Regression with sklearn - Exercise.html 965 Bytes
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/230 Dummies and Variance Inflation Factor - Exercise.html 965 Bytes
  • 36 Advanced Statistical Methods - Logistic Regression/247 Calculating the Accuracy of the Model.html 965 Bytes
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/205 Dealing with Categorical Data - Dummy Variables.html 964 Bytes
  • 17 Statistics - Inferential Statistics Fundamentals/099 The Standard Normal Distribution Exercise.html 963 Bytes
  • 15 Statistics - Descriptive Statistics/080 Cross Tables and Scatter Plots Exercise.html 961 Bytes
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/223 Feature Scaling (Standardization) - Exercise.html 961 Bytes
  • 36 Advanced Statistical Methods - Logistic Regression/250 Testing the Model - Exercise.html 956 Bytes
  • 15 Statistics - Descriptive Statistics/092 Correlation Coefficient Exercise.html 954 Bytes
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/219 Multiple Linear Regression - Exercise.html 954 Bytes
  • 15 Statistics - Descriptive Statistics/074 Categorical Variables Exercise.html 952 Bytes
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/196 Multiple Linear Regression Exercise.html 952 Bytes
  • 15 Statistics - Descriptive Statistics/082 Mean Median and Mode Exercise.html 951 Bytes
  • 15 Statistics - Descriptive Statistics/076 Numerical Variables Exercise.html 950 Bytes
  • 15 Statistics - Descriptive Statistics/090 Covariance Exercise.html 941 Bytes
  • 15 Statistics - Descriptive Statistics/078 Histogram Exercise.html 940 Bytes
  • 15 Statistics - Descriptive Statistics/084 Skewness Exercise.html 939 Bytes
  • 51 Deep Learning - Business Case Example/external-assets-links.txt 934 Bytes
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 1.01.Simple-linear-regression.csv 922 Bytes
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/210 1.01.Simple-linear-regression.csv 922 Bytes
  • 55 Appendix Deep Learning - TensorFlow 1 Business Case/external-assets-links.txt 897 Bytes
  • 27 Python - Python Functions/external-assets-links.txt 890 Bytes
  • 50 Deep Learning - Classifying on the MNIST Dataset/external-assets-links.txt 841 Bytes
  • 59 Case Study - Applying Machine Learning to Create the absenteeism_module/external-assets-links.txt 778 Bytes
  • 35 Advanced Statistical Methods - Practical Example Linear Regression/external-assets-links.txt 772 Bytes
  • 24 Python - Basic Python Syntax/external-assets-links.txt 771 Bytes
  • 29 Python - Iterations/external-assets-links.txt 734 Bytes
  • 58 Case Study - Preprocessing the Absenteeism_data/external-assets-links.txt 704 Bytes
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/external-assets-links.txt 529 Bytes
  • 44 Deep Learning - TensorFlow 2.0 Introduction/external-assets-links.txt 519 Bytes
  • 28 Python - Sequences/external-assets-links.txt 511 Bytes
  • 26 Python - Conditional Statements/external-assets-links.txt 459 Bytes
  • 23 Python - Variables and Data Types/external-assets-links.txt 312 Bytes
  • 60 Case Study - Loading the absenteeism_module/external-assets-links.txt 277 Bytes
  • 25 Python - Other Python Operators/external-assets-links.txt 230 Bytes
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/external-assets-links.txt 221 Bytes
  • 01 Part 1 Introduction/external-assets-links.txt 102 Bytes
  • 39 Advanced Statistical Methods - Other Types of Clustering/external-assets-links.txt 88 Bytes
  • 00 Websites you may like/[FreeAllCourse.Com].url 52 Bytes
  • [FreeAllCourse.Com].url 52 Bytes

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