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| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx |
146.51Кб |
| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx |
1.74Мб |
| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx |
51.71Кб |
| 1.1 Arithmetic Operators - Resources.html |
134б |
| 1.1 Audiobooks_data.csv.csv |
710.77Кб |
| 1.1 Comparison Operators - Resources.html |
134б |
| 1.1 Course notes_descriptive_statistics.pdf.pdf |
482.27Кб |
| 1.1 Course notes_hypothesis_testing.pdf.pdf |
648.60Кб |
| 1.1 Course notes_inferential statistics.pdf.pdf |
382.32Кб |
| 1.1 Course Notes - Section 2.pdf.pdf |
927.67Кб |
| 1.1 Course Notes - Section 6.pdf.pdf |
936.42Кб |
| 1.1 Defining a Function in Python - Resources.html |
134б |
| 1.1 For Loops - Resources.html |
134б |
| 1.1 Glossary.xlsx.xlsx |
19.97Кб |
| 1.1 Introduction to the If Statement - Resources.html |
134б |
| 1.1 Lists - Resources.html |
134б |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17Кб |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17Кб |
| 1.1 Variables - Resources.html |
134б |
| 1.2 Bais NN Example Part 1.html |
136б |
| 1.2 Course notes_descriptive_statistics.pdf.pdf |
482.27Кб |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
126.87Мб |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt |
8.99Кб |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt |
7.90Кб |
| 1. A Practical Example What You Will Learn in This Course.mp4 |
49.03Мб |
| 1. A Practical Example What You Will Learn in This Course.srt |
6.37Кб |
| 1. A Practical Example What You Will Learn in This Course.vtt |
5.62Кб |
| 1. Basic NN Example (Part 1).mp4 |
20.60Мб |
| 1. Basic NN Example (Part 1).srt |
4.47Кб |
| 1. Basic NN Example (Part 1).vtt |
3.91Кб |
| 1. Business Case Getting acquainted with the dataset.mp4 |
87.66Мб |
| 1. Business Case Getting acquainted with the dataset.srt |
10.79Кб |
| 1. Business Case Getting acquainted with the dataset.vtt |
9.37Кб |
| 1. Comparison Operators.mp4 |
10.18Мб |
| 1. Comparison Operators.srt |
2.47Кб |
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2.14Кб |
| 1. Data Science and Business Buzzwords Why are there so many.mp4 |
81.41Мб |
| 1. Data Science and Business Buzzwords Why are there so many.srt |
6.63Кб |
| 1. Data Science and Business Buzzwords Why are there so many.vtt |
5.84Кб |
| 1. Debunking Common Misconceptions.mp4 |
72.85Мб |
| 1. Debunking Common Misconceptions.srt |
5.30Кб |
| 1. Debunking Common Misconceptions.vtt |
4.69Кб |
| 1. Defining a Function in Python.mp4 |
7.74Мб |
| 1. Defining a Function in Python.srt |
2.53Кб |
| 1. Defining a Function in Python.vtt |
2.20Кб |
| 1. Finding the Job - What to Expect and What to Look for.mp4 |
54.38Мб |
| 1. Finding the Job - What to Expect and What to Look for.srt |
4.50Кб |
| 1. Finding the Job - What to Expect and What to Look for.vtt |
3.94Кб |
| 1. For Loops.mp4 |
11.79Мб |
| 1. For Loops.srt |
2.80Кб |
| 1. For Loops.vtt |
2.44Кб |
| 1. How to Install TensorFlow.mp4 |
14.56Мб |
| 1. How to Install TensorFlow.srt |
3.22Кб |
| 1. How to Install TensorFlow.vtt |
2.84Кб |
| 1. Introduction.mp4 |
15.51Мб |
| 1. Introduction.srt |
1.63Кб |
| 1. Introduction.vtt |
1.44Кб |
| 1. Introduction to Cluster Analysis.mp4 |
53.42Мб |
| 1. Introduction to Cluster Analysis.srt |
4.80Кб |
| 1. Introduction to Cluster Analysis.vtt |
4.21Кб |
| 1. Introduction to Logistic Regression.mp4 |
27.07Мб |
| 1. Introduction to Logistic Regression.srt |
1.62Кб |
| 1. Introduction to Logistic Regression.vtt |
1.44Кб |
| 1. Introduction to Neural Networks.mp4 |
42.92Мб |
| 1. Introduction to Neural Networks.srt |
5.90Кб |
| 1. Introduction to Neural Networks.vtt |
5.18Кб |
| 1. Introduction to Programming.mp4 |
58.55Мб |
| 1. Introduction to Programming.srt |
6.91Кб |
| 1. Introduction to Programming.vtt |
6.08Кб |
| 1. Introduction to Regression Analysis.mp4 |
17.32Мб |
| 1. Introduction to Regression Analysis.srt |
2.21Кб |
| 1. Introduction to Regression Analysis.vtt |
1.95Кб |
| 1. K-Means Clustering.mp4 |
27.28Мб |
| 1. K-Means Clustering.srt |
6.67Кб |
| 1. K-Means Clustering.vtt |
5.76Кб |
| 1. Lists.mp4 |
22.00Мб |
| 1. Lists.srt |
4.99Кб |
| 1. Lists.vtt |
4.30Кб |
| 1. MNIST What is the MNIST Dataset.mp4 |
17.82Мб |
| 1. MNIST What is the MNIST Dataset.srt |
3.50Кб |
| 1. MNIST What is the MNIST Dataset.vtt |
3.07Кб |
| 1. Multiple Linear Regression.mp4 |
21.53Мб |
| 1. Multiple Linear Regression.srt |
3.35Кб |
| 1. Multiple Linear Regression.vtt |
2.93Кб |
| 1. Necessary Programming Languages and Software Used in Data Science.mp4 |
103.52Мб |
| 1. Necessary Programming Languages and Software Used in Data Science.srt |
7.30Кб |
| 1. Necessary Programming Languages and Software Used in Data Science.vtt |
6.42Кб |
| 1. Object Oriented Programming.mp4 |
33.59Мб |
| 1. Object Oriented Programming.srt |
6.10Кб |
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5.34Кб |
| 1. Population and Sample.mp4 |
58.11Мб |
| 1. Population and Sample.srt |
5.47Кб |
| 1. Population and Sample.vtt |
4.81Кб |
| 1. Practical Example Descriptive Statistics.mp4 |
159.46Мб |
| 1. Practical Example Descriptive Statistics.srt |
20.61Кб |
| 1. Practical Example Descriptive Statistics.vtt |
17.85Кб |
| 1. Practical Example Hypothesis Testing.mp4 |
69.48Мб |
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8.49Кб |
| 1. Practical Example Hypothesis Testing.vtt |
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| 1. Practical Example Inferential Statistics.mp4 |
102.67Мб |
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| 1. Preprocessing Introduction.mp4 |
27.78Мб |
| 1. Preprocessing Introduction.srt |
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3.39Кб |
| 1. Stochastic Gradient Descent.mp4 |
28.68Мб |
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| 1. Summary of What You Learned.mp4 |
39.76Мб |
| 1. Summary of What You Learned.srt |
5.22Кб |
| 1. Summary of What You Learned.vtt |
4.61Кб |
| 1. Techniques for Working with Traditional Data.mp4 |
138.30Мб |
| 1. Techniques for Working with Traditional Data.srt |
10.63Кб |
| 1. Techniques for Working with Traditional Data.vtt |
9.30Кб |
| 1. The IF Statement.mp4 |
13.63Мб |
| 1. The IF Statement.srt |
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| 1. The IF Statement.vtt |
3.12Кб |
| 1. The Linear Regression Model.mp4 |
57.37Мб |
| 1. The Linear Regression Model.srt |
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| 1. The Linear Regression Model.vtt |
6.14Кб |
| 1. The Null vs Alternative Hypothesis.mp4 |
92.12Мб |
| 1. The Null vs Alternative Hypothesis.srt |
7.36Кб |
| 1. The Null vs Alternative Hypothesis.vtt |
6.43Кб |
| 1. The Reason behind these Disciplines.mp4 |
81.19Мб |
| 1. The Reason behind these Disciplines.srt |
6.50Кб |
| 1. The Reason behind these Disciplines.vtt |
5.69Кб |
| 1. Types of Clustering.mp4 |
44.58Мб |
| 1. Types of Clustering.srt |
4.66Кб |
| 1. Types of Clustering.vtt |
4.12Кб |
| 1. Types of Data.mp4 |
72.52Мб |
| 1. Types of Data.srt |
5.96Кб |
| 1. Types of Data.vtt |
5.25Кб |
| 1. Using Arithmetic Operators in Python.mp4 |
18.92Мб |
| 1. Using Arithmetic Operators in Python.srt |
4.12Кб |
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3.58Кб |
| 1. Variables.mp4 |
26.61Мб |
| 1. Variables.srt |
6.18Кб |
| 1. Variables.vtt |
5.35Кб |
| 1. What are Confidence Intervals.mp4 |
49.98Мб |
| 1. What are Confidence Intervals.srt |
3.26Кб |
| 1. What are Confidence Intervals.vtt |
2.86Кб |
| 1. What is a Layer.mp4 |
12.50Мб |
| 1. What is a Layer.srt |
2.39Кб |
| 1. What is a Layer.vtt |
2.13Кб |
| 1. What is a matrix.mp4 |
33.59Мб |
| 1. What is a matrix.srt |
4.35Кб |
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3.80Кб |
| 1. What is Initialization.mp4 |
21.76Мб |
| 1. What is Initialization.srt |
3.51Кб |
| 1. What is Initialization.vtt |
3.09Кб |
| 1. What is Overfitting.mp4 |
31.08Мб |
| 1. What is Overfitting.srt |
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| 1. What is Overfitting.vtt |
4.93Кб |
| 1. What to Expect from this Part.mp4 |
31.10Мб |
| 1. What to Expect from this Part.srt |
4.63Кб |
| 1. What to Expect from this Part.vtt |
4.05Кб |
| 10.1 2.5.The-Histogram-exercise.xlsx.xlsx |
15.50Кб |
| 10.1 Addition and Subtraction of Matrices Python Notebook.html |
178б |
| 10.1 Indexing Elements - Resources.html |
134б |
| 10.1 Online p-value calculator.pdf.pdf |
1.22Мб |
| 10.1 TensorFlow MNIST All Exercises.html |
144б |
| 10.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx |
17.10Кб |
| 10. A2 No Endogeneity.mp4 |
35.67Мб |
| 10. A2 No Endogeneity.srt |
5.24Кб |
| 10. A2 No Endogeneity.vtt |
4.58Кб |
| 10. A Breakdown of our Data Science Infographic.html |
161б |
| 10. Addition and Subtraction of Matrices.mp4 |
32.62Мб |
| 10. Addition and Subtraction of Matrices.srt |
4.05Кб |
| 10. Addition and Subtraction of Matrices.vtt |
3.48Кб |
| 10. Business Case Testing the Model.mp4 |
11.20Мб |
| 10. Business Case Testing the Model.srt |
2.71Кб |
| 10. Business Case Testing the Model.vtt |
2.36Кб |
| 10. Histogram Exercise.html |
81б |
| 10. How is Clustering Useful.mp4 |
74.45Мб |
| 10. How is Clustering Useful.srt |
6.40Кб |
| 10. How is Clustering Useful.vtt |
5.65Кб |
| 10. How to Interpret the Regression Table.mp4 |
44.64Мб |
| 10. How to Interpret the Regression Table.srt |
6.31Кб |
| 10. How to Interpret the Regression Table.vtt |
5.50Кб |
| 10. Indexing Elements.mp4 |
5.94Мб |
| 10. Indexing Elements.srt |
1.71Кб |
| 10. Indexing Elements.vtt |
1.47Кб |
| 10. Jupyter's Interface.html |
161б |
| 10. Margin of Error.html |
161б |
| 10. MNIST Exercises.html |
2.13Кб |
| 10. p-value.mp4 |
55.87Мб |
| 10. p-value.srt |
5.04Кб |
| 10. p-value.vtt |
4.46Кб |
| 10. Standard error.mp4 |
22.77Мб |
| 10. Standard error.srt |
2.03Кб |
| 10. Standard error.vtt |
1.76Кб |
| 10. Techniques for Working with Traditional Methods.mp4 |
123.51Мб |
| 10. Techniques for Working with Traditional Methods.srt |
11.08Кб |
| 10. Techniques for Working with Traditional Methods.vtt |
9.66Кб |
| 10. The Linear Model with Multiple Inputs.html |
161б |
| 10. Underfitting and Overfitting.mp4 |
22.29Мб |
| 10. Underfitting and Overfitting.srt |
4.98Кб |
| 10. Underfitting and Overfitting.vtt |
4.37Кб |
| 11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
172б |
| 11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
157б |
| 11.1 2.6. Cross table and scatter plot.xlsx.xlsx |
26.12Кб |
| 11.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx |
10.47Кб |
| 11.1 TensorFlow Business Case Homework.html |
134б |
| 11.1 TensorFlow MNIST 'Time' Solution.html |
162б |
| 11.1 Test dataset.html |
134б |
| 11.2 TensorFlow MNIST '1. Width' Solution.html |
150б |
| 11.3 TensorFlow MNIST '3. Width and Depth' Solution.html |
160б |
| 11.4 TensorFlow MNIST '2. Depth' Solution.html |
150б |
| 11.5 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
165б |
| 11.6 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
165б |
| 11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
162б |
| 11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
172б |
| 11.9 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
162б |
| 11. A2 No Endogeneity.html |
161б |
| 11. Addition and Subtraction of Matrices.html |
161б |
| 11. Business Case A Comment on the Homework.mp4 |
36.38Мб |
| 11. Business Case A Comment on the Homework.srt |
5.30Кб |
| 11. Business Case A Comment on the Homework.vtt |
4.65Кб |
| 11. Confidence intervals. Two means. Dependent samples.mp4 |
70.47Мб |
| 11. Confidence intervals. Two means. Dependent samples.srt |
8.04Кб |
| 11. Confidence intervals. Two means. Dependent samples.vtt |
7.10Кб |
| 11. Cross Table and Scatter Plot.mp4 |
39.81Мб |
| 11. Cross Table and Scatter Plot.srt |
6.69Кб |
| 11. Cross Table and Scatter Plot.vtt |
5.87Кб |
| 11. Decomposition of Variability.mp4 |
49.66Мб |
| 11. Decomposition of Variability.srt |
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| 11. Decomposition of Variability.vtt |
3.67Кб |
| 11. Estimators and Estimates.mp4 |
47.83Мб |
| 11. Estimators and Estimates.srt |
3.72Кб |
| 11. Estimators and Estimates.vtt |
3.27Кб |
| 11. Indexing Elements.html |
161б |
| 11. MNIST Solutions.html |
2.19Кб |
| 11. p-value.html |
161б |
| 11. Techniques for Working with Traditional Methods.html |
161б |
| 11. Testing the Model.mp4 |
32.27Мб |
| 11. Testing the Model.srt |
6.55Кб |
| 11. Testing the Model.vtt |
5.70Кб |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.31Мб |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
5.47Кб |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.vtt |
4.79Кб |
| 12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx |
40.44Кб |
| 12.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx |
14.24Кб |
| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx |
14.54Кб |
| 12.1 Errors when Adding Matrices Python Notebook.html |
220б |
| 12.1 Structure Your Code with Indentation - Resources.html |
134б |
| 12.1 TensorFlow Business Case Homework.html |
134б |
| 12.2 2.6. Cross table and scatter plot_exercise.xlsx.xlsx |
16.28Кб |
| 12.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx |
13.74Кб |
| 12. A3 Normality and Homoscedasticity.mp4 |
42.70Мб |
| 12. A3 Normality and Homoscedasticity.srt |
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| 12. A3 Normality and Homoscedasticity.vtt |
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| 12. Business Case Final Exercise.html |
439б |
| 12. Confidence intervals. Two means. Dependent samples Exercise.html |
81б |
| 12. Cross Tables and Scatter Plots Exercise.html |
81б |
| 12. Decomposition of Variability.html |
161б |
| 12. Errors when Adding Matrices.mp4 |
11.18Мб |
| 12. Errors when Adding Matrices.srt |
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| 12. Errors when Adding Matrices.vtt |
2.27Кб |
| 12. Estimators and Estimates.html |
161б |
| 12. Real Life Examples of Traditional Methods.mp4 |
42.78Мб |
| 12. Real Life Examples of Traditional Methods.srt |
3.59Кб |
| 12. Real Life Examples of Traditional Methods.vtt |
3.14Кб |
| 12. Structuring with Indentation.mp4 |
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| 12. Structuring with Indentation.srt |
2.27Кб |
| 12. Structuring with Indentation.vtt |
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| 12. Test for the Mean. Population Variance Unknown.mp4 |
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| 12. Test for the Mean. Population Variance Unknown.srt |
5.90Кб |
| 12. Test for the Mean. Population Variance Unknown.vtt |
5.18Кб |
| 12. The Linear model with Multiple Inputs and Multiple Outputs.html |
161б |
| 13.1 2.7. Mean, median and mode_lesson.xlsx.xlsx |
10.49Кб |
| 13.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx |
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| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx |
11.85Кб |
| 13.1 Transpose of a Matrix Python Notebook.html |
167б |
| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx |
11.34Кб |
| 13. A4 No Autocorrelation.mp4 |
31.52Мб |
| 13. A4 No Autocorrelation.srt |
4.91Кб |
| 13. A4 No Autocorrelation.vtt |
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| 13. Confidence intervals. Two means. Independent samples (Part 1).mp4 |
28.75Мб |
| 13. Confidence intervals. Two means. Independent samples (Part 1).srt |
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| 13. Confidence intervals. Two means. Independent samples (Part 1).vtt |
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| 13. Graphical Representation of Simple Neural Networks.mp4 |
22.64Мб |
| 13. Graphical Representation of Simple Neural Networks.srt |
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| 13. Graphical Representation of Simple Neural Networks.vtt |
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| 13. Machine Learning (ML) Techniques.mp4 |
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| 13. Machine Learning (ML) Techniques.srt |
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| 13. Machine Learning (ML) Techniques.vtt |
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| 13. Mean, median and mode.mp4 |
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| 13. Mean, median and mode.srt |
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| 13. Mean, median and mode.vtt |
5.00Кб |
| 13. Structuring with Indentation.html |
161б |
| 13. Test for the Mean. Population Variance Unknown Exercise.html |
81б |
| 13. Transpose of a Matrix.mp4 |
38.07Мб |
| 13. Transpose of a Matrix.srt |
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| 13. What is the OLS.mp4 |
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| 13. What is the OLS.srt |
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| 13. What is the OLS.vtt |
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| 14.1 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx |
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| 14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx |
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| 14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx |
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| 14.1 Dot Product Python Notebook.html |
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| 14.2 2.7. Mean, median and mode_exercise.xlsx.xlsx |
10.87Кб |
| 14.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx |
9.83Кб |
| 14. A4 No autocorrelation.html |
161б |
| 14. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html |
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| 14. Dot Product.mp4 |
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| 14. Graphical Representation of Simple Neural Networks.html |
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| 14. Machine Learning (ML) Techniques.html |
161б |
| 14. Mean, Median and Mode Exercise.html |
81б |
| 14. R-Squared.mp4 |
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| 14. R-Squared.srt |
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| 14. Test for the Mean. Dependent Samples.mp4 |
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| 14. Test for the Mean. Dependent Samples.srt |
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| 14. Test for the Mean. Dependent Samples.vtt |
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| 15.1 2.8. Skewness_lesson.xlsx.xlsx |
34.63Кб |
| 15.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx |
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| 15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx |
12.80Кб |
| 15.1 Dot Product of Matrices Python Notebook.html |
171б |
| 15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx |
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| 15. A5 No Multicollinearity.mp4 |
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| 15. A5 No Multicollinearity.srt |
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| 15. Confidence intervals. Two means. Independent samples (Part 2).mp4 |
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| 15. Confidence intervals. Two means. Independent samples (Part 2).srt |
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| 15. Confidence intervals. Two means. Independent samples (Part 2).vtt |
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| 15. Dot Product of Matrices.mp4 |
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| 15. Dot Product of Matrices.srt |
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| 15. Dot Product of Matrices.vtt |
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| 15. R-Squared.html |
161б |
| 15. Skewness.mp4 |
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| 15. Skewness.srt |
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| 15. Skewness.vtt |
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| 15. Test for the Mean. Dependent Samples Exercise.html |
81б |
| 15. Types of Machine Learning.mp4 |
125.15Мб |
| 15. Types of Machine Learning.srt |
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| 15. Types of Machine Learning.vtt |
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| 9. Backpropagation - A Peek into the Mathematics of Optimization.html |
539б |
| 9. Basic NN Example with TF Exercises.html |
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| 9. Business Case Interpretation.mp4 |
25.74Мб |
| 9. Business Case Interpretation.srt |
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| 9. Business Case Interpretation.vtt |
2.60Кб |
| 9. Calculating the Accuracy of the Model.mp4 |
32.85Мб |
| 9. Calculating the Accuracy of the Model.srt |
4.13Кб |
| 9. Calculating the Accuracy of the Model.vtt |
3.63Кб |
| 9. Central Limit Theorem.html |
161б |
| 9. Margin of Error.mp4 |
59.09Мб |
| 9. Margin of Error.srt |
6.21Кб |
| 9. Margin of Error.vtt |
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| 9. Market Segmentation with Cluster Analysis (Part 2).mp4 |
56.11Мб |
| 9. Market Segmentation with Cluster Analysis (Part 2).srt |
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| 9. Market Segmentation with Cluster Analysis (Part 2).vtt |
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| 9. MNIST Results and Testing.mp4 |
62.77Мб |
| 9. MNIST Results and Testing.srt |
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| 9. MNIST Results and Testing.vtt |
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| 9. Prerequisites for Coding in the Jupyter Notebooks.mp4 |
30.58Мб |
| 9. Prerequisites for Coding in the Jupyter Notebooks.srt |
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| 9. Prerequisites for Coding in the Jupyter Notebooks.vtt |
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| 9. Real Life Examples of Business Intelligence (BI).mp4 |
29.54Мб |
| 9. Real Life Examples of Business Intelligence (BI).srt |
2.13Кб |
| 9. Real Life Examples of Business Intelligence (BI).vtt |
1.89Кб |
| 9. Test for the Mean. Population Variance Known Exercise.html |
81б |
| 9. The Histogram.mp4 |
13.78Мб |
| 9. The Histogram.srt |
3.01Кб |
| 9. The Histogram.vtt |
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| 9. The Linear Model with Multiple Inputs.mp4 |
25.11Мб |
| 9. The Linear Model with Multiple Inputs.srt |
3.10Кб |
| 9. The Linear Model with Multiple Inputs.vtt |
2.74Кб |
| 9. Understanding Line Continuation.mp4 |
2.35Мб |
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1.14Кб |
| 9. Understanding Line Continuation.vtt |
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| 9. Using Seaborn for Graphs.mp4 |
12.24Мб |
| 9. Using Seaborn for Graphs.srt |
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| 9. Using Seaborn for Graphs.vtt |
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| 9. What is a Tensor.html |
161б |