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| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx |
146.51KB |
| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx |
1.74MB |
| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx |
51.71KB |
| 1.1 Arithmetic Operators - Resources.html |
134B |
| 1.1 Audiobooks_data.csv.csv |
710.77KB |
| 1.1 Comparison Operators - Resources.html |
134B |
| 1.1 Course notes_descriptive_statistics.pdf.pdf |
482.27KB |
| 1.1 Course notes_hypothesis_testing.pdf.pdf |
648.60KB |
| 1.1 Course notes_inferential statistics.pdf.pdf |
382.32KB |
| 1.1 Course Notes - Section 2.pdf.pdf |
927.67KB |
| 1.1 Course Notes - Section 6.pdf.pdf |
936.42KB |
| 1.1 Defining a Function in Python - Resources.html |
134B |
| 1.1 For Loops - Resources.html |
134B |
| 1.1 Glossary.xlsx.xlsx |
19.97KB |
| 1.1 Introduction to the If Statement - Resources.html |
134B |
| 1.1 Lists - Resources.html |
134B |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
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| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17KB |
| 1.1 Variables - Resources.html |
134B |
| 1.2 Bais NN Example Part 1.html |
136B |
| 1.2 Course notes_descriptive_statistics.pdf.pdf |
482.27KB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
126.87MB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt |
8.99KB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt |
7.90KB |
| 1. A Practical Example What You Will Learn in This Course.mp4 |
49.03MB |
| 1. A Practical Example What You Will Learn in This Course.srt |
6.37KB |
| 1. A Practical Example What You Will Learn in This Course.vtt |
5.62KB |
| 1. Basic NN Example (Part 1).mp4 |
20.60MB |
| 1. Basic NN Example (Part 1).srt |
4.47KB |
| 1. Basic NN Example (Part 1).vtt |
3.91KB |
| 1. Business Case Getting acquainted with the dataset.mp4 |
87.66MB |
| 1. Business Case Getting acquainted with the dataset.srt |
10.79KB |
| 1. Business Case Getting acquainted with the dataset.vtt |
9.37KB |
| 1. Comparison Operators.mp4 |
10.18MB |
| 1. Comparison Operators.srt |
2.47KB |
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| 1. Data Science and Business Buzzwords Why are there so many.mp4 |
81.41MB |
| 1. Data Science and Business Buzzwords Why are there so many.srt |
6.63KB |
| 1. Data Science and Business Buzzwords Why are there so many.vtt |
5.84KB |
| 1. Debunking Common Misconceptions.mp4 |
72.85MB |
| 1. Debunking Common Misconceptions.srt |
5.30KB |
| 1. Debunking Common Misconceptions.vtt |
4.69KB |
| 1. Defining a Function in Python.mp4 |
7.74MB |
| 1. Defining a Function in Python.srt |
2.53KB |
| 1. Defining a Function in Python.vtt |
2.20KB |
| 1. Finding the Job - What to Expect and What to Look for.mp4 |
54.38MB |
| 1. Finding the Job - What to Expect and What to Look for.srt |
4.50KB |
| 1. Finding the Job - What to Expect and What to Look for.vtt |
3.94KB |
| 1. For Loops.mp4 |
11.79MB |
| 1. For Loops.srt |
2.80KB |
| 1. For Loops.vtt |
2.44KB |
| 1. How to Install TensorFlow.mp4 |
14.56MB |
| 1. How to Install TensorFlow.srt |
3.22KB |
| 1. How to Install TensorFlow.vtt |
2.84KB |
| 1. Introduction.mp4 |
15.51MB |
| 1. Introduction.srt |
1.63KB |
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1.44KB |
| 1. Introduction to Cluster Analysis.mp4 |
53.42MB |
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4.80KB |
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| 1. Introduction to Logistic Regression.mp4 |
27.07MB |
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1.62KB |
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1.44KB |
| 1. Introduction to Neural Networks.mp4 |
42.92MB |
| 1. Introduction to Neural Networks.srt |
5.90KB |
| 1. Introduction to Neural Networks.vtt |
5.18KB |
| 1. Introduction to Programming.mp4 |
58.55MB |
| 1. Introduction to Programming.srt |
6.91KB |
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6.08KB |
| 1. Introduction to Regression Analysis.mp4 |
17.32MB |
| 1. Introduction to Regression Analysis.srt |
2.21KB |
| 1. Introduction to Regression Analysis.vtt |
1.95KB |
| 1. K-Means Clustering.mp4 |
27.28MB |
| 1. K-Means Clustering.srt |
6.67KB |
| 1. K-Means Clustering.vtt |
5.76KB |
| 1. Lists.mp4 |
22.00MB |
| 1. Lists.srt |
4.99KB |
| 1. Lists.vtt |
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| 1. MNIST What is the MNIST Dataset.mp4 |
17.82MB |
| 1. MNIST What is the MNIST Dataset.srt |
3.50KB |
| 1. MNIST What is the MNIST Dataset.vtt |
3.07KB |
| 1. Multiple Linear Regression.mp4 |
21.53MB |
| 1. Multiple Linear Regression.srt |
3.35KB |
| 1. Multiple Linear Regression.vtt |
2.93KB |
| 1. Necessary Programming Languages and Software Used in Data Science.mp4 |
103.52MB |
| 1. Necessary Programming Languages and Software Used in Data Science.srt |
7.30KB |
| 1. Necessary Programming Languages and Software Used in Data Science.vtt |
6.42KB |
| 1. Object Oriented Programming.mp4 |
33.59MB |
| 1. Object Oriented Programming.srt |
6.10KB |
| 1. Object Oriented Programming.vtt |
5.34KB |
| 1. Population and Sample.mp4 |
58.11MB |
| 1. Population and Sample.srt |
5.47KB |
| 1. Population and Sample.vtt |
4.81KB |
| 1. Practical Example Descriptive Statistics.mp4 |
159.46MB |
| 1. Practical Example Descriptive Statistics.srt |
20.61KB |
| 1. Practical Example Descriptive Statistics.vtt |
17.85KB |
| 1. Practical Example Hypothesis Testing.mp4 |
69.48MB |
| 1. Practical Example Hypothesis Testing.srt |
8.49KB |
| 1. Practical Example Hypothesis Testing.vtt |
7.43KB |
| 1. Practical Example Inferential Statistics.mp4 |
102.67MB |
| 1. Practical Example Inferential Statistics.srt |
13.65KB |
| 1. Practical Example Inferential Statistics.vtt |
11.90KB |
| 1. Preprocessing Introduction.mp4 |
27.78MB |
| 1. Preprocessing Introduction.srt |
3.87KB |
| 1. Preprocessing Introduction.vtt |
3.39KB |
| 1. Stochastic Gradient Descent.mp4 |
28.68MB |
| 1. Stochastic Gradient Descent.srt |
4.82KB |
| 1. Stochastic Gradient Descent.vtt |
4.18KB |
| 1. Summary of What You Learned.mp4 |
39.76MB |
| 1. Summary of What You Learned.srt |
5.22KB |
| 1. Summary of What You Learned.vtt |
4.61KB |
| 1. Techniques for Working with Traditional Data.mp4 |
138.30MB |
| 1. Techniques for Working with Traditional Data.srt |
10.63KB |
| 1. Techniques for Working with Traditional Data.vtt |
9.30KB |
| 1. The IF Statement.mp4 |
13.63MB |
| 1. The IF Statement.srt |
3.60KB |
| 1. The IF Statement.vtt |
3.12KB |
| 1. The Linear Regression Model.mp4 |
57.37MB |
| 1. The Linear Regression Model.srt |
7.06KB |
| 1. The Linear Regression Model.vtt |
6.14KB |
| 1. The Null vs Alternative Hypothesis.mp4 |
92.12MB |
| 1. The Null vs Alternative Hypothesis.srt |
7.36KB |
| 1. The Null vs Alternative Hypothesis.vtt |
6.43KB |
| 1. The Reason behind these Disciplines.mp4 |
81.19MB |
| 1. The Reason behind these Disciplines.srt |
6.50KB |
| 1. The Reason behind these Disciplines.vtt |
5.69KB |
| 1. Types of Clustering.mp4 |
44.58MB |
| 1. Types of Clustering.srt |
4.66KB |
| 1. Types of Clustering.vtt |
4.12KB |
| 1. Types of Data.mp4 |
72.52MB |
| 1. Types of Data.srt |
5.96KB |
| 1. Types of Data.vtt |
5.25KB |
| 1. Using Arithmetic Operators in Python.mp4 |
18.92MB |
| 1. Using Arithmetic Operators in Python.srt |
4.12KB |
| 1. Using Arithmetic Operators in Python.vtt |
3.58KB |
| 1. Variables.mp4 |
26.61MB |
| 1. Variables.srt |
6.18KB |
| 1. Variables.vtt |
5.35KB |
| 1. What are Confidence Intervals.mp4 |
49.98MB |
| 1. What are Confidence Intervals.srt |
3.26KB |
| 1. What are Confidence Intervals.vtt |
2.86KB |
| 1. What is a Layer.mp4 |
12.50MB |
| 1. What is a Layer.srt |
2.39KB |
| 1. What is a Layer.vtt |
2.13KB |
| 1. What is a matrix.mp4 |
33.59MB |
| 1. What is a matrix.srt |
4.35KB |
| 1. What is a matrix.vtt |
3.80KB |
| 1. What is Initialization.mp4 |
21.76MB |
| 1. What is Initialization.srt |
3.51KB |
| 1. What is Initialization.vtt |
3.09KB |
| 1. What is Overfitting.mp4 |
31.08MB |
| 1. What is Overfitting.srt |
5.58KB |
| 1. What is Overfitting.vtt |
4.93KB |
| 1. What to Expect from this Part.mp4 |
31.10MB |
| 1. What to Expect from this Part.srt |
4.63KB |
| 1. What to Expect from this Part.vtt |
4.05KB |
| 10.1 2.5.The-Histogram-exercise.xlsx.xlsx |
15.50KB |
| 10.1 Addition and Subtraction of Matrices Python Notebook.html |
178B |
| 10.1 Indexing Elements - Resources.html |
134B |
| 10.1 Online p-value calculator.pdf.pdf |
1.22MB |
| 10.1 TensorFlow MNIST All Exercises.html |
144B |
| 10.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx |
17.10KB |
| 10. A2 No Endogeneity.mp4 |
35.67MB |
| 10. A2 No Endogeneity.srt |
5.24KB |
| 10. A2 No Endogeneity.vtt |
4.58KB |
| 10. A Breakdown of our Data Science Infographic.html |
161B |
| 10. Addition and Subtraction of Matrices.mp4 |
32.62MB |
| 10. Addition and Subtraction of Matrices.srt |
4.05KB |
| 10. Addition and Subtraction of Matrices.vtt |
3.48KB |
| 10. Business Case Testing the Model.mp4 |
11.20MB |
| 10. Business Case Testing the Model.srt |
2.71KB |
| 10. Business Case Testing the Model.vtt |
2.36KB |
| 10. Histogram Exercise.html |
81B |
| 10. How is Clustering Useful.mp4 |
74.45MB |
| 10. How is Clustering Useful.srt |
6.40KB |
| 10. How is Clustering Useful.vtt |
5.65KB |
| 10. How to Interpret the Regression Table.mp4 |
44.64MB |
| 10. How to Interpret the Regression Table.srt |
6.31KB |
| 10. How to Interpret the Regression Table.vtt |
5.50KB |
| 10. Indexing Elements.mp4 |
5.94MB |
| 10. Indexing Elements.srt |
1.71KB |
| 10. Indexing Elements.vtt |
1.47KB |
| 10. Jupyter's Interface.html |
161B |
| 10. Margin of Error.html |
161B |
| 10. MNIST Exercises.html |
2.13KB |
| 10. p-value.mp4 |
55.87MB |
| 10. p-value.srt |
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| 10. p-value.vtt |
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| 10. Standard error.mp4 |
22.77MB |
| 10. Standard error.srt |
2.03KB |
| 10. Standard error.vtt |
1.76KB |
| 10. Techniques for Working with Traditional Methods.mp4 |
123.51MB |
| 10. Techniques for Working with Traditional Methods.srt |
11.08KB |
| 10. Techniques for Working with Traditional Methods.vtt |
9.66KB |
| 10. The Linear Model with Multiple Inputs.html |
161B |
| 10. Underfitting and Overfitting.mp4 |
22.29MB |
| 10. Underfitting and Overfitting.srt |
4.98KB |
| 10. Underfitting and Overfitting.vtt |
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| 11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
172B |
| 11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
157B |
| 11.1 2.6. Cross table and scatter plot.xlsx.xlsx |
26.12KB |
| 11.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx |
10.47KB |
| 11.1 TensorFlow Business Case Homework.html |
134B |
| 11.1 TensorFlow MNIST 'Time' Solution.html |
162B |
| 11.1 Test dataset.html |
134B |
| 11.2 TensorFlow MNIST '1. Width' Solution.html |
150B |
| 11.3 TensorFlow MNIST '3. Width and Depth' Solution.html |
160B |
| 11.4 TensorFlow MNIST '2. Depth' Solution.html |
150B |
| 11.5 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
165B |
| 11.6 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
165B |
| 11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
162B |
| 11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
172B |
| 11.9 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
162B |
| 11. A2 No Endogeneity.html |
161B |
| 11. Addition and Subtraction of Matrices.html |
161B |
| 11. Business Case A Comment on the Homework.mp4 |
36.38MB |
| 11. Business Case A Comment on the Homework.srt |
5.30KB |
| 11. Business Case A Comment on the Homework.vtt |
4.65KB |
| 11. Confidence intervals. Two means. Dependent samples.mp4 |
70.47MB |
| 11. Confidence intervals. Two means. Dependent samples.srt |
8.04KB |
| 11. Confidence intervals. Two means. Dependent samples.vtt |
7.10KB |
| 11. Cross Table and Scatter Plot.mp4 |
39.81MB |
| 11. Cross Table and Scatter Plot.srt |
6.69KB |
| 11. Cross Table and Scatter Plot.vtt |
5.87KB |
| 11. Decomposition of Variability.mp4 |
49.66MB |
| 11. Decomposition of Variability.srt |
4.17KB |
| 11. Decomposition of Variability.vtt |
3.67KB |
| 11. Estimators and Estimates.mp4 |
47.83MB |
| 11. Estimators and Estimates.srt |
3.72KB |
| 11. Estimators and Estimates.vtt |
3.27KB |
| 11. Indexing Elements.html |
161B |
| 11. MNIST Solutions.html |
2.19KB |
| 11. p-value.html |
161B |
| 11. Techniques for Working with Traditional Methods.html |
161B |
| 11. Testing the Model.mp4 |
32.27MB |
| 11. Testing the Model.srt |
6.55KB |
| 11. Testing the Model.vtt |
5.70KB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.31MB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
5.47KB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.vtt |
4.79KB |
| 12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx |
40.44KB |
| 12.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx |
14.24KB |
| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx |
14.54KB |
| 12.1 Errors when Adding Matrices Python Notebook.html |
220B |
| 12.1 Structure Your Code with Indentation - Resources.html |
134B |
| 12.1 TensorFlow Business Case Homework.html |
134B |
| 12.2 2.6. Cross table and scatter plot_exercise.xlsx.xlsx |
16.28KB |
| 12.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx |
13.74KB |
| 12. A3 Normality and Homoscedasticity.mp4 |
42.70MB |
| 12. A3 Normality and Homoscedasticity.srt |
6.67KB |
| 12. A3 Normality and Homoscedasticity.vtt |
5.81KB |
| 12. Business Case Final Exercise.html |
439B |
| 12. Confidence intervals. Two means. Dependent samples Exercise.html |
81B |
| 12. Cross Tables and Scatter Plots Exercise.html |
81B |
| 12. Decomposition of Variability.html |
161B |
| 12. Errors when Adding Matrices.mp4 |
11.18MB |
| 12. Errors when Adding Matrices.srt |
2.58KB |
| 12. Errors when Adding Matrices.vtt |
2.27KB |
| 12. Estimators and Estimates.html |
161B |
| 12. Real Life Examples of Traditional Methods.mp4 |
42.78MB |
| 12. Real Life Examples of Traditional Methods.srt |
3.59KB |
| 12. Real Life Examples of Traditional Methods.vtt |
3.14KB |
| 12. Structuring with Indentation.mp4 |
6.81MB |
| 12. Structuring with Indentation.srt |
2.27KB |
| 12. Structuring with Indentation.vtt |
1.96KB |
| 12. Test for the Mean. Population Variance Unknown.mp4 |
40.21MB |
| 12. Test for the Mean. Population Variance Unknown.srt |
5.90KB |
| 12. Test for the Mean. Population Variance Unknown.vtt |
5.18KB |
| 12. The Linear model with Multiple Inputs and Multiple Outputs.html |
161B |
| 13.1 2.7. Mean, median and mode_lesson.xlsx.xlsx |
10.49KB |
| 13.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx |
9.83KB |
| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx |
11.85KB |
| 13.1 Transpose of a Matrix Python Notebook.html |
167B |
| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx |
11.34KB |
| 13. A4 No Autocorrelation.mp4 |
31.52MB |
| 13. A4 No Autocorrelation.srt |
4.91KB |
| 13. A4 No Autocorrelation.vtt |
4.27KB |
| 13. Confidence intervals. Two means. Independent samples (Part 1).mp4 |
28.75MB |
| 13. Confidence intervals. Two means. Independent samples (Part 1).srt |
6.07KB |
| 13. Confidence intervals. Two means. Independent samples (Part 1).vtt |
5.33KB |
| 13. Graphical Representation of Simple Neural Networks.mp4 |
22.64MB |
| 13. Graphical Representation of Simple Neural Networks.srt |
2.69KB |
| 13. Graphical Representation of Simple Neural Networks.vtt |
2.34KB |
| 13. Machine Learning (ML) Techniques.mp4 |
99.32MB |
| 13. Machine Learning (ML) Techniques.srt |
8.74KB |
| 13. Machine Learning (ML) Techniques.vtt |
7.67KB |
| 13. Mean, median and mode.mp4 |
37.07MB |
| 13. Mean, median and mode.srt |
5.73KB |
| 13. Mean, median and mode.vtt |
5.00KB |
| 13. Structuring with Indentation.html |
161B |
| 13. Test for the Mean. Population Variance Unknown Exercise.html |
81B |
| 13. Transpose of a Matrix.mp4 |
38.07MB |
| 13. Transpose of a Matrix.srt |
5.37KB |
| 13. Transpose of a Matrix.vtt |
4.69KB |
| 13. What is the OLS.mp4 |
28.31MB |
| 13. What is the OLS.srt |
3.82KB |
| 13. What is the OLS.vtt |
3.33KB |
| 14.1 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx |
11.35KB |
| 14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx |
10.12KB |
| 14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx |
9.79KB |
| 14.1 Dot Product Python Notebook.html |
154B |
| 14.2 2.7. Mean, median and mode_exercise.xlsx.xlsx |
10.87KB |
| 14.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx |
9.83KB |
| 14. A4 No autocorrelation.html |
161B |
| 14. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html |
81B |
| 14. Dot Product.mp4 |
24.00MB |
| 14. Dot Product.srt |
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| 14. Dot Product.vtt |
3.68KB |
| 14. Graphical Representation of Simple Neural Networks.html |
161B |
| 14. Machine Learning (ML) Techniques.html |
161B |
| 14. Mean, Median and Mode Exercise.html |
81B |
| 14. R-Squared.mp4 |
41.03MB |
| 14. R-Squared.srt |
6.58KB |
| 14. R-Squared.vtt |
5.79KB |
| 14. Test for the Mean. Dependent Samples.mp4 |
50.39MB |
| 14. Test for the Mean. Dependent Samples.srt |
6.70KB |
| 14. Test for the Mean. Dependent Samples.vtt |
5.86KB |
| 15.1 2.8. Skewness_lesson.xlsx.xlsx |
34.63KB |
| 15.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx |
9.52KB |
| 15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx |
12.80KB |
| 15.1 Dot Product of Matrices Python Notebook.html |
171B |
| 15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx |
14.40KB |
| 15. A5 No Multicollinearity.mp4 |
28.71MB |
| 15. A5 No Multicollinearity.srt |
4.62KB |
| 15. A5 No Multicollinearity.vtt |
4.04KB |
| 15. Confidence intervals. Two means. Independent samples (Part 2).mp4 |
26.82MB |
| 15. Confidence intervals. Two means. Independent samples (Part 2).srt |
4.51KB |
| 15. Confidence intervals. Two means. Independent samples (Part 2).vtt |
3.98KB |
| 15. Dot Product of Matrices.mp4 |
49.43MB |
| 15. Dot Product of Matrices.srt |
9.52KB |
| 15. Dot Product of Matrices.vtt |
8.22KB |
| 15. R-Squared.html |
161B |
| 15. Skewness.mp4 |
19.41MB |
| 15. Skewness.srt |
3.65KB |
| 15. Skewness.vtt |
3.20KB |
| 15. Test for the Mean. Dependent Samples Exercise.html |
81B |
| 15. Types of Machine Learning.mp4 |
125.15MB |
| 15. Types of Machine Learning.srt |
10.52KB |
| 15. Types of Machine Learning.vtt |
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| 15. What is the Objective Function.mp4 |
17.91MB |
| 15. What is the Objective Function.srt |
2.12KB |
| 15. What is the Objective Function.vtt |
1.87KB |
| 16.1 2.8. Skewness_exercise.xlsx.xlsx |
9.49KB |
| 16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx |
9.79KB |
| 16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx |
9.63KB |
| 16.2 2.8. Skewness_exercise_solution.xlsx.xlsx |
19.78KB |
| 16.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx |
9.17KB |
| 16. A5 No Multicollinearity.html |
161B |
| 16. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html |
81B |
| 16. Skewness Exercise.html |
81B |
| 16. Test for the mean. Independent samples (Part 1).mp4 |
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626B |
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161B |
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| 5.1 Combining Conditional Statements and Functions - Resources.html |
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| 5.1 List Slicing - Resources.html |
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| 5.1 Preprocessing Exercise.html |
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| 5.1 Reassign Values - Resources.html |
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| 5.1 Strings - Resources.html |
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| 5.1 TensorFlow MNIST Part 3 with Comments.html |
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| 5.2 Basic NN Example (All Exercises).html |
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| 5.3 Basic NN Example Exercise 4 Solution.html |
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| 5.4 Basic NN Example Exercise 1 Solution.html |
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| 5.5 Basic NN Example Exercise 2 Solution.html |
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| 5.6 Basic NN Example Exercise 3d Solution.html |
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| 5.8 Basic NN Example Exercise 3c Solution.html |
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| 5. Activation Functions.mp4 |
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| 5. Activation Functions.srt |
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| 5. An Invaluable Coding Tip.mp4 |
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| 5. A Note on Boolean Values.mp4 |
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| 5. An Overview of RNNs.mp4 |
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| 5. Basic NN Example Exercises.html |
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| 5. Binary and One-Hot Encoding.mp4 |
28.95MB |
| 5. Binary and One-Hot Encoding.srt |
4.81KB |
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| 5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 |
64.51MB |
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.srt |
10.63KB |
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9.26KB |
| 5. Business Case Preprocessing Exercise.html |
383B |
| 5. Categorical Variables - Visualization Techniques.mp4 |
38.46MB |
| 5. Categorical Variables - Visualization Techniques.srt |
6.43KB |
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| 5. Conditional Statements and Functions.mp4 |
15.69MB |
| 5. Conditional Statements and Functions.srt |
3.52KB |
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| 5. Geometrical Representation of the Linear Regression Model.mp4 |
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| 5. How to Reassign Values.mp4 |
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| 5. Learning Rate Schedules Visualized.mp4 |
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| 5. Linear Algebra and Geometry.mp4 |
49.79MB |
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| 5. List Slicing.mp4 |
30.77MB |
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| 5. Lists with the range() Function.html |
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| 5. MNIST Loss and Optimization Algorithm.mp4 |
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| 5. N-Fold Cross Validation.mp4 |
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| 5. Pros and Cons of K-Means Clustering.mp4 |
37.71MB |
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| 5. Python Strings.mp4 |
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| 5. Rejection Region and Significance Level.html |
161B |
| 5. Student's T Distribution.mp4 |
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| 5. Student's T Distribution.srt |
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| 5. Techniques for Working with Big Data.html |
161B |
| 5. Test for Significance of the Model (F-Test).mp4 |
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| 5. Test for Significance of the Model (F-Test).srt |
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| 5. The Normal Distribution.html |
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| 5. Types of File Formats, supporting Tensors.mp4 |
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| 5. Types of Machine Learning.mp4 |
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| 5. What is the Standard Library.mp4 |
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| 5. Why Jupyter.mp4 |
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| 6.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx |
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| 6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx |
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| 6.1 Basic NN Example with TensorFlow (Part 2).html |
154B |
| 6.1 Creating a Data Provider (Class).html |
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| 6.1 Creating Functions Containing a Few Arguments - Resources.html |
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| 6.1 TensorFlow MNIST Part 4 with Comments.html |
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| 6.1 Tuples - Resources.html |
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| 6.1 Use Conditional Statements and Loops Together - Resources.html |
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| 6.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx |
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| 6. Activation Functions Softmax Activation.mp4 |
25.92MB |
| 6. Activation Functions Softmax Activation.srt |
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| 6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp4 |
26.35MB |
| 6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).srt |
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| 6. A Note on Boolean Values.html |
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| 6. An Overview of non-NN Approaches.mp4 |
44.77MB |
| 6. An Overview of non-NN Approaches.srt |
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| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 |
38.49MB |
| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt |
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| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt |
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| 6. Business Analytics, Data Analytics, and Data Science An Introduction.html |
161B |
| 6. Calculating the Accuracy of the Model.mp4 |
43.90MB |
| 6. Calculating the Accuracy of the Model.srt |
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| 6. Calculating the Accuracy of the Model.vtt |
4.51KB |
| 6. Categorical Variables Exercise.html |
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| 6. Conditional Statements and Loops.mp4 |
16.09MB |
| 6. Conditional Statements and Loops.srt |
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| 6. Creating a Data Provider.mp4 |
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| 6. Creating a Data Provider.srt |
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| 6. Early Stopping or When to Stop Training.mp4 |
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| 6. Early Stopping or When to Stop Training.srt |
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| 6. Functions Containing a Few Arguments.mp4 |
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| 6. Functions Containing a Few Arguments.srt |
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| 6. How to Reassign Values.html |
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| 6. Linear Algebra and Geometry.html |
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| 6. OLS Assumptions.mp4 |
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| 6. Python Packages Installation.mp4 |
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| 6. Python Strings.html |
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| 6. Real Life Examples of Big Data.mp4 |
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| 6. Real Life Examples of Big Data.srt |
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| 6. Student's T Distribution.html |
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| 6. The Standard Normal Distribution.mp4 |
22.51MB |
| 6. The Standard Normal Distribution.srt |
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| 6. To Standardize or to not Standardize.mp4 |
30.11MB |
| 6. To Standardize or to not Standardize.srt |
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| 6. Tuples.mp4 |
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| 6. Type I Error and Type II Error.mp4 |
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| 6. Type I Error and Type II Error.srt |
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| 6. Types of Machine Learning.html |
161B |
| 6. Understanding Logistic Regression Tables.mp4 |
30.55MB |
| 6. Understanding Logistic Regression Tables.srt |
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| 6. What is the Standard Library.html |
161B |
| 6. Why Jupyter.html |
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| 7.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx |
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| 7.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx |
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| 7.1 3.4. Standard normal distribution_exercise.xlsx.xlsx |
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| 7.1 365_DataScience_Diagram.pdf.pdf |
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| 7.1 Add Comments - Resources.html |
134B |
| 7.1 All In - Conditional Statements, Functions, and Loops - Resources.html |
134B |
| 7.1 Arrays in Python Notebook.html |
181B |
| 7.1 Basic NN Example with TensorFlow (Part 3).html |
154B |
| 7.1 Dictionaries - Resources.html |
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| 7.1 Notable Built-In Functions in Python - Resources.html |
134B |
| 7.1 Simple linear regression - Lecture.html |
134B |
| 7.1 TensorFlow Business Case Model Outline.html |
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| 7.1 TensorFlow MNIST Part 5 with Comments.html |
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| 7.2 3.11. The t-table.xlsx.xlsx |
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| 7.2 3.4. Standard normal distribution_exercise_solution.xlsx.xlsx |
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| 7.2 365_DataScience.png.png |
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| 7.2 Simple linear regression - Exercise.html |
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| 7. Adam (Adaptive Moment Estimation).mp4 |
22.36MB |
| 7. Adam (Adaptive Moment Estimation).srt |
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| 7. Add Comments.mp4 |
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| 7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 |
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| 7. Arrays in Python - A Convenient Way To Represent Matrices.srt |
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| 7. Backpropagation.mp4 |
34.95MB |
| 7. Backpropagation.srt |
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| 7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 |
32.51MB |
| 7. Basic NN Example with TF Loss Function and Gradient Descent.srt |
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| 7. Built-in Functions in Python.mp4 |
22.02MB |
| 7. Built-in Functions in Python.srt |
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| 7. Business Case Model Outline.mp4 |
53.13MB |
| 7. Business Case Model Outline.srt |
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| 7. Business Intelligence (BI) Techniques.mp4 |
89.94MB |
| 7. Business Intelligence (BI) Techniques.srt |
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7.57KB |
| 7. Conditional Statements, Functions, and Loops.mp4 |
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| 7. Confidence Intervals; Population Variance Unknown; t-score.mp4 |
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| 7. Confidence Intervals; Population Variance Unknown; t-score.srt |
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| 7. Continuing with BI, ML, and AI.mp4 |
108.98MB |
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| 7. Dictionaries.mp4 |
25.04MB |
| 7. Dictionaries.srt |
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| 7. First Regression in Python.mp4 |
44.57MB |
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| 7. Importing Modules in Python.mp4 |
19.93MB |
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| 7. Installing Python and Jupyter.mp4 |
54.41MB |
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| 7. MNIST Batching and Early Stopping.mp4 |
12.85MB |
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| 7. Numerical Variables - Frequency Distribution Table.mp4 |
25.98MB |
| 7. Numerical Variables - Frequency Distribution Table.srt |
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| 7. OLS Assumptions.html |
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| 7. Relationship between Clustering and Regression.mp4 |
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| 7. The Linear Model (Linear Algebraic Version).mp4 |
28.44MB |
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3.88KB |
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| 7. The Standard Normal Distribution Exercise.html |
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| 7. Type I Error and Type II Error.html |
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| 7. What do the Odds Actually Mean.mp4 |
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| 8.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx |
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| 8.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx |
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| 8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx |
10.96KB |
| 8.1 Basic NN Example with TensorFlow (Complete).html |
156B |
| 8.1 Binary predictors.html |
134B |
| 8.1 Iterating over Dictionaries - Resources.html |
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| 8.1 Market segmentation example.html |
134B |
| 8.1 Simple Linear Regression Exercise.html |
134B |
| 8.1 TensorFlow Business Case Optimization.html |
134B |
| 8.1 TensorFlow MNIST Part 6 with Comments.html |
159B |
| 8.1 Tensors Notebook.html |
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| 8.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx |
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| 8.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx |
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| 8. A1 Linearity.mp4 |
12.61MB |
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| 8. Add Comments.html |
161B |
| 8. Backpropagation picture.mp4 |
19.51MB |
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| 8. Basic NN Example with TF Model Output.mp4 |
37.39MB |
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6.87KB |
| 8. Binary Predictors in a Logistic Regression.mp4 |
38.43MB |
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| 8. Business Case Optimization.mp4 |
41.52MB |
| 8. Business Case Optimization.srt |
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| 8. Business Intelligence (BI) Techniques.html |
161B |
| 8. Central Limit Theorem.mp4 |
62.88MB |
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5.64KB |
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| 8. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html |
81B |
| 8. Continuing with BI, ML, and AI.html |
161B |
| 8. Dictionaries.html |
161B |
| 8. First Regression in Python Exercise.html |
76B |
| 8. How to Iterate over Dictionaries.mp4 |
16.98MB |
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3.88KB |
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3.34KB |
| 8. Importing Modules in Python.html |
161B |
| 8. Market Segmentation with Cluster Analysis (Part 1).mp4 |
43.01MB |
| 8. Market Segmentation with Cluster Analysis (Part 1).srt |
7.53KB |
| 8. Market Segmentation with Cluster Analysis (Part 1).vtt |
6.53KB |
| 8. MNIST Learning.mp4 |
46.69MB |
| 8. MNIST Learning.srt |
10.20KB |
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| 8. Numerical Variables Exercise.html |
81B |
| 8. Python Functions.html |
161B |
| 8. Test for the Mean. Population Variance Known.mp4 |
54.22MB |
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7.12KB |
| 8. The Linear Model.html |
161B |
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 |
13.79MB |
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| 8. What is a Tensor.mp4 |
22.53MB |
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| 9.1 2.5. The Histogram_lesson.xlsx.xlsx |
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| 9.1 365_DataScience.png.png |
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| 9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx |
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| 9.1 Accuracy.html |
134B |
| 9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf |
182.36KB |
| 9.1 Basic NN Example with TensorFlow Exercise 2.4 Solution.html |
162B |
| 9.1 Line Continuation - Resources.html |
134B |
| 9.1 Market segmentation example (Part 2).html |
134B |
| 9.1 TensorFlow Business Case Interpretation.html |
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| 9.1 TensorFlow MNIST Complete Code with Comments.html |
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| 9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx |
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| 9.3 Basic NN Example with TensorFlow (All Exercises).html |
154B |
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160B |
| 9.5 Basic NN Example with TensorFlow Exercise 2.2 Solution.html |
162B |
| 9.6 Basic NN Example with TensorFlow Exercise 4 Solution.html |
160B |
| 9.7 Basic NN Example with TensorFlow Exercise 1 Solution.html |
160B |
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162B |
| 9. A1 Linearity.html |
161B |
| 9. A Breakdown of our Data Science Infographic.mp4 |
67.74MB |
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4.45KB |
| 9. Backpropagation - A Peek into the Mathematics of Optimization.html |
539B |
| 9. Basic NN Example with TF Exercises.html |
1.59KB |
| 9. Business Case Interpretation.mp4 |
25.74MB |
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| 9. Calculating the Accuracy of the Model.mp4 |
32.85MB |
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4.13KB |
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3.63KB |
| 9. Central Limit Theorem.html |
161B |
| 9. Margin of Error.mp4 |
59.09MB |
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| 9. Market Segmentation with Cluster Analysis (Part 2).mp4 |
56.11MB |
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| 9. MNIST Results and Testing.mp4 |
62.77MB |
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| 9. Prerequisites for Coding in the Jupyter Notebooks.mp4 |
30.58MB |
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| 9. Real Life Examples of Business Intelligence (BI).mp4 |
29.54MB |
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2.13KB |
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1.89KB |
| 9. Test for the Mean. Population Variance Known Exercise.html |
81B |
| 9. The Histogram.mp4 |
13.78MB |
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| 9. The Linear Model with Multiple Inputs.mp4 |
25.11MB |
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3.10KB |
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2.74KB |
| 9. Understanding Line Continuation.mp4 |
2.35MB |
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12.24MB |
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1.30KB |
| 9. What is a Tensor.html |
161B |