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| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1.1 1.04. Real-life example.csv |
219.83Кб |
| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx |
146.51Кб |
| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx |
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| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx |
51.90Кб |
| 1.1 Absenteeism_predictions.csv |
2.10Кб |
| 1.1 Absenteeism_preprocessed.csv |
29.13Кб |
| 1.1 Additional-Python-Tools-Solutions.ipynb |
25.49Кб |
| 1.1 Arithmetic Operators - Exercise_Py3.ipynb |
2.62Кб |
| 1.1 Audiobooks_data.csv |
710.77Кб |
| 1.1 Audiobooks_data.csv |
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| 1.1 Comparison Operators - Solution_Py3.ipynb |
2.41Кб |
| 1.1 Course_Notes_Cluster_Analysis.pdf |
208.65Кб |
| 1.1 Course_Notes_Logistic_Regression.pdf |
335.17Кб |
| 1.1 Course notes_descriptive_statistics.pdf |
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| 1.1 Course notes_hypothesis_testing.pdf |
656.44Кб |
| 1.1 Course notes_inferential statistics.pdf |
382.32Кб |
| 1.1 Course notes_regression_analysis.pdf |
312.18Кб |
| 1.1 Course notes_regression_analysis.pdf |
312.18Кб |
| 1.1 Course Notes - Basic Probability.pdf |
371.05Кб |
| 1.1 Course Notes - Bayesian Inference.pdf |
386.01Кб |
| 1.1 Course Notes - Combinatorics.pdf |
226.12Кб |
| 1.1 Course Notes - Probability Distributions.pdf |
463.95Кб |
| 1.1 Course Notes - Section 2.pdf |
578.08Кб |
| 1.1 Course Notes - Section 6.pdf |
936.42Кб |
| 1.1 data_preprocessing_homework.pdf |
134.47Кб |
| 1.1 Defining a Function in Python - Lecture_Py3.ipynb |
868б |
| 1.1 For Loops - Solution_Py3.ipynb |
1.80Кб |
| 1.1 Introduction to the If Statement - Lecture_Py3.ipynb |
1.14Кб |
| 1.1 Lists - Solution_Py3.ipynb |
3.18Кб |
| 1.1 Minimal_example_Part_1.ipynb |
1.19Кб |
| 1.1 model.original |
1.01Кб |
| 1.1 Probability in Finance Homework.pdf |
110.68Кб |
| 1.1 Shortcuts-for-Jupyter.pdf |
619.17Кб |
| 1.1 Statistics Glossary.xlsx |
20.26Кб |
| 1.1 Variables - Lecture_Py3.ipynb |
3.61Кб |
| 1.2 Absenteeism_data.csv |
32.05Кб |
| 1.2 Additional-Python-Tools-Exercises.ipynb |
11.37Кб |
| 1.2 Arithmetic Operators - Lecture_Py3.ipynb |
3.53Кб |
| 1.2 Comparison Operators - Lecture_Py3.ipynb |
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| 1.2 Course notes_descriptive_statistics.pdf |
482.21Кб |
| 1.2 For Loops - Exercise_Py3.ipynb |
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| 1.2 Glossary.xlsx |
19.97Кб |
| 1.2 Introduction to the If Statement - Solution_Py3.ipynb |
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| 1.2 Lists - Exercise_Py3.ipynb |
2.14Кб |
| 1.2 Probability in Finance Solutions.pdf |
184.46Кб |
| 1.2 scaler.original |
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| 1.2 Shortcuts-for-Jupyter.pdf |
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| 1.2 sklearn - Linear Regression - Practical Example (Part 1)_with_comments.ipynb |
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| 1.2 Variables - Exercise_Py3.ipynb |
2.23Кб |
| 1.3 absenteeism_module.py |
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| 1.3 Additional-Python-Tools-Lectures.ipynb |
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| 1.3 Arithmetic Operators - Solution_Py3.ipynb |
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| 1.3 Comparison Operators - Exercise_Py3.ipynb |
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| 1.3 df_preprocessed.csv |
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| 1.3 For Loops - Lecture_Py3.ipynb |
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| 1.3 Introduction to the If Statement - Exercise_Py3.ipynb |
1.53Кб |
| 1.3 Lists - Lecture_Py3.ipynb |
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| 1.3 Python Introduction - Course Notes.pdf |
2.04Мб |
| 1.3 sklearn - Linear Regression - Practical Example (Part 1).ipynb |
166.91Кб |
| 1.4 Absenteeism Exercise - Integration.ipynb |
62.35Кб |
| 1.4 Variables - Solution_Py3.ipynb |
3.79Кб |
| 1.5 Absenteeism_new_data.csv |
1.87Кб |
| 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. 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 |
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| 1. Are You Sure You're All Set.html |
519б |
| 1. Basic NN Example (Part 1).mp4 |
20.60Мб |
| 1. Basic NN Example (Part 1).srt |
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| 1. Bonus Lecture Next Steps.html |
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| 1. Business Case Exploring the Dataset and Identifying Predictors.mp4 |
66.27Мб |
| 1. Business Case Exploring the Dataset and Identifying Predictors.srt |
10.66Кб |
| 1. Business Case Getting Acquainted with the Dataset.mp4 |
87.65Мб |
| 1. Business Case Getting Acquainted with the Dataset.srt |
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| 1. Comparison Operators.mp4 |
10.17Мб |
| 1. Comparison Operators.srt |
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| 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 |
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| 1. Debunking Common Misconceptions.mp4 |
72.85Мб |
| 1. Debunking Common Misconceptions.srt |
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| 1. Defining a Function in Python.mp4 |
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| 1. Defining a Function in Python.srt |
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| 1. EXERCISE - Age vs Probability.html |
385б |
| 1. Exploring the Problem with a Machine Learning Mindset.mp4 |
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| 1. Exploring the Problem with a Machine Learning Mindset.srt |
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| 1. Finding the Job - What to Expect and What to Look for.mp4 |
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| 1. Finding the Job - What to Expect and What to Look for.srt |
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| 1. For Loops.mp4 |
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| 1. For Loops.srt |
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| 1. Fundamentals of Combinatorics.mp4 |
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| 1. Fundamentals of Combinatorics.srt |
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| 1. Fundamentals of Probability Distributions.mp4 |
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| 1. Fundamentals of Probability Distributions.srt |
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| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
52.31Мб |
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt |
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| 1. How to Install TensorFlow 2.0.mp4 |
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| 1. How to Install TensorFlow 2.0.srt |
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| 1. Introduction.mp4 |
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| 1. Introduction.srt |
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| 1. Introduction to Cluster Analysis.mp4 |
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| 1. Introduction to Cluster Analysis.srt |
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| 1. Introduction to Logistic Regression.mp4 |
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| 1. Introduction to Logistic Regression.srt |
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| 1. Introduction to Neural Networks.mp4 |
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| 1. Introduction to Neural Networks.srt |
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| 1. Introduction to Programming.mp4 |
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| 1. Introduction to Programming.srt |
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| 1. Introduction to Regression Analysis.mp4 |
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| 1. Introduction to Regression Analysis.srt |
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| 1. K-Means Clustering.mp4 |
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| 1. K-Means Clustering.srt |
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| 1. Lists.mp4 |
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| 1. Lists.srt |
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| 1. MNIST The Dataset.mp4 |
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| 1. MNIST The Dataset.srt |
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| 1. MNIST What is the MNIST Dataset.mp4 |
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| 1. MNIST What is the MNIST Dataset.srt |
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| 1. Multiple Linear Regression.mp4 |
21.53Мб |
| 1. Multiple Linear Regression.srt |
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| 1. Necessary Programming Languages and Software Used in Data Science.mp4 |
103.52Мб |
| 1. Necessary Programming Languages and Software Used in Data Science.srt |
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| 1. Null vs Alternative Hypothesis.mp4 |
92.04Мб |
| 1. Null vs Alternative Hypothesis.srt |
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| 1. Object Oriented Programming.mp4 |
33.59Мб |
| 1. Object Oriented Programming.srt |
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| 1. Population and Sample.mp4 |
58.11Мб |
| 1. Population and Sample.srt |
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| 1. Practical Example Descriptive Statistics.mp4 |
160.46Мб |
| 1. Practical Example Descriptive Statistics.srt |
20.78Кб |
| 1. Practical Example Hypothesis Testing.mp4 |
69.48Мб |
| 1. Practical Example Hypothesis Testing.srt |
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| 1. Practical Example Inferential Statistics.mp4 |
102.66Мб |
| 1. Practical Example Inferential Statistics.srt |
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| 1. Practical Example Linear Regression (Part 1).mp4 |
97.08Мб |
| 1. Practical Example Linear Regression (Part 1).srt |
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| 1. Preprocessing Introduction.mp4 |
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| 1. Preprocessing Introduction.srt |
3.87Кб |
| 1. Probability in Finance.mp4 |
99.06Мб |
| 1. Probability in Finance.srt |
9.83Кб |
| 1. READ ME!!!!.html |
564б |
| 1. Sets and Events.mp4 |
53.46Мб |
| 1. Sets and Events.srt |
5.06Кб |
| 1. Stochastic Gradient Descent.mp4 |
28.68Мб |
| 1. Stochastic Gradient Descent.srt |
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| 1. Summary on What You've Learned.mp4 |
39.75Мб |
| 1. Summary on What You've Learned.srt |
5.21Кб |
| 1. Techniques for Working with Traditional Data.mp4 |
138.30Мб |
| 1. Techniques for Working with Traditional Data.srt |
10.62Кб |
| 1. The Basic Probability Formula.mp4 |
85.91Мб |
| 1. The Basic Probability Formula.srt |
8.90Кб |
| 1. The IF Statement.mp4 |
10.81Мб |
| 1. The IF Statement.srt |
3.53Кб |
| 1. The Linear Regression Model.mp4 |
57.37Мб |
| 1. The Linear Regression Model.srt |
7.06Кб |
| 1. The Reason Behind These Disciplines.mp4 |
81.19Мб |
| 1. The Reason Behind These Disciplines.srt |
6.50Кб |
| 1. Types of Clustering.mp4 |
44.58Мб |
| 1. Types of Clustering.srt |
4.66Кб |
| 1. Types of Data.mp4 |
72.52Мб |
| 1. Types of Data.srt |
5.95Кб |
| 1. Using Arithmetic Operators in Python.mp4 |
18.93Мб |
| 1. Using Arithmetic Operators in Python.srt |
4.11Кб |
| 1. Using the .format() Method.mp4 |
47.64Мб |
| 1. Using the .format() Method.srt |
12.34Кб |
| 1. Variables.mp4 |
14.09Мб |
| 1. Variables.srt |
4.53Кб |
| 1. What are Confidence Intervals.mp4 |
49.99Мб |
| 1. What are Confidence Intervals.srt |
3.26Кб |
| 1. What are Data, Servers, Clients, Requests, and Responses.mp4 |
69.03Мб |
| 1. What are Data, Servers, Clients, Requests, and Responses.srt |
5.93Кб |
| 1. What is a Layer.mp4 |
12.51Мб |
| 1. What is a Layer.srt |
2.39Кб |
| 1. What is a Matrix.mp4 |
33.59Мб |
| 1. What is a Matrix.srt |
4.34Кб |
| 1. What is Initialization.mp4 |
21.76Мб |
| 1. What is Initialization.srt |
3.50Кб |
| 1. What is Overfitting.mp4 |
31.08Мб |
| 1. What is Overfitting.srt |
5.58Кб |
| 1. What is sklearn and How is it Different from Other Packages.mp4 |
27.26Мб |
| 1. What is sklearn and How is it Different from Other Packages.srt |
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| 1. What to Expect from the Following Sections.html |
2.48Кб |
| 1. What to Expect from this Part.mp4 |
31.10Мб |
| 1. What to Expect from this Part.srt |
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| 10.1 1.02. Multiple linear regression.csv |
1.07Кб |
| 10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx |
13.15Кб |
| 10.1 Adding and subtracting matrices.ipynb |
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| 10.1 Binary predictors.ipynb |
2.41Кб |
| 10.1 Indexing Elements - Exercise_Py3.ipynb |
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| 10.1 Online p-value calculator.pdf |
1.15Мб |
| 10.1 TensorFlow_Minimal_Example_Exercise_1_Solution.ipynb |
23.63Кб |
| 10.1 TensorFlow_MNIST_Exercises_All.ipynb |
15.47Кб |
| 10.1 TensorFlow_MNIST_Part6_with_comments.ipynb |
12.54Кб |
| 10.2 2.02. Binary predictors.csv |
2.56Кб |
| 10.2 Indexing Elements - Lecture_Py3.ipynb |
1.32Кб |
| 10.2 sklearn - Feature Selection with F-regression.ipynb |
10.44Кб |
| 10.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.ipynb |
49.96Кб |
| 10.3 Indexing Elements - Solution_Py3.ipynb |
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| 10.3 sklearn - Feature Selection with F-regression_with_comments.ipynb |
12.99Кб |
| 10.3 TensorFlow_Minimal_Example_All_Exercises.ipynb |
13.97Кб |
| 10.4 TensorFlow_Minimal_Example_Exercise_4_Solution.ipynb |
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| 10.5 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb |
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| 10.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb |
25.51Кб |
| 10.7 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb |
25.54Кб |
| 10.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.ipynb |
21.75Кб |
| 10. A1 Linearity.html |
165б |
| 10. A Breakdown of our Data Science Infographic.html |
165б |
| 10. Addition and Subtraction of Matrices.mp4 |
32.62Мб |
| 10. Addition and Subtraction of Matrices.srt |
4.04Кб |
| 10. Analyzing the Reasons for Absence.mp4 |
40.58Мб |
| 10. Analyzing the Reasons for Absence.srt |
5.85Кб |
| 10. Basic NN Example with TF Exercises.html |
1.59Кб |
| 10. Binary Predictors in a Logistic Regression.mp4 |
38.43Мб |
| 10. Binary Predictors in a Logistic Regression.srt |
5.41Кб |
| 10. Business Case Testing the Model.mp4 |
11.20Мб |
| 10. Business Case Testing the Model.srt |
2.71Кб |
| 10. Central Limit Theorem.html |
165б |
| 10. Discrete Distributions The Bernoulli Distribution.html |
165б |
| 10. Feature Selection (F-regression).mp4 |
29.51Мб |
| 10. Feature Selection (F-regression).srt |
6.67Кб |
| 10. Indexing Elements.mp4 |
5.93Мб |
| 10. Indexing Elements.srt |
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| 10. Interpreting the Coefficients of the Logistic Regression.mp4 |
40.40Мб |
| 10. Interpreting the Coefficients of the Logistic Regression.srt |
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| 10. Jupyter's Interface.html |
165б |
| 10. Margin of Error.mp4 |
47.24Мб |
| 10. Margin of Error.srt |
6.15Кб |
| 10. MNIST Exercises.html |
2.13Кб |
| 10. MNIST Learning.mp4 |
40.96Мб |
| 10. MNIST Learning.srt |
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| 10. Mutually Exclusive Sets.html |
165б |
| 10. Numerical Variables Exercise.html |
81б |
| 10. p-value.mp4 |
55.87Мб |
| 10. p-value.srt |
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| 10. Relationship between Clustering and Regression.mp4 |
9.93Мб |
| 10. Relationship between Clustering and Regression.srt |
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| 10. Setting an Early Stopping Mechanism - Exercise.html |
192б |
| 10. Software Integration - Explained.html |
165б |
| 10. Solving Variations without Repetition.html |
165б |
| 10. Techniques for Working with Traditional Methods.mp4 |
111.65Мб |
| 10. Techniques for Working with Traditional Methods.srt |
10.98Кб |
| 10. The Linear Model with Multiple Inputs.html |
165б |
| 10. Using Seaborn for Graphs.mp4 |
12.24Мб |
| 10. Using Seaborn for Graphs.srt |
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| 11.10 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb |
20.58Кб |
| 11.10 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb |
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| 11.11 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb |
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| 11.11 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb |
14.16Кб |
| 11.1 2.5. The Histogram_lesson.xlsx |
18.63Кб |
| 11.1 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb |
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| 11.1 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb |
14.07Кб |
| 11.1 Bank_data.csv |
19.55Кб |
| 11.1 Combinations With Repetition.pdf |
207.41Кб |
| 11.1 Logistic Regression prior to Backward Elimination.html |
226б |
| 11.1 Market segmentation example_with_comments.ipynb |
5.90Кб |
| 11.1 sklearn - How to properly include p-values.ipynb |
12.71Кб |
| 11.1 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb |
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| 11.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb |
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| 11.2 1.02. Multiple linear regression.csv |
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| 11.2 1. TensorFlow_MNIST_Width_Solution.ipynb |
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| 11.2 2. TensorFlow_MNIST_Depth_Solution.ipynb |
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| 11.2 3.12. Example.csv |
283б |
| 11.2 Binary Predictors in a Logistic Regression - Exercise.ipynb |
2.54Кб |
| 11.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb |
11.19Кб |
| 11.3 0. TensorFlow_MNIST_take_note_of_time_Solution.ipynb |
14.00Кб |
| 11.3 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb |
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| 11.3 Audiobooks_data.csv |
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| 11.3 Binary Predictors in a Logistic Regression - Solution.ipynb |
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| 11.3 Market segmentation example.ipynb |
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| 11.4 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb |
13.93Кб |
| 11.4 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb |
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| 11.5 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb |
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| 11.5 TensorFlow_MNIST_around_98_percent_accuracy.ipynb |
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| 11.6 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb |
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| 11.6 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb |
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| 11.7 1. TensorFlow_MNIST_Width_Solution.ipynb |
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| 11.7 TensorFlow_MNIST_All_Exercises.ipynb |
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| 11.8 2. TensorFlow_MNIST_Depth_Solution.ipynb |
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| 11.8 TensorFlow_MNIST_around_98_percent_accuracy.ipynb |
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| 11.9 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb |
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| 11.9 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb |
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| 11. A2 No Endogeneity.mp4 |
35.68Мб |
| 11. A2 No Endogeneity.srt |
5.24Кб |
| 11. Addition and Subtraction of Matrices.html |
165б |
| 11. A Note on Calculation of P-values with sklearn.html |
372б |
| 11. Backward Elimination or How to Simplify Your Model.mp4 |
39.56Мб |
| 11. Backward Elimination or How to Simplify Your Model.srt |
5.24Кб |
| 11. Binary Predictors in a Logistic Regression - Exercise.html |
87б |
| 11. Business Case A Comment on the Homework.mp4 |
36.38Мб |
| 11. Business Case A Comment on the Homework.srt |
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| 11. Business Case Testing the Model.mp4 |
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| 11. Business Case Testing the Model.srt |
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| 11. Dependence and Independence of Sets.mp4 |
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| 11. Dependence and Independence of Sets.srt |
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| 11. Discrete Distributions The Binomial Distribution.mp4 |
68.83Мб |
| 11. Discrete Distributions The Binomial Distribution.srt |
8.30Кб |
| 11. How to Interpret the Regression Table.mp4 |
44.64Мб |
| 11. How to Interpret the Regression Table.srt |
6.30Кб |
| 11. Indexing Elements.html |
165б |
| 11. Margin of Error.html |
165б |
| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 |
43.01Мб |
| 11. Market Segmentation with Cluster Analysis (Part 1).srt |
7.53Кб |
| 11. MNIST - Exercises.html |
1.98Кб |
| 11. MNIST Solutions.html |
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| 11. Obtaining Dummies from a Single Feature.mp4 |
81.12Мб |
| 11. Obtaining Dummies from a Single Feature.srt |
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| 11. p-value.html |
165б |
| 11. Solving Combinations.mp4 |
57.34Мб |
| 11. Solving Combinations.srt |
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| 11. Standard error.mp4 |
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| 11. Standard error.srt |
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| 11. Techniques for Working with Traditional Methods.html |
165б |
| 11. The Histogram.mp4 |
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| 11. The Histogram.srt |
3.01Кб |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.31Мб |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
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| 12.1 1.02. Multiple linear regression.csv |
1.07Кб |
| 12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx |
10.47Кб |
| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx |
14.54Кб |
| 12.1 Accuracy_with_comments.ipynb |
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| 12.1 Errors when adding scalars, vectors, and matrices in Python.ipynb |
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| 12.1 Market segmentation example_Part2.ipynb |
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| 7. Business Intelligence (BI) Techniques.mp4 |
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| 7. Categorical Variables Exercise.html |
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| 7. Communication between Software Products through Text Files.mp4 |
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| 7. Conditional Statements, Functions, and Loops.mp4 |
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| 7. Continuing with BI, ML, and AI.mp4 |
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| 7. Creating a Summary Table with the Coefficients and Intercept.mp4 |
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| 7. Dictionaries.mp4 |
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| 7. Discrete Distributions The Uniform Distribution.mp4 |
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| 7. Dropping a Column from a DataFrame in Python.mp4 |
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| 7. Dummy Variables - Exercise.html |
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| 7. How to Choose the Number of Clusters - Exercise.html |
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| 7. Importing Modules in Python.mp4 |
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| 7. The Standard Normal Distribution.html |
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| 7. Type I Error and Type II Error.html |
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| 7. Understanding Logistic Regression Tables.mp4 |
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| 8.1 12.8. TensorFlow_MNIST_with_comments_Part_6.ipynb |
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| 8.3 Iterating over Dictionaries - Solution_Py3.ipynb |
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| 8. Backpropagation Picture.mp4 |
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| 8. Business Intelligence (BI) Techniques.html |
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| 8. Confidence Intervals; Population Variance Unknown; T-score.mp4 |
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| 8. Dictionaries.html |
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| 8. EXERCISE - Dropping a Column from a DataFrame in Python.html |
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| 9.1 1.02. Multiple linear regression.csv |
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| 9. A Breakdown of our Data Science Infographic.mp4 |
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| 9. Backpropagation - A Peek into the Mathematics of Optimization.html |
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| 9. Basic NN Example with TF Model Output.mp4 |
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| 9. Business Case Interpretation.mp4 |
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| 9. Calculating the Adjusted R-Squared in sklearn - Exercise.html |
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| 9. Central Limit Theorem.mp4 |
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| 9. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html |
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| 9. First Regression in Python Exercise.html |
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| 9. Numerical Variables - Frequency Distribution Table.html |
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| 9. Real Life Examples of Business Intelligence (BI).mp4 |
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| 9. SOLUTION - Dropping a Column from a DataFrame in Python.html |
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| 9. Test for the Mean. Population Variance Known Exercise.html |
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| 9. The Linear Model with Multiple Inputs.mp4 |
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| 9. To Standardize or not to Standardize.mp4 |
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| 9. Understanding Line Continuation.mp4 |
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| 9. What is a Tensor.html |
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