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[FreeCourseSite.com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
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[FreeCourseSite.com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
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2022-03-24
最近下载：
2024-08-06
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文件列表
11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4
168.2 MB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4
151.4 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4
145.0 MB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4
133.0 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4
131.2 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4
129.5 MB
46. Software Integration/5. Taking a Closer Look at APIs.mp4
121.2 MB
15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4
118.1 MB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4
114.3 MB
46. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4
109.1 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4
108.6 MB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4
108.4 MB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4
107.6 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4
104.1 MB
15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4
96.5 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4
94.3 MB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4
91.9 MB
29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4
90.7 MB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4
85.4 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4
85.1 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4
85.0 MB
13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4
82.0 MB
44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4
80.1 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4
79.2 MB
17. Part 3 Introduction to Python/3. Why Python.mp4
78.7 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4
78.2 MB
31. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4
78.1 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4
76.4 MB
46. Software Integration/9. Software Integration - Explained.mp4
76.2 MB
10. Statistics - Descriptive Statistics/1. Types of Data.mp4
76.0 MB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4
75.0 MB
13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4
73.9 MB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4
72.9 MB
46. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4
72.4 MB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4
71.0 MB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4
67.6 MB
12. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4
65.9 MB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4
65.8 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4
65.3 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4
64.9 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4
64.8 MB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4
64.6 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4
64.1 MB
46. Software Integration/7. Communication between Software Products through Text Files.mp4
63.3 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4
62.2 MB
51. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4
62.2 MB
13. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4
62.0 MB
45. Deep Learning - Conclusion/3. An overview of CNNs.mp4
61.7 MB
17. Part 3 Introduction to Python/1. Introduction to Programming.mp4
61.4 MB
9. Part 2 Statistics/1. Population and Sample.mp4
60.9 MB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4
60.2 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4
60.1 MB
13. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4
59.8 MB
51. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4
59.3 MB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4
59.1 MB
31. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4
58.8 MB
15. Statistics - Hypothesis Testing/10. p-value.mp4
58.6 MB
28. Advanced Statistical Methods - Multiple Linear Regression/18. Dealing with Categorical Data - Dummy Variables.mp4
58.4 MB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4
58.3 MB
28. Advanced Statistical Methods - Multiple Linear Regression/3. Adjusted R-Squared.mp4
57.5 MB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4
57.1 MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4
57.0 MB
10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4
57.0 MB
50. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4
56.9 MB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4
56.9 MB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4
56.2 MB
30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4
56.0 MB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4
55.7 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4
55.3 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4
54.9 MB
47. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4
54.8 MB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4
54.3 MB
42. Deep Learning - Preprocessing/3. Standardization.mp4
53.5 MB
10. Statistics - Descriptive Statistics/22. Variance.mp4
53.4 MB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4
52.8 MB
13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4
52.4 MB
12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4
52.3 MB
33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp4
52.2 MB
27. Advanced Statistical Methods - Linear regression/13. Decomposition of Variability.mp4
52.1 MB
33. Part 5 Mathematics/15. Dot Product of Matrices.mp4
51.8 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4
51.4 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4
51.4 MB
12. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4
50.1 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4
50.1 MB
37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4
50.0 MB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4
49.0 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4
48.0 MB
10. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4
47.3 MB
35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4
47.3 MB
45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4
46.9 MB
27. Advanced Statistical Methods - Linear regression/11. How to Interpret the Regression Table.mp4
46.8 MB
32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4
46.7 MB
27. Advanced Statistical Methods - Linear regression/8. First Regression in Python.mp4
46.7 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4
46.6 MB
17. Part 3 Introduction to Python/5. Why Jupyter.mp4
46.5 MB
31. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4
46.3 MB
15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4
46.1 MB
43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4
46.0 MB
31. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4
45.1 MB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4
45.0 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4
44.9 MB
28. Advanced Statistical Methods - Multiple Linear Regression/13. A3 Normality and Homoscedasticity.mp4
44.8 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4
43.6 MB
44. Deep Learning - Business Case Example/8. Business Case Optimization.mp4
43.5 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4
43.2 MB
27. Advanced Statistical Methods - Linear regression/17. R-Squared.mp4
43.0 MB
47. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4
42.8 MB
51. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4
42.6 MB
27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.mp4
42.6 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4
42.5 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4
42.4 MB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4
42.2 MB
10. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4
41.7 MB
45. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4
41.7 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4
41.5 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4
41.5 MB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4
41.3 MB
44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4
41.3 MB
47. Case Study - What's Next in the Course/2. The Business Task.mp4
41.1 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4
40.8 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4
40.6 MB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4
40.4 MB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4
40.3 MB
29. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4
40.3 MB
35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4
40.2 MB
33. Part 5 Mathematics/13. Transpose of a Matrix.mp4
39.9 MB
31. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4
39.5 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4
39.3 MB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4
39.2 MB
35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4
39.0 MB
10. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4
38.9 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4
38.6 MB
15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4
38.2 MB
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4
38.1 MB
30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4
37.9 MB
28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.mp4
37.4 MB
13. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4
37.2 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4
36.6 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4
36.6 MB
29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4
36.4 MB
33. Part 5 Mathematics/3. Scalars and Vectors.mp4
35.5 MB
25. Python - Advanced Python Tools/1. Object Oriented Programming.mp4
35.2 MB
33. Part 5 Mathematics/1. What is a matrix.mp4
35.2 MB
21. Python - Conditional Statements/4. The ELIF Statement.mp4
34.8 MB
29. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4
34.5 MB
39. Deep Learning - Overfitting/3. What is Validation.mp4
34.3 MB
33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp4
34.2 MB
37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4
34.1 MB
29. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4
33.8 MB
29. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4
33.8 MB
13. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4
33.8 MB
28. Advanced Statistical Methods - Multiple Linear Regression/14. A4 No Autocorrelation.mp4
33.0 MB
34. Part 6 Deep Learning/1. What to Expect from this Part.mp4
32.6 MB
39. Deep Learning - Overfitting/1. What is Overfitting.mp4
32.6 MB
23. Python - Sequences/5. List Slicing.mp4
32.3 MB
18. Python - Variables and Data Types/5. Python Strings.mp4
32.3 MB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4
32.1 MB
29. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4
32.0 MB
31. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4
31.6 MB
20. Python - Other Python Operators/3. Logical and Identity Operators.mp4
31.5 MB
15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4
31.4 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4
31.4 MB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4
31.1 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4
31.0 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4
31.0 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4
30.9 MB
10. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4
30.8 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4
30.5 MB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4
30.5 MB
42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4
30.3 MB
13. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4
30.2 MB
28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.mp4
30.1 MB
35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4
30.1 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4
30.1 MB
35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4
29.8 MB
27. Advanced Statistical Methods - Linear regression/15. What is the OLS.mp4
29.7 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4
29.3 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4
29.2 MB
42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4
29.1 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4
29.0 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4
28.9 MB
10. Statistics - Descriptive Statistics/27. Covariance.mp4
28.8 MB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4
28.6 MB
29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4
28.4 MB
13. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4
28.1 MB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4
28.0 MB
18. Python - Variables and Data Types/1. Variables.mp4
27.9 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4
27.6 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4
27.2 MB
43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4
27.1 MB
10. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4
27.1 MB
44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4
27.0 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4
26.9 MB
50. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4
26.7 MB
45. Deep Learning - Conclusion/5. An Overview of RNNs.mp4
26.5 MB
39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4
26.4 MB
35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4
26.3 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4
26.3 MB
39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4
26.3 MB
23. Python - Sequences/7. Dictionaries.mp4
26.3 MB
28. Advanced Statistical Methods - Multiple Linear Regression/20. Making Predictions with the Linear Regression.mp4
25.9 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4
25.6 MB
39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4
25.4 MB
33. Part 5 Mathematics/14. Dot Product.mp4
25.2 MB
22. Python - Python Functions/2. How to Create a Function with a Parameter.mp4
25.0 MB
35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4
24.4 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4
24.3 MB
29. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4
24.2 MB
12. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4
23.9 MB
35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4
23.8 MB
43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4
23.7 MB
33. Part 5 Mathematics/8. What is a Tensor.mp4
23.6 MB
12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4
23.6 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4
23.4 MB
29. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4
23.4 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4
23.1 MB
22. Python - Python Functions/7. Built-in Functions in Python.mp4
23.1 MB
23. Python - Sequences/1. Lists.mp4
23.1 MB
23. Python - Sequences/3. Using Methods.mp4
23.0 MB
28. Advanced Statistical Methods - Multiple Linear Regression/7. OLS Assumptions.mp4
22.9 MB
40. Deep Learning - Initialization/1. What is Initialization.mp4
22.8 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4
22.7 MB
28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp4
22.6 MB
31. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4
22.3 MB
39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4
21.7 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4
21.6 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp4
21.6 MB
37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4
21.3 MB
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10. Statistics - Descriptive Statistics/21.2 2.8. Skewness_exercise.xlsx.xlsx
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9.7 kB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.vtt
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15. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx
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5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.vtt
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5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.vtt
9.5 kB
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9.4 kB
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9.4 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.vtt
9.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.vtt
9.1 kB
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9.0 kB
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9.0 kB
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8.9 kB
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8.6 kB
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8.5 kB
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31. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).vtt
8.1 kB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.vtt
8.1 kB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt
8.1 kB
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7.9 kB
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7.9 kB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.vtt
7.7 kB
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7.7 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.vtt
7.6 kB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.vtt
7.6 kB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.vtt
7.6 kB
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7.5 kB
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7.3 kB
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7.3 kB
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7.3 kB
13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.vtt
7.3 kB
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7.1 kB
27. Advanced Statistical Methods - Linear regression/8. First Regression in Python.vtt
7.1 kB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.vtt
7.0 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.vtt
7.0 kB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.vtt
7.0 kB
44. Deep Learning - Business Case Example/6. Creating a Data Provider.vtt
7.0 kB
10. Statistics - Descriptive Statistics/22. Variance.vtt
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6.8 kB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.vtt
6.8 kB
28. Advanced Statistical Methods - Multiple Linear Regression/3. Adjusted R-Squared.vtt
6.7 kB
31. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).vtt
6.7 kB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt
6.6 kB
18. Python - Variables and Data Types/5. Python Strings.vtt
6.6 kB
31. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.vtt
6.6 kB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.vtt
6.6 kB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.vtt
6.6 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.vtt
6.6 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.vtt
6.5 kB
51. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.vtt
6.5 kB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.vtt
6.4 kB
29. Advanced Statistical Methods - Logistic Regression/5.2 Example_bank_data.csv.csv
6.4 kB
15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.vtt
6.3 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.vtt
6.3 kB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.vtt
6.3 kB
17. Part 3 Introduction to Python/3. Why Python.vtt
6.3 kB
17. Part 3 Introduction to Python/1. Introduction to Programming.vtt
6.2 kB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.vtt
6.2 kB
39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.vtt
6.2 kB
46. Software Integration/9. Software Integration - Explained.vtt
6.1 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).vtt
6.0 kB
10. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.vtt
6.0 kB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.vtt
6.0 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.vtt
6.0 kB
28. Advanced Statistical Methods - Multiple Linear Regression/13. A3 Normality and Homoscedasticity.vtt
6.0 kB
27. Advanced Statistical Methods - Linear regression/17. R-Squared.vtt
5.9 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.vtt
5.9 kB
44. Deep Learning - Business Case Example/8. Business Case Optimization.vtt
5.9 kB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.vtt
5.9 kB
10. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.vtt
5.9 kB
21. Python - Conditional Statements/4. The ELIF Statement.vtt
5.9 kB
29. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.vtt
5.8 kB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.vtt
5.8 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.vtt
5.8 kB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.vtt
5.8 kB
45. Deep Learning - Conclusion/3. An overview of CNNs.vtt
5.8 kB
31. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.vtt
5.8 kB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.vtt
5.8 kB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.vtt
5.7 kB
27. Advanced Statistical Methods - Linear regression/11. How to Interpret the Regression Table.vtt
5.6 kB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.vtt
5.6 kB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.vtt
5.6 kB
13. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.vtt
5.5 kB
18. Python - Variables and Data Types/1. Variables.vtt
5.5 kB
25. Python - Advanced Python Tools/1. Object Oriented Programming.vtt
5.5 kB
13. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).vtt
5.5 kB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.vtt
5.4 kB
42. Deep Learning - Preprocessing/3. Standardization.vtt
5.4 kB
10. Statistics - Descriptive Statistics/1. Types of Data.vtt
5.4 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt
5.3 kB
46. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.vtt
5.3 kB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.vtt
5.3 kB
31. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.vtt
5.3 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.vtt
5.2 kB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.vtt
5.2 kB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.vtt
5.2 kB
29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.vtt
5.2 kB
13. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.vtt
5.1 kB
10. Statistics - Descriptive Statistics/17. Mean, median and mode.vtt
5.1 kB
20. Python - Other Python Operators/3. Logical and Identity Operators.vtt
5.1 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt
5.1 kB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.vtt
5.1 kB
12. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.vtt
5.1 kB
39. Deep Learning - Overfitting/1. What is Overfitting.vtt
5.1 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.vtt
5.0 kB
15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.vtt
5.0 kB
27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.vtt
5.0 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.vtt
5.0 kB
29. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.vtt
5.0 kB
23. Python - Sequences/5. List Slicing.vtt
4.9 kB
13. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.vtt
4.9 kB
9. Part 2 Statistics/1. Population and Sample.vtt
4.9 kB
46. Software Integration/7. Communication between Software Products through Text Files.vtt
4.9 kB
47. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt
4.9 kB
15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).vtt
4.9 kB
35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.vtt
4.9 kB
29. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.vtt
4.9 kB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.vtt
4.8 kB
33. Part 5 Mathematics/13. Transpose of a Matrix.vtt
4.8 kB
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.vtt
4.8 kB
35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.vtt
4.7 kB
45. Deep Learning - Conclusion/1. Summary on What You've Learned.vtt
4.7 kB
37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.vtt
4.7 kB
28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.vtt
4.7 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.vtt
4.7 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.vtt
4.7 kB
35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.vtt
4.7 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt
4.7 kB
45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.vtt
4.7 kB
15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).vtt
4.7 kB
43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.vtt
4.6 kB
1. Part 1 Introduction/2. What Does the Course Cover.vtt
4.6 kB
15. Statistics - Hypothesis Testing/10. p-value.vtt
4.6 kB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.vtt
4.6 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.vtt
4.5 kB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.vtt
4.5 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt
4.5 kB
29. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.vtt
4.5 kB
12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.vtt
4.4 kB
10. Statistics - Descriptive Statistics/27. Covariance.vtt
4.4 kB
23. Python - Sequences/1. Lists.vtt
4.4 kB
28. Advanced Statistical Methods - Multiple Linear Regression/14. A4 No Autocorrelation.vtt
4.4 kB
29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.vtt
4.4 kB
39. Deep Learning - Overfitting/3. What is Validation.vtt
4.4 kB
37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.vtt
4.3 kB
30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.vtt
4.3 kB
50. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.vtt
4.3 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.vtt
4.3 kB
29. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.vtt
4.3 kB
42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.vtt
4.3 kB
25. Python - Advanced Python Tools/7. Importing Modules in Python.vtt
4.3 kB
10. Statistics - Descriptive Statistics/30. Correlation Coefficient.vtt
4.2 kB
32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.vtt
4.2 kB
17. Part 3 Introduction to Python/5. Why Jupyter.vtt
4.2 kB
34. Part 6 Deep Learning/1. What to Expect from this Part.vtt
4.1 kB
28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.vtt
4.1 kB
10. Statistics - Descriptive Statistics/3. Levels of Measurement.vtt
4.1 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.vtt
4.1 kB
31. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.vtt
4.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).vtt
4.1 kB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.vtt
4.0 kB
44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.vtt
4.0 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.vtt
4.0 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).vtt
4.0 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.vtt
4.0 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.vtt
4.0 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).vtt
4.0 kB
28. Advanced Statistical Methods - Multiple Linear Regression/20. Making Predictions with the Linear Regression.vtt
4.0 kB
10. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.vtt
3.9 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.vtt
3.9 kB
33. Part 5 Mathematics/1. What is a matrix.vtt
3.9 kB
35. Deep Learning - Introduction to Neural Networks/3. Training the Model.vtt
3.9 kB
22. Python - Python Functions/2. How to Create a Function with a Parameter.vtt
3.9 kB
33. Part 5 Mathematics/14. Dot Product.vtt
3.8 kB
13. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.vtt
3.8 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.vtt
3.8 kB
22. Python - Python Functions/7. Built-in Functions in Python.vtt
3.8 kB
39. Deep Learning - Overfitting/5. N-Fold Cross Validation.vtt
3.8 kB
27. Advanced Statistical Methods - Linear regression/13. Decomposition of Variability.vtt
3.8 kB
47. Case Study - What's Next in the Course/3. Introducing the Data Set.vtt
3.7 kB
23. Python - Sequences/7. Dictionaries.vtt
3.7 kB
31. Advanced Statistical Methods - K-Means Clustering/15.1 iris_with_answers.csv.csv
3.7 kB
29. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.vtt
3.7 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.vtt
3.7 kB
19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.vtt
3.7 kB
33. Part 5 Mathematics/5. Linear Algebra and Geometry.vtt
3.6 kB
30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.vtt
3.6 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.vtt
3.6 kB
33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.vtt
3.6 kB
23. Python - Sequences/3. Using Methods.vtt
3.6 kB
12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.vtt
3.5 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.vtt
3.5 kB
35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).vtt
3.5 kB
42. Deep Learning - Preprocessing/1. Preprocessing Introduction.vtt
3.5 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.vtt
3.5 kB
24. Python - Iterations/8. How to Iterate over Dictionaries.vtt
3.4 kB
27. Advanced Statistical Methods - Linear regression/15. What is the OLS.vtt
3.4 kB
45. Deep Learning - Conclusion/5. An Overview of RNNs.vtt
3.4 kB
47. Case Study - What's Next in the Course/2. The Business Task.vtt
3.4 kB
33. Part 5 Mathematics/3. Scalars and Vectors.vtt
3.4 kB
12. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.vtt
3.4 kB
17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt
3.3 kB
40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt
3.3 kB
40. Deep Learning - Initialization/2. Types of Simple Initializations.vtt
3.3 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.vtt
3.3 kB
10. Statistics - Descriptive Statistics/19. Skewness.vtt
3.3 kB
18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.vtt
3.2 kB
33. Part 5 Mathematics/8. What is a Tensor.vtt
3.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.vtt
3.2 kB
25. Python - Advanced Python Tools/5. What is the Standard Library.vtt
3.2 kB
24. Python - Iterations/6. Conditional Statements and Loops.vtt
3.2 kB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.vtt
3.2 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.vtt
3.2 kB
21. Python - Conditional Statements/1. The IF Statement.vtt
3.2 kB
39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.vtt
3.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.vtt
3.2 kB
40. Deep Learning - Initialization/1. What is Initialization.vtt
3.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.vtt
3.1 kB
22. Python - Python Functions/5. Conditional Statements and Functions.vtt
3.1 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.vtt
3.1 kB
37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.vtt
3.1 kB
23. Python - Sequences/6. Tuples.vtt
3.0 kB
28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.vtt
3.0 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).vtt
3.0 kB
29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.vtt
3.0 kB
30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.vtt
3.0 kB
13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.vtt
2.9 kB
37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.vtt
2.9 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/5. What's Regression Analysis - a Quick Refresher.html
2.9 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.vtt
2.9 kB
31. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.vtt
2.9 kB
29. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.vtt
2.8 kB
35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.vtt
2.8 kB
22. Python - Python Functions/3. Defining a Function in Python - Part II.vtt
2.8 kB
28. Advanced Statistical Methods - Multiple Linear Regression/7. OLS Assumptions.vtt
2.7 kB
10. Statistics - Descriptive Statistics/11. The Histogram.vtt
2.7 kB
44. Deep Learning - Business Case Example/9. Business Case Interpretation.vtt
2.7 kB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).vtt
2.6 kB
43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.vtt
2.6 kB
21. Python - Conditional Statements/5. A Note on Boolean Values.vtt
2.6 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.vtt
2.6 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/1. What to Expect from the Following Sections.html
2.5 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.vtt
2.5 kB
21. Python - Conditional Statements/3. The ELSE Statement.vtt
2.5 kB
24. Python - Iterations/4. Lists with the range() Function.vtt
2.5 kB
24. Python - Iterations/1. For Loops.vtt
2.5 kB
35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.vtt
2.5 kB
42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.vtt
2.5 kB
24. Python - Iterations/3. While Loops and Incrementing.vtt
2.5 kB
31. Advanced Statistical Methods - K-Means Clustering/14.2 iris_dataset.csv.csv
2.5 kB
31. Advanced Statistical Methods - K-Means Clustering/15.2 iris_dataset.csv.csv
2.5 kB
44. Deep Learning - Business Case Example/10. Business Case Testing the Model.vtt
2.4 kB
35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.vtt
2.4 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/14. Dropping a Dummy Variable from the Data Set.html
2.4 kB
37. Deep Learning - TensorFlow Introduction/2. A Note on Installing Packages in Anaconda.html
2.4 kB
39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.vtt
2.4 kB
15. Statistics - Hypothesis Testing/2. Further Reading on Null and Alternative Hypothesis.html
2.3 kB
33. Part 5 Mathematics/12. Errors when Adding Matrices.vtt
2.3 kB
45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.vtt
2.3 kB
28. Advanced Statistical Methods - Multiple Linear Regression/6. Test for Significance of the Model (F-Test).vtt
2.3 kB
22. Python - Python Functions/1. Defining a Function in Python.vtt
2.3 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.vtt
2.2 kB
44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.vtt
2.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/11. MNIST Solutions.html
2.2 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/14. ARTICLE - A Note on 'pickling'.html
2.2 kB
20. Python - Other Python Operators/1. Comparison Operators.vtt
2.2 kB
43. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Exercises.html
2.2 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.vtt
2.2 kB
24. Python - Iterations/7. Conditional Statements, Functions, and Loops.vtt
2.1 kB
28. Advanced Statistical Methods - Multiple Linear Regression/9. A1 Linearity.vtt
2.1 kB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.vtt
2.0 kB
19. Python - Basic Python Syntax/12. Structuring with Indentation.vtt
2.0 kB
26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.vtt
2.0 kB
37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.vtt
2.0 kB
31. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.vtt
2.0 kB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.vtt
1.9 kB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).vtt
1.9 kB
43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.vtt
1.9 kB
35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.vtt
1.9 kB
27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.vtt
1.9 kB
22. Python - Python Functions/4. How to Use a Function within a Function.vtt
1.8 kB
12. Statistics - Inferential Statistics Fundamentals/11. Standard error.vtt
1.8 kB
13. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).vtt
1.8 kB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.vtt
1.7 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.vtt
1.6 kB
19. Python - Basic Python Syntax/3. The Double Equality Sign.vtt
1.6 kB
37. Deep Learning - TensorFlow Introduction/9. Basic NN Example with TF Exercises.html
1.6 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.vtt
1.5 kB
19. Python - Basic Python Syntax/7. Add Comments.vtt
1.5 kB
19. Python - Basic Python Syntax/10. Indexing Elements.vtt
1.5 kB
42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.vtt
1.5 kB
27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.vtt
1.5 kB
12. Statistics - Inferential Statistics Fundamentals/1. Introduction.vtt
1.5 kB
29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.vtt
1.5 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5. Basic NN Example Exercises.html
1.4 kB
27. Advanced Statistical Methods - Linear regression/10. Using Seaborn for Graphs.vtt
1.3 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/29. EXERCISE - Removing the Date Column.html
1.2 kB
19. Python - Basic Python Syntax/5. How to Reassign Values.vtt
1.2 kB
25. Python - Advanced Python Tools/3. Modules and Packages.vtt
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45. Deep Learning - Conclusion/7. Download All Resources.html
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49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11.1 Logistic Regression prior to Backward Elimination.html
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17. Part 3 Introduction to Python/10. Jupyter's Interface.html
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17. Part 3 Introduction to Python/2. Introduction to Programming.html
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25. Python - Advanced Python Tools/6. What is the Standard Library.html
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25. Python - Advanced Python Tools/8. Importing Modules in Python.html
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26. Part 4 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html
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27. Advanced Statistical Methods - Linear regression/14. Decomposition of Variability.html
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47. Case Study - What's Next in the Course/4. Introducing the Data Set.html
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5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html
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31. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html
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31. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data - Exercise.html
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31. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html
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10. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html
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10. Statistics - Descriptive Statistics/13. Histogram Exercise.html
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10. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html
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10. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html
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10. Statistics - Descriptive Statistics/21. Skewness Exercise.html
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10. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html
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10. Statistics - Descriptive Statistics/29. Covariance Exercise.html
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10. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html
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10. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html
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11. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html
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12. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html
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13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html
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13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html
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13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html
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13. Statistics - Inferential Statistics Confidence Intervals/4. Confidence Intervals; Population Variance Known; z-score; Exercise.html
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13. Statistics - Inferential Statistics Confidence Intervals/9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html
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14. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html
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15. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html
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15. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html
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15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 1). Exercise.html
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15. Statistics - Hypothesis Testing/20. Test for the mean. Independent samples (Part 2) Exercise.html
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15. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html
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16. Statistics - Practical Example Hypothesis Testing/2. Practical Example Hypothesis Testing Exercise.html
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27. Advanced Statistical Methods - Linear regression/9. First Regression in Python Exercise.html
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28. Advanced Statistical Methods - Multiple Linear Regression/19. Dealing with Categorical Data - Dummy Variables.html
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28. Advanced Statistical Methods - Multiple Linear Regression/5. Multiple Linear Regression Exercise.html
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