搜索
[FreeCourseLab.com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
磁力链接/BT种子名称
[FreeCourseLab.com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
磁力链接/BT种子简介
种子哈希：
f1f2d40f9a83ba4fb22e56ffdf7ecd687454d922
文件大小：
11.49G
已经下载：
229
次
下载速度：
极快
收录时间：
2021-04-10
最近下载：
2025-05-27
移花宫入口
移花宫.com
邀月.com
怜星.com
花无缺.com
yhgbt.icu
yhgbt.top
磁力链接下载
magnet:?xt=urn:btih:F1F2D40F9A83BA4FB22E56FFDF7ECD687454D922
推荐使用
PIKPAK网盘
下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看
下载BT种子文件
磁力链接
迅雷下载
PIKPAK在线播放
世界之窗
91视频
含羞草
欲漫涩
逼哩逼哩
成人快手
51品茶
抖阴破解版
极乐禁地
91短视频
暗网Xvideo
TikTok成人版
PornHub
听泉鉴鲍
少女日记
草榴社区
哆哔涩漫
呦乐园
萝莉岛
悠悠禁区
悠悠禁区
拔萝卜
疯马秀
最近搜索
nsodn-003
大海探花
小狸酱
1977836
国产夫妻论坛
反差调教
皮条
輪姦
ssis-135
最新作品
主播乐乐
发达秘笈
调教羞辱
刷礼物
鱼里
痴人
私密分享交
完具
福建兄妹
超级丰满
母子、
单男合集
东北 熟女
波波妹妹
银月+ai
极品巨乳肥臀
rebel.moon.directors.cut. 1080p.x265 psa
多肉小
白阿姨
极品小萝莉无套
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
151.3 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4
145.0 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.vtt
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
117.7 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
108.5 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
107.7 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4
104.2 MB
15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4
96.6 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
67.7 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
61.6 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
10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4
57.0 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
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
50.2 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
42.9 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
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4
38.1 MB
15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).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
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4
36.6 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.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
33. Part 5 Mathematics/1. What is a matrix.mp4
35.2 MB
25. Python - Advanced Python Tools/1. Object Oriented Programming.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
33.1 MB
27. Advanced Statistical Methods - Linear regression/7. Python Packages Installation.vtt
32.9 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
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4
31.0 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.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
30.4 MB
13. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4
30.2 MB
35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4
30.1 MB
28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.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
10. Statistics - Descriptive Statistics/27. Covariance.vtt
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
39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.vtt
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
25.3 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
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp4
21.6 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4
21.6 MB
37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4
21.3 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp4
21.2 MB
45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4
21.1 MB
13. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp4
20.9 MB
25. Python - Advanced Python Tools/7. Importing Modules in Python.mp4
20.9 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4
20.4 MB
10. Statistics - Descriptive Statistics/19. Skewness.mp4
20.3 MB
19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4
19.8 MB
43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4
19.8 MB
42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4
19.5 MB
25. Python - Advanced Python Tools/5. What is the Standard Library.mp4
18.9 MB
35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4
18.8 MB
43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4
18.7 MB
37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4
18.3 MB
26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4
18.2 MB
40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4
18.0 MB
29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4
17.9 MB
18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4
17.9 MB
24. Python - Iterations/8. How to Iterate over Dictionaries.mp4
17.8 MB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp4
17.6 MB
23. Python - Sequences/6. Tuples.mp4
17.5 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4
17.2 MB
28. Advanced Statistical Methods - Multiple Linear Regression/6. Test for Significance of the Model (F-Test).mp4
17.2 MB
24. Python - Iterations/6. Conditional Statements and Loops.mp4
16.9 MB
22. Python - Python Functions/5. Conditional Statements and Functions.mp4
16.4 MB
12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4
16.2 MB
24. Python - Iterations/3. While Loops and Incrementing.mp4
16.2 MB
22. Python - Python Functions/3. Defining a Function in Python - Part II.mp4
15.5 MB
27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp4
15.5 MB
30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4
15.3 MB
37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4
15.3 MB
40. Deep Learning - Initialization/2. Types of Simple Initializations.mp4
15.0 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4
14.7 MB
17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4
14.5 MB
10. Statistics - Descriptive Statistics/11. The Histogram.mp4
14.4 MB
48. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4
14.4 MB
21. Python - Conditional Statements/1. The IF Statement.mp4
14.3 MB
21. Python - Conditional Statements/3. The ELSE Statement.mp4
14.3 MB
43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4
13.5 MB
28. Advanced Statistical Methods - Multiple Linear Regression/9. A1 Linearity.mp4
13.2 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4
13.1 MB
27. Advanced Statistical Methods - Linear regression/10. Using Seaborn for Graphs.mp4
12.8 MB
44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4
12.8 MB
42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4
12.4 MB
24. Python - Iterations/1. For Loops.mp4
12.4 MB
24. Python - Iterations/4. Lists with the range() Function.mp4
12.0 MB
21. Python - Conditional Statements/5. A Note on Boolean Values.vtt
11.8 MB
21. Python - Conditional Statements/5. A Note on Boolean Values.mp4
11.8 MB
44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp4
11.8 MB
33. Part 5 Mathematics/12. Errors when Adding Matrices.mp4
11.7 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4
11.6 MB
20. Python - Other Python Operators/1. Comparison Operators.mp4
10.7 MB
31. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4
10.4 MB
24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4
9.9 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4
9.5 MB
25. Python - Advanced Python Tools/3. Modules and Packages.mp4
8.9 MB
22. Python - Python Functions/4. How to Use a Function within a Function.mp4
8.5 MB
22. Python - Python Functions/1. Defining a Function in Python.mp4
8.1 MB
22. Python - Python Functions/6. Functions Containing a Few Arguments.mp4
7.9 MB
2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience.png.png
7.3 MB
2. The Field of Data Science - The Various Data Science Disciplines/9.1 365_DataScience.png.png
7.3 MB
19. Python - Basic Python Syntax/12. Structuring with Indentation.mp4
7.2 MB
19. Python - Basic Python Syntax/3. The Double Equality Sign.mp4
6.3 MB
19. Python - Basic Python Syntax/10. Indexing Elements.mp4
6.2 MB
27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.mp4
5.4 MB
19. Python - Basic Python Syntax/7. Add Comments.mp4
5.2 MB
19. Python - Basic Python Syntax/5. How to Reassign Values.mp4
4.2 MB
19. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4
2.5 MB
18. Python - Variables and Data Types/1.1 Python Introduction - Course Notes.pdf.pdf
2.1 MB
14. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx
1.8 MB
14. Statistics - Practical Example Inferential Statistics/2.1 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx
1.8 MB
14. Statistics - Practical Example Inferential Statistics/2.2 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx
1.8 MB
15. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf.pdf
1.3 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1.1 Course Notes - Section 6.pdf.pdf
958.9 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf
958.9 kB
35. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf.pdf
949.9 kB
35. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf.pdf
949.9 kB
44. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv
727.8 kB
15. Statistics - Hypothesis Testing/1.1 Course notes_hypothesis_testing.pdf.pdf
663.8 kB
15. Statistics - Hypothesis Testing/4.1 Course notes_hypothesis_testing.pdf.pdf
663.8 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.2 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
37. Deep Learning - TensorFlow Introduction/1.1 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
37. Deep Learning - TensorFlow Introduction/4.1 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
10. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf.pdf
493.8 kB
9. Part 2 Statistics/1.2 Course notes_descriptive_statistics.pdf.pdf
493.8 kB
12. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf.pdf
391.5 kB
12. Statistics - Inferential Statistics Fundamentals/2.1 Course notes_inferential statistics.pdf.pdf
391.5 kB
2. The Field of Data Science - The Various Data Science Disciplines/5.1 365_DataScience_Diagram.pdf.pdf
330.8 kB
2. The Field of Data Science - The Various Data Science Disciplines/7.2 365_DataScience_Diagram.pdf.pdf
330.8 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf
186.7 kB
11. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx
150.0 kB
11. Statistics - Practical Example Descriptive Statistics/2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx
149.7 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/1.1 data_preprocessing_homework.pdf.pdf
137.7 kB
11. Statistics - Practical Example Descriptive Statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx
123.2 kB
16. Statistics - Practical Example Hypothesis Testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx
53.0 kB
16. Statistics - Practical Example Hypothesis Testing/2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx
45.1 kB
16. Statistics - Practical Example Hypothesis Testing/2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx
44.4 kB
35. Deep Learning - Introduction to Neural Networks/21.1 GD-function-example.xlsx.xlsx
43.2 kB
10. Statistics - Descriptive Statistics/7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx
42.1 kB
10. Statistics - Descriptive Statistics/16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx
41.4 kB
10. Statistics - Descriptive Statistics/19.1 2.8. Skewness_lesson.xlsx.xlsx
35.5 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/1.3 Absenteeism_data.csv.csv
32.8 kB
10. Statistics - Descriptive Statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx
31.5 kB
10. Statistics - Descriptive Statistics/29.2 2.11. Covariance_exercise_solution.xlsx.xlsx
30.2 kB
10. Statistics - Descriptive Statistics/32.1 2.12. Correlation_exercise_solution.xlsx.xlsx
30.2 kB
10. Statistics - Descriptive Statistics/32.2 2.12. Correlation_exercise.xlsx.xlsx
30.0 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1.1 Absenteeism_preprocessed.csv.csv
29.8 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/1.2 df_preprocessed.csv.csv
29.8 kB
10. Statistics - Descriptive Statistics/14.1 2.6. Cross table and scatter plot.xlsx.xlsx
26.7 kB
10. Statistics - Descriptive Statistics/27.1 2.11. Covariance_lesson.xlsx.xlsx
25.5 kB
12. Statistics - Inferential Statistics Fundamentals/8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx
24.6 kB
9. Part 2 Statistics/1.1 Statistics Glossary.xlsx.xlsx
20.8 kB
10. Statistics - Descriptive Statistics/29.1 2.11. Covariance_exercise.xlsx.xlsx
20.7 kB
10. Statistics - Descriptive Statistics/21.2 2.8. Skewness_exercise_solution.xlsx.xlsx
20.2 kB
29. Advanced Statistical Methods - Logistic Regression/11.2 Bank_data.csv.csv
20.0 kB
29. Advanced Statistical Methods - Logistic Regression/13.3 Bank_data.csv.csv
20.0 kB
29. Advanced Statistical Methods - Logistic Regression/16.2 Bank_data.csv.csv
20.0 kB
29. Advanced Statistical Methods - Logistic Regression/8.2 Bank_data.csv.csv
20.0 kB
12. Statistics - Inferential Statistics Fundamentals/2.2 3.2. What is a distribution_lesson.xlsx.xlsx
19.9 kB
10. Statistics - Descriptive Statistics/11.1 2.5. The Histogram_lesson.xlsx.xlsx
19.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/3.1 3.9. The z-table.xlsx.xlsx
18.9 kB
13. Statistics - Inferential Statistics Confidence Intervals/4.3 3.9. The z-table.xlsx.xlsx
18.9 kB
11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.vtt
18.4 kB
10. Statistics - Descriptive Statistics/13.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx
17.5 kB
10. Statistics - Descriptive Statistics/16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx
16.7 kB
13. Statistics - Inferential Statistics Confidence Intervals/8.2 3.11. The t-table.xlsx.xlsx
16.2 kB
10. Statistics - Descriptive Statistics/13.1 2.5.The-Histogram-exercise.xlsx.xlsx
15.9 kB
10. Statistics - Descriptive Statistics/7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx
15.6 kB
15. Statistics - Hypothesis Testing/12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx
14.9 kB
15. Statistics - Hypothesis Testing/15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx
14.7 kB
13. Statistics - Inferential Statistics Confidence Intervals/13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx
14.6 kB
13. Statistics - Inferential Statistics Confidence Intervals/13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx
14.1 kB
10. Statistics - Descriptive Statistics/10.2 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx
13.5 kB
15. Statistics - Hypothesis Testing/15.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx
13.1 kB
15. Statistics - Hypothesis Testing/13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx
13.0 kB
10. Statistics - Descriptive Statistics/26.2 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx.xlsx
12.7 kB
12. Statistics - Inferential Statistics Fundamentals/8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx
12.3 kB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.vtt
12.2 kB
10. Statistics - Descriptive Statistics/10.1 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx
12.0 kB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.vtt
12.0 kB
10. Statistics - Descriptive Statistics/8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx
11.7 kB
15. Statistics - Hypothesis Testing/20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx
11.7 kB
10. Statistics - Descriptive Statistics/18.1 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx
11.6 kB
15. Statistics - Hypothesis Testing/13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx
11.6 kB
10. Statistics - Descriptive Statistics/26.1 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx
11.6 kB
15. Statistics - Hypothesis Testing/17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx
11.5 kB
15. Statistics - Hypothesis Testing/9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx
11.5 kB
13. Statistics - Inferential Statistics Confidence Intervals/3.2 3.9. Population variance known, z-score_lesson.xlsx.xlsx
11.5 kB
13. Statistics - Inferential Statistics Confidence Intervals/4.1 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx
11.4 kB
13. Statistics - Inferential Statistics Confidence Intervals/9.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx
11.4 kB
10. Statistics - Descriptive Statistics/23.2 2.9. Variance_exercise_solution.xlsx.xlsx
11.3 kB
15. Statistics - Hypothesis Testing/9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx
11.3 kB
10. Statistics - Descriptive Statistics/24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx
11.2 kB
15. Statistics - Hypothesis Testing/8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx
11.2 kB
10. Statistics - Descriptive Statistics/18.2 2.7. Mean, median and mode_exercise.xlsx.xlsx
11.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx
11.1 kB
10. Statistics - Descriptive Statistics/23.1 2.9. Variance_exercise.xlsx.xlsx
11.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/8.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx
11.0 kB
15. Statistics - Hypothesis Testing/17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx
11.0 kB
13. Statistics - Inferential Statistics Confidence Intervals/9.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx
10.9 kB
15. Statistics - Hypothesis Testing/20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx
10.8 kB
10. Statistics - Descriptive Statistics/17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx
10.7 kB
13. Statistics - Inferential Statistics Confidence Intervals/12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx
10.7 kB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.vtt
10.7 kB
12. Statistics - Inferential Statistics Fundamentals/6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx
10.6 kB
31. Advanced Statistical Methods - K-Means Clustering/5.1 Categorical.csv.csv
10.6 kB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.vtt
10.6 kB
13. Statistics - Inferential Statistics Confidence Intervals/15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx
10.4 kB
10. Statistics - Descriptive Statistics/22.1 2.9. Variance_lesson.xlsx.xlsx
10.3 kB
13. Statistics - Inferential Statistics Confidence Intervals/14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx
10.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx
10.1 kB
13. Statistics - Inferential Statistics Confidence Intervals/17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx
10.0 kB
15. Statistics - Hypothesis Testing/14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx
10.0 kB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.vtt
9.9 kB
15. Statistics - Hypothesis Testing/16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx
9.9 kB
13. Statistics - Inferential Statistics Confidence Intervals/16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx
9.7 kB
10. Statistics - Descriptive Statistics/21.1 2.8. Skewness_exercise.xlsx.xlsx
9.7 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).vtt
9.7 kB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.vtt
9.6 kB
15. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx
9.5 kB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt
9.5 kB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.vtt
9.5 kB
13. Statistics - Inferential Statistics Confidence Intervals/17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx
9.4 kB
46. Software Integration/5. Taking a Closer Look at APIs.vtt
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
48. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.vtt
9.0 kB
51. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.vtt
9.0 kB
13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.vtt
8.9 kB
51. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.vtt
8.6 kB
29. Advanced Statistical Methods - Logistic Regression/16.1 Bank_data_testing.csv.csv
8.5 kB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.vtt
8.5 kB
31. Advanced Statistical Methods - K-Means Clustering/3.1 Countries_exercise.csv.csv
8.5 kB
31. Advanced Statistical Methods - K-Means Clustering/7.3 Countries_exercise.csv.csv
8.5 kB
33. Part 5 Mathematics/15. Dot Product of Matrices.vtt
8.4 kB
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
15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.vtt
8.0 kB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.vtt
7.9 kB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.vtt
7.7 kB
46. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.vtt
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
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.vtt
7.5 kB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.vtt
7.3 kB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.vtt
7.3 kB
28. Advanced Statistical Methods - Multiple Linear Regression/18. Dealing with Categorical Data - Dummy Variables.vtt
7.3 kB
13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.vtt
7.3 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.vtt
7.2 kB
48. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.vtt
7.1 kB
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.vtt
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
6.8 kB
50. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.vtt
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
15. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.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/5.3 Example_bank_data.csv.csv
6.4 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
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
15. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).vtt
4.8 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
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
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/15.2 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
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
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/14.3 iris_dataset.csv.csv
2.5 kB
31. Advanced Statistical Methods - K-Means Clustering/15.1 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
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
15. Statistics - Hypothesis Testing/2. Further Reading on Null and Alternative Hypothesis.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
1.2 kB
22. Python - Python Functions/6. Functions Containing a Few Arguments.vtt
1.2 kB
45. Deep Learning - Conclusion/4. DeepMind and Deep Learning.html
1.1 kB
19. Python - Basic Python Syntax/9. Understanding Line Continuation.vtt
1.0 kB
50. Case Study - Loading the 'absenteeism_module'/4. Exporting the Obtained Data Set as a .csv.html
998 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/8. EXERCISE - Dropping a Column from a DataFrame in Python.html
866 Bytes
1. Part 1 Introduction/3. Download All Resources.html
730 Bytes
51. Case Study - Analyzing the Predicted Outputs in Tableau/5. EXERCISE - Transportation Expense vs Probability.html
561 Bytes
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9. Backpropagation - A Peek into the Mathematics of Optimization.html
539 Bytes
10. Statistics - Descriptive Statistics/23. Variance Exercise.html
522 Bytes
50. Case Study - Loading the 'absenteeism_module'/1. Are You Sure You're All Set.html
519 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html
462 Bytes
45. Deep Learning - Conclusion/7. Download All Resources.html
458 Bytes
44. Deep Learning - Business Case Example/12. Business Case Final Exercise.html
439 Bytes
51. Case Study - Analyzing the Predicted Outputs in Tableau/3. EXERCISE - Reasons vs Probability.html
401 Bytes
51. Case Study - Analyzing the Predicted Outputs in Tableau/1. EXERCISE - Age vs Probability.html
385 Bytes
44. Deep Learning - Business Case Example/5. Business Case Preprocessing Exercise.html
383 Bytes
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15. EXERCISE - Saving the Model (and Scaler).html
284 Bytes
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11.1 Logistic Regression prior to Backward Elimination.html
226 Bytes
33. Part 5 Mathematics/12.1 Errors when Adding Matrices Python Notebook.html
220 Bytes
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9.1 Logistic Regression prior to Custom Scaler.html
219 Bytes
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15.2 Logistic Regression with Comments.html
210 Bytes
31. Advanced Statistical Methods - K-Means Clustering/15.3 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html
204 Bytes
31. Advanced Statistical Methods - K-Means Clustering/15.4 EXERCISE Species Segmentation with Cluster Analysis (Part 2) - Solution.html
204 Bytes
31. Advanced Statistical Methods - K-Means Clustering/14.1 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html
203 Bytes
31. Advanced Statistical Methods - K-Means Clustering/14.2 EXERCISE Species Segmentation with Cluster Analysis (Part 1) - Solution.html
203 Bytes
29. Advanced Statistical Methods - Logistic Regression/11.1 Binary Predictors in a Logistic Regression - Exercise.html
202 Bytes
29. Advanced Statistical Methods - Logistic Regression/11.3 Binary Predictors in a Logistic Regression - Solution.html
202 Bytes
29. Advanced Statistical Methods - Logistic Regression/8.1 Understanding Logistic Regression Tables - Exercise.html
200 Bytes
29. Advanced Statistical Methods - Logistic Regression/8.3 Understanding Logistic Regression Tables - Solution.html
200 Bytes
29. Advanced Statistical Methods - Logistic Regression/13.1 Calculating the Accuracy of the Model - Exercise.html
197 Bytes
29. Advanced Statistical Methods - Logistic Regression/13.2 Calculating the Accuracy of the Model - Solution.html
197 Bytes
49. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15.1 Logistic Regression.html
196 Bytes
31. Advanced Statistical Methods - K-Means Clustering/7.1 How to Choose the Number of Clusters - Solution.html
191 Bytes
31. Advanced Statistical Methods - K-Means Clustering/7.2 How to Choose the Number of Clusters - Exercise.html
191 Bytes
29. Advanced Statistical Methods - Logistic Regression/5.1 Building a Logistic Regression - Solution.html
190 Bytes
29. Advanced Statistical Methods - Logistic Regression/5.2 Building a Logistic Regression - Exercise.html
190 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/18. EXERCISE - Using .concat() in Python.html
189 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/29.2 Removing the “Date” Column.html
188 Bytes
31. Advanced Statistical Methods - K-Means Clustering/3.2 A Simple Example of Clustering - Solution.html
185 Bytes
31. Advanced Statistical Methods - K-Means Clustering/3.3 A Simple Example of Clustering - Exercise.html
185 Bytes
50. Case Study - Loading the 'absenteeism_module'/4.1 Deploying the ‘absenteeism_module.html
185 Bytes
31. Advanced Statistical Methods - K-Means Clustering/5.2 Clustering Categorical Data - Solution.html
182 Bytes
31. Advanced Statistical Methods - K-Means Clustering/5.3 Clustering Categorical Data - Exercise.html
182 Bytes
33. Part 5 Mathematics/7.1 Arrays in Python Notebook.html
181 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/23.1 Creating Checkpoints.html
181 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/29.1 Preprocessing.html
181 Bytes
29. Advanced Statistical Methods - Logistic Regression/16.3 Testing the Model - Exercise.html
178 Bytes
33. Part 5 Mathematics/10.1 Addition and Subtraction of Matrices Python Notebook.html
178 Bytes
29. Advanced Statistical Methods - Logistic Regression/16.4 Testing the Model - Solution.html
177 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.5 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html
172 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html
172 Bytes
33. Part 5 Mathematics/15.1 Dot Product of Matrices Python Notebook.html
171 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/32.2 Exercises and solutions.html
170 Bytes
33. Part 5 Mathematics/13.1 Transpose of a Matrix Python Notebook.html
167 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html
167 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.6 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html
165 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.9 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html
165 Bytes
37. Deep Learning - TensorFlow Introduction/9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html
162 Bytes
37. Deep Learning - TensorFlow Introduction/9.5 Basic NN Example with TensorFlow Exercise 2.3 Solution.html
162 Bytes
37. Deep Learning - TensorFlow Introduction/9.6 Basic NN Example with TensorFlow Exercise 2.2 Solution.html
162 Bytes
37. Deep Learning - TensorFlow Introduction/9.8 Basic NN Example with TensorFlow Exercise 2.4 Solution.html
162 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.1 TensorFlow MNIST 'Time' Solution.html
162 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.3 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html
162 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.7 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html
162 Bytes
10. Statistics - Descriptive Statistics/12. The Histogram.html
161 Bytes
10. Statistics - Descriptive Statistics/15. Cross Tables and Scatter Plots.html
161 Bytes
10. Statistics - Descriptive Statistics/2. Types of Data.html
161 Bytes
10. Statistics - Descriptive Statistics/20. Skewness.html
161 Bytes
10. Statistics - Descriptive Statistics/25. Standard Deviation.html
161 Bytes
10. Statistics - Descriptive Statistics/28. Covariance.html
161 Bytes
10. Statistics - Descriptive Statistics/31. Correlation.html
161 Bytes
10. Statistics - Descriptive Statistics/4. Levels of Measurement.html
161 Bytes
10. Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html
161 Bytes
10. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html
161 Bytes
12. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html
161 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/11. Margin of Error.html
161 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html
161 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/7. Student's T Distribution.html
161 Bytes
15. Statistics - Hypothesis Testing/11. p-value.html
161 Bytes
15. Statistics - Hypothesis Testing/19. Test for the mean. Independent samples (Part 2).html
161 Bytes
15. Statistics - Hypothesis Testing/3. Null vs Alternative Hypothesis.html
161 Bytes
15. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html
161 Bytes
15. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html
161 Bytes
17. Part 3 Introduction to Python/10. Jupyter's Interface.html
161 Bytes
17. Part 3 Introduction to Python/2. Introduction to Programming.html
161 Bytes
17. Part 3 Introduction to Python/4. Why Python.html
161 Bytes
17. Part 3 Introduction to Python/6. Why Jupyter.html
161 Bytes
18. Python - Variables and Data Types/2. Variables.html
161 Bytes
18. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html
161 Bytes
18. Python - Variables and Data Types/6. Python Strings.html
161 Bytes
19. Python - Basic Python Syntax/11. Indexing Elements.html
161 Bytes
19. Python - Basic Python Syntax/13. Structuring with Indentation.html
161 Bytes
19. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html
161 Bytes
19. Python - Basic Python Syntax/4. The Double Equality Sign.html
161 Bytes
19. Python - Basic Python Syntax/6. How to Reassign Values.html
161 Bytes
19. Python - Basic Python Syntax/8. Add Comments.html
161 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/10. A Breakdown of our Data Science Infographic.html
161 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/2. Data Science and Business Buzzwords Why are there so many.html
161 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/4. What is the difference between Analysis and Analytics.html
161 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/6. Business Analytics, Data Analytics, and Data Science An Introduction.html
161 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/8. Continuing with BI, ML, and AI.html
161 Bytes
20. Python - Other Python Operators/2. Comparison Operators.html
161 Bytes
20. Python - Other Python Operators/4. Logical and Identity Operators.html
161 Bytes
21. Python - Conditional Statements/2. The IF Statement.html
161 Bytes
21. Python - Conditional Statements/6. A Note on Boolean Values.html
161 Bytes
22. Python - Python Functions/8. Python Functions.html
161 Bytes
23. Python - Sequences/2. Lists.html
161 Bytes
23. Python - Sequences/4. Using Methods.html
161 Bytes
23. Python - Sequences/8. Dictionaries.html
161 Bytes
24. Python - Iterations/2. For Loops.html
161 Bytes
24. Python - Iterations/5. Lists with the range() Function.html
161 Bytes
25. Python - Advanced Python Tools/2. Object Oriented Programming.html
161 Bytes
25. Python - Advanced Python Tools/4. Modules and Packages.html
161 Bytes
25. Python - Advanced Python Tools/6. What is the Standard Library.html
161 Bytes
25. Python - Advanced Python Tools/8. Importing Modules in Python.html
161 Bytes
26. Part 4 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/12. How to Interpret the Regression Table.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/14. Decomposition of Variability.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/16. What is the OLS.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/18. R-Squared.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/2. The Linear Regression Model.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/4. Correlation vs Regression.html
161 Bytes
27. Advanced Statistical Methods - Linear regression/6. Geometrical Representation of the Linear Regression Model.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/10. A1 Linearity.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/12. A2 No Endogeneity.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/15. A4 No autocorrelation.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/17. A5 No Multicollinearity.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/2. Multiple Linear Regression.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/4. Adjusted R-Squared.html
161 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/8. OLS Assumptions.html
161 Bytes
3. The Field of Data Science - Connecting the Data Science Disciplines/2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html
161 Bytes
33. Part 5 Mathematics/11. Addition and Subtraction of Matrices.html
161 Bytes
33. Part 5 Mathematics/2. What is a Matrix.html
161 Bytes
33. Part 5 Mathematics/4. Scalars and Vectors.html
161 Bytes
33. Part 5 Mathematics/6. Linear Algebra and Geometry.html
161 Bytes
33. Part 5 Mathematics/9. What is a Tensor.html
161 Bytes
34. Part 6 Deep Learning/2. What is Machine Learning.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/4. Training the Model.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html
161 Bytes
35. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html
161 Bytes
4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason behind these Disciplines.html
161 Bytes
46. Software Integration/10. Software Integration - Explained.html
161 Bytes
46. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html
161 Bytes
46. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html
161 Bytes
46. Software Integration/6. Taking a Closer Look at APIs.html
161 Bytes
46. Software Integration/8. Communication between Software Products through Text Files.html
161 Bytes
47. Case Study - What's Next in the Course/4. Introducing the Data Set.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/5. Techniques for Working with Big Data.html
161 Bytes
5. The Field of Data Science - Popular Data Science Techniques/8. Business Intelligence (BI) Techniques.html
161 Bytes
6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html
161 Bytes
7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html
161 Bytes
8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html
161 Bytes
9. Part 2 Statistics/2. Population and Sample.html
161 Bytes
37. Deep Learning - TensorFlow Introduction/9.3 Basic NN Example with TensorFlow Exercise 4 Solution.html
160 Bytes
37. Deep Learning - TensorFlow Introduction/9.4 Basic NN Example with TensorFlow Exercise 1 Solution.html
160 Bytes
37. Deep Learning - TensorFlow Introduction/9.7 Basic NN Example with TensorFlow Exercise 3 Solution.html
160 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.4 TensorFlow MNIST '3. Width and Depth' Solution.html
160 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/3.1 TensorFlow MNIST Part 1 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/4.1 TensorFlow MNIST Part 2 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/5.1 TensorFlow MNIST Part 3 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/6.1 TensorFlow MNIST Part 4 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/7.1 TensorFlow MNIST Part 5 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/8.1 TensorFlow MNIST Part 6 with Comments.html
159 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.10 TensorFlow MNIST 'Around 98% Accuracy' Solution.html
157 Bytes
37. Deep Learning - TensorFlow Introduction/8.1 Basic NN Example with TensorFlow (Complete).html
156 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/32.1 Preprocessing.html
156 Bytes
33. Part 5 Mathematics/14.1 Dot Product Python Notebook.html
154 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Basic NN Example Exercise 3a Solution.html
154 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Basic NN Example Exercise 3b Solution.html
154 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Basic NN Example Exercise 3d Solution.html
154 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.8 Basic NN Example Exercise 3c Solution.html
154 Bytes
37. Deep Learning - TensorFlow Introduction/5.1 Basic NN Example with TensorFlow (Part 1).html
154 Bytes
37. Deep Learning - TensorFlow Introduction/6.1 Basic NN Example with TensorFlow (Part 2).html
154 Bytes
37. Deep Learning - TensorFlow Introduction/7.1 Basic NN Example with TensorFlow (Part 3).html
154 Bytes
37. Deep Learning - TensorFlow Introduction/9.1 Basic NN Example with TensorFlow (All Exercises).html
154 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/9.1 TensorFlow MNIST Complete Code with Comments.html
152 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.11 TensorFlow MNIST '1. Width' Solution.html
150 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/11.2 TensorFlow MNIST '2. Depth' Solution.html
150 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.2 Basic NN Example Exercise 2 Solution.html
149 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Basic NN Example Exercise 5 Solution.html
149 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Basic NN Example Exercise 4 Solution.html
149 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.7 Basic NN Example Exercise 1 Solution.html
149 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Basic NN Example Exercise 6 Solution.html
149 Bytes
33. Part 5 Mathematics/8.1 Tensors Notebook.html
148 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4.1 Basic NN Example (Part 4).html
145 Bytes
43. Deep Learning - Classifying on the MNIST Dataset/10.1 TensorFlow MNIST All Exercises.html
144 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 Basic NN Example (All Exercises).html
143 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html
142 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html
137 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.1 Bais NN Example Part 1.html
136 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Basic NN Example (Part 2).html
136 Bytes
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Basic NN Example (Part 3).html
136 Bytes
18. Python - Variables and Data Types/1.2 Variables - Resources.html
134 Bytes
18. Python - Variables and Data Types/3.1 Numbers and Boolean Values - Resources.html
134 Bytes
18. Python - Variables and Data Types/5.1 Strings - Resources.html
134 Bytes
19. Python - Basic Python Syntax/1.1 Arithmetic Operators - Resources.html
134 Bytes
19. Python - Basic Python Syntax/10.1 Indexing Elements - Resources.html
134 Bytes
19. Python - Basic Python Syntax/12.1 Structure Your Code with Indentation - Resources.html
134 Bytes
19. Python - Basic Python Syntax/3.1 The Double Equality Sign - Resources.html
134 Bytes
19. Python - Basic Python Syntax/5.1 Reassign Values - Resources.html
134 Bytes
19. Python - Basic Python Syntax/7.1 Add Comments - Resources.html
134 Bytes
19. Python - Basic Python Syntax/9.1 Line Continuation - Resources.html
134 Bytes
20. Python - Other Python Operators/1.1 Comparison Operators - Resources.html
134 Bytes
20. Python - Other Python Operators/3.1 Logical and Identity Operators - Resources.html
134 Bytes
21. Python - Conditional Statements/1.1 Introduction to the If Statement - Resources.html
134 Bytes
21. Python - Conditional Statements/3.1 Add an Else Statement - Resources.html
134 Bytes
21. Python - Conditional Statements/4.1 Else if, for Brief - Elif - Resources.html
134 Bytes
21. Python - Conditional Statements/5.1 A Note on Boolean Values - Resources.html
134 Bytes
22. Python - Python Functions/1.1 Defining a Function in Python - Resources.html
134 Bytes
22. Python - Python Functions/2.1 Creating a Function with a Parameter - Resources.html
134 Bytes
22. Python - Python Functions/3.1 Another Way to Define a Function - Resources.html
134 Bytes
22. Python - Python Functions/4.1 Using a Function in Another Function - Resources.html
134 Bytes
22. Python - Python Functions/5.1 Combining Conditional Statements and Functions - Resources.html
134 Bytes
22. Python - Python Functions/6.1 Creating Functions Containing a Few Arguments - Resources.html
134 Bytes
22. Python - Python Functions/7.1 Notable Built-In Functions in Python - Resources.html
134 Bytes
23. Python - Sequences/1.1 Lists - Resources.html
134 Bytes
23. Python - Sequences/3.1 Help Yourself with Methods - Resources.html
134 Bytes
23. Python - Sequences/5.1 List Slicing - Resources.html
134 Bytes
23. Python - Sequences/6.1 Tuples - Resources.html
134 Bytes
23. Python - Sequences/7.1 Dictionaries - Resources.html
134 Bytes
24. Python - Iterations/1.1 For Loops - Resources.html
134 Bytes
24. Python - Iterations/3.1 While Loops and Incrementing - Resources.html
134 Bytes
24. Python - Iterations/4.1 Create Lists with the range() Function - Resources.html
134 Bytes
24. Python - Iterations/6.1 Use Conditional Statements and Loops Together - Resources.html
134 Bytes
24. Python - Iterations/7.1 All In - Conditional Statements, Functions, and Loops - Resources.html
134 Bytes
24. Python - Iterations/8.1 Iterating over Dictionaries - Resources.html
134 Bytes
27. Advanced Statistical Methods - Linear regression/8.1 Simple linear regression - Lecture.html
134 Bytes
27. Advanced Statistical Methods - Linear regression/8.2 Simple linear regression - Exercise.html
134 Bytes
27. Advanced Statistical Methods - Linear regression/9.1 Simple Linear Regression Exercise.html
134 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/18.1 Dummies - Lecture.html
134 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/19.1 Dummy variables Exercise.html
134 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/20.1 Making predictions - Lecture.html
134 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/3.1 Multiple linear regression - Lecture.html
134 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/5.1 Multiple Linear Regression Exercise.html
134 Bytes
29. Advanced Statistical Methods - Logistic Regression/10.1 Binary predictors.html
134 Bytes
29. Advanced Statistical Methods - Logistic Regression/12.1 Accuracy.html
134 Bytes
29. Advanced Statistical Methods - Logistic Regression/15.1 Test dataset.html
134 Bytes
29. Advanced Statistical Methods - Logistic Regression/2.1 Simple logistic regression example.html
134 Bytes
29. Advanced Statistical Methods - Logistic Regression/4.1 Building a logistic regression.html
134 Bytes
31. Advanced Statistical Methods - K-Means Clustering/11.1 Market segmentation example.html
134 Bytes
31. Advanced Statistical Methods - K-Means Clustering/12.1 Market segmentation example (Part 2).html
134 Bytes
31. Advanced Statistical Methods - K-Means Clustering/2.1 Country clusters.html
134 Bytes
31. Advanced Statistical Methods - K-Means Clustering/4.1 Clustering categorical data.html
134 Bytes
31. Advanced Statistical Methods - K-Means Clustering/6.1 Selecting the number of clusters.html
134 Bytes
32. Advanced Statistical Methods - Other Types of Clustering/3.1 Heatmaps.html
134 Bytes
44. Deep Learning - Business Case Example/11.1 TensorFlow Business Case Homework.html
134 Bytes
44. Deep Learning - Business Case Example/12.1 TensorFlow Business Case Homework.html
134 Bytes
44. Deep Learning - Business Case Example/4.1 Audiobooks Preprocessing.html
134 Bytes
44. Deep Learning - Business Case Example/5.1 Preprocessing Exercise.html
134 Bytes
44. Deep Learning - Business Case Example/6.1 Creating a Data Provider (Class).html
134 Bytes
44. Deep Learning - Business Case Example/7.1 TensorFlow Business Case Model Outline.html
134 Bytes
44. Deep Learning - Business Case Example/8.1 TensorFlow Business Case Optimization.html
134 Bytes
44. Deep Learning - Business Case Example/9.1 TensorFlow Business Case Interpretation.html
134 Bytes
50. Case Study - Loading the 'absenteeism_module'/1.1 5 Files Needed to Deploy the Model.html
134 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html
129 Bytes
[FreeCourseLab.com].url
126 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html
117 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html
116 Bytes
48. Case Study - Preprocessing the 'Absenteeism_data'/9. SOLUTION - Dropping a Column from a DataFrame in Python.html
113 Bytes
29. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html
87 Bytes
29. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html
87 Bytes
29. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html
87 Bytes
29. Advanced Statistical Methods - Logistic Regression/5. Building a Logistic Regression - Exercise.html
87 Bytes
29. Advanced Statistical Methods - Logistic Regression/8. Understanding Logistic Regression Tables - Exercise.html
87 Bytes
31. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html
87 Bytes
31. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html
87 Bytes
31. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html
87 Bytes
31. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data.html
87 Bytes
31. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html
87 Bytes
10. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/13. Histogram Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/21. Skewness Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/29. Covariance Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html
81 Bytes
10. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html
81 Bytes
11. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html
81 Bytes
12. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html
81 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html
81 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html
81 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html
81 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/4. Confidence Intervals; Population Variance Known; z-score; Exercise.html
81 Bytes
13. Statistics - Inferential Statistics Confidence Intervals/9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html
81 Bytes
14. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html
81 Bytes
15. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html
81 Bytes
15. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html
81 Bytes
15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 1). Exercise.html
81 Bytes
15. Statistics - Hypothesis Testing/20. Test for the mean. Independent samples (Part 2) Exercise.html
81 Bytes
15. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html
81 Bytes
16. Statistics - Practical Example Hypothesis Testing/2. Practical Example Hypothesis Testing Exercise.html
81 Bytes
27. Advanced Statistical Methods - Linear regression/9. First Regression in Python Exercise.html
76 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/19. Dealing with Categorical Data - Dummy Variables.html
76 Bytes
28. Advanced Statistical Methods - Multiple Linear Regression/5. Multiple Linear Regression Exercise.html
76 Bytes
随机展示
相关说明
本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！
