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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 5 Files Needed to Deploy the Model.html |
134б |
1.1 Absenteeism_data.csv.csv |
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1.1 Absenteeism_preprocessed.csv.csv |
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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 |
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1.1 Course notes_descriptive_statistics.pdf.pdf |
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1.1 Course notes_hypothesis_testing.pdf.pdf |
648.20Кб |
1.1 Course notes_inferential statistics.pdf.pdf |
382.32Кб |
1.1 Course Notes - Basic Probability.pdf.pdf |
371.05Кб |
1.1 Course Notes - Bayesian Inference.pdf.pdf |
386.01Кб |
1.1 Course Notes - Combinatorics.pdf.pdf |
226.12Кб |
1.1 Course Notes - Probability Distributions.pdf.pdf |
456.24Кб |
1.1 Course Notes - Section 2.pdf.pdf |
578.08Кб |
1.1 Course Notes - Section 6.pdf.pdf |
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1.1 Defining a Function in Python - Resources.html |
134б |
1.1 For Loops - Resources.html |
134б |
1.1 Introduction to the If Statement - Resources.html |
134б |
1.1 Lists - Resources.html |
134б |
1.1 Python Introduction - Course Notes.pdf.pdf |
2.04Мб |
1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17Кб |
1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17Кб |
1.1 sklearn - Linear Regression - Practical Example (Part 1).html |
134б |
1.2 Bais NN Example Part 1.html |
136б |
1.2 df_preprocessed.csv.csv |
29.11Кб |
1.2 Statistics Glossary.xlsx.xlsx |
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1.2 Variables - Resources.html |
134б |
1.3 data_preprocessing_homework.pdf.pdf |
134.47Кб |
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
79.56Мб |
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt |
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1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt |
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1. A Practical Example What You Will Learn in This Course.mp4 |
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1. A Practical Example What You Will Learn in This Course.srt |
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1. A Practical Example What You Will Learn in This Course.vtt |
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1. Are You Sure You're All Set.html |
519б |
1. Basic NN Example (Part 1).mp4 |
20.59Мб |
1. Basic NN Example (Part 1).srt |
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1. Basic NN Example (Part 1).vtt |
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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 |
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1. Comparison Operators.srt |
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1. Data Science and Business Buzzwords Why are there so many.mp4 |
37.68Мб |
1. Data Science and Business Buzzwords Why are there so many.srt |
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1. Data Science and Business Buzzwords Why are there so many.vtt |
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1. Debunking Common Misconceptions.mp4 |
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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. Defining a Function in Python.vtt |
2.20Кб |
1. EXERCISE - Age vs Probability.html |
385б |
1. Exploring the Problem with a Machine Learning Mindset.mp4 |
27.54Мб |
1. Exploring the Problem with a Machine Learning Mindset.srt |
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1. Exploring the Problem with a Machine Learning Mindset.vtt |
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1. Finding the Job - What to Expect and What to Look for.mp4 |
35.79Мб |
1. Finding the Job - What to Expect and What to Look for.srt |
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1. Finding the Job - What to Expect and What to Look for.vtt |
3.94Кб |
1. For Loops.mp4 |
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1. For Loops.srt |
2.80Кб |
1. For Loops.vtt |
2.44Кб |
1. Fundamentals of Combinatorics.mp4 |
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1. Fundamentals of Probability Distributions.mp4 |
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1. Fundamentals of Probability Distributions.vtt |
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1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
52.30Мб |
1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt |
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1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt |
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1. How to Install TensorFlow.mp4 |
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1. How to Install TensorFlow.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 Cluster Analysis.vtt |
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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 Logistic Regression.vtt |
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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. K-Means Clustering.vtt |
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1. Lists.mp4 |
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1. Lists.srt |
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1. MNIST What is the MNIST Dataset.mp4 |
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1. Multiple Linear Regression.mp4 |
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1. Multiple Linear Regression.srt |
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1. Necessary Programming Languages and Software Used in Data Science.mp4 |
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1. Necessary Programming Languages and Software Used in Data Science.srt |
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1. Null vs Alternative Hypothesis.mp4 |
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1. Null vs Alternative Hypothesis.srt |
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1. Null vs Alternative Hypothesis.vtt |
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1. Object Oriented Programming.mp4 |
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1. Object Oriented Programming.srt |
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1. Population and Sample.mp4 |
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1. Population and Sample.srt |
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1. Population and Sample.vtt |
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1. Practical Example Descriptive Statistics.mp4 |
160.46Мб |
1. Practical Example Descriptive Statistics.srt |
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1. Practical Example Descriptive Statistics.vtt |
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1. Practical Example Hypothesis Testing.mp4 |
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1. Practical Example Hypothesis Testing.srt |
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1. Practical Example Hypothesis Testing.vtt |
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1. Practical Example Inferential Statistics.mp4 |
102.67Мб |
1. Practical Example Inferential Statistics.srt |
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1. Practical Example Inferential Statistics.vtt |
11.90Кб |
1. Practical Example Linear Regression (Part 1).mp4 |
97.09Мб |
1. Practical Example Linear Regression (Part 1).srt |
14.86Кб |
1. Practical Example Linear Regression (Part 1).vtt |
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1. Preprocessing Introduction.mp4 |
27.78Мб |
1. Preprocessing Introduction.srt |
3.87Кб |
1. Preprocessing Introduction.vtt |
3.39Кб |
1. Probability in Finance.mp4 |
99.07Мб |
1. Probability in Finance.srt |
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1. Probability in Finance.vtt |
8.71Кб |
1. Sets and Events.mp4 |
25.02Мб |
1. Sets and Events.srt |
5.06Кб |
1. Sets and Events.vtt |
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1. Stochastic Gradient Descent.mp4 |
28.69Мб |
1. Stochastic Gradient Descent.srt |
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1. Stochastic Gradient Descent.vtt |
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1. Summary on What You've Learned.mp4 |
39.76Мб |
1. Summary on What You've Learned.srt |
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1. Summary on What You've Learned.vtt |
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1. Techniques for Working with Traditional Data.mp4 |
79.85Мб |
1. Techniques for Working with Traditional Data.srt |
10.63Кб |
1. Techniques for Working with Traditional Data.vtt |
9.30Кб |
1. The Basic Probability Formula.mp4 |
44.02Мб |
1. The Basic Probability Formula.srt |
8.91Кб |
1. The Basic Probability Formula.vtt |
7.83Кб |
1. The IF Statement.mp4 |
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1. The Linear Regression Model.mp4 |
57.37Мб |
1. The Linear Regression Model.srt |
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6.14Кб |
1. The Reason behind these Disciplines.mp4 |
71.19Мб |
1. The Reason behind these Disciplines.srt |
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1. The Reason behind these Disciplines.vtt |
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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 |
72.53Мб |
1. Types of Data.srt |
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1. Types of Data.vtt |
5.25Кб |
1. Using Arithmetic Operators in Python.mp4 |
18.92Мб |
1. Using Arithmetic Operators in Python.srt |
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1. Using Arithmetic Operators in Python.vtt |
3.58Кб |
1. Variables.mp4 |
25.30Мб |
1. Variables.srt |
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1. Variables.vtt |
5.27Кб |
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 are Data, Servers, Clients, Requests, and Responses.mp4 |
69.04Мб |
1. What are Data, Servers, Clients, Requests, and Responses.srt |
5.94Кб |
1. What are Data, Servers, Clients, Requests, and Responses.vtt |
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1. What is a Layer.mp4 |
12.50Мб |
1. What is a Layer.srt |
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1. What is a Layer.vtt |
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1. What is a matrix.mp4 |
33.59Мб |
1. What is a matrix.srt |
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1. What is a matrix.vtt |
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 |
31.09Мб |
1. What is Overfitting.srt |
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1. What is Overfitting.vtt |
4.93Кб |
1. What is sklearn and How is it Different from Other Packages.mp4 |
27.25Мб |
1. What is sklearn and How is it Different from Other Packages.srt |
3.43Кб |
1. What is sklearn and How is it Different from Other Packages.vtt |
3.01Кб |
1. What to Expect from the Following Sections.html |
2.48Кб |
1. What to Expect from this Part.mp4 |
31.11Мб |
1. What to Expect from this Part.srt |
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1. What to Expect from this Part.vtt |
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10.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx |
13.15Кб |
10.1 Addition and Subtraction of Matrices Python Notebook.html |
178б |
10.1 Binary predictors.html |
134б |
10.1 Feature selection.html |
134б |
10.1 Indexing Elements - Resources.html |
134б |
10.1 Online p-value calculator.pdf.pdf |
1.15Мб |
10.1 TensorFlow MNIST All Exercises.html |
144б |
10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx |
11.75Кб |
10. A1 Linearity.html |
158б |
10. A Breakdown of our Data Science Infographic.html |
158б |
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. Analyzing the Reasons for Absence.mp4 |
40.57Мб |
10. Analyzing the Reasons for Absence.srt |
5.85Кб |
10. Analyzing the Reasons for Absence.vtt |
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10. Binary Predictors in a Logistic Regression.mp4 |
38.44Мб |
10. Binary Predictors in a Logistic Regression.srt |
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10. Binary Predictors in a Logistic Regression.vtt |
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10. Business Case Testing the Model.mp4 |
11.21Мб |
10. Business Case Testing the Model.srt |
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10. Business Case Testing the Model.vtt |
2.36Кб |
10. Central Limit Theorem.html |
158б |
10. Discrete Distributions The Bernoulli Distribution.html |
158б |
10. Feature Selection (F-regression).mp4 |
29.51Мб |
10. Feature Selection (F-regression).srt |
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10. Indexing Elements.mp4 |
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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. Interpreting the Coefficients of the Logistic Regression.vtt |
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10. Jupyter's Interface.html |
158б |
10. Margin of Error.mp4 |
59.16Мб |
10. Margin of Error.srt |
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10. Margin of Error.vtt |
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10. MNIST Exercises.html |
2.13Кб |
10. Mutually Exclusive Sets.html |
158б |
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 |
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10. Relationship between Clustering and Regression.srt |
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10. Relationship between Clustering and Regression.vtt |
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10. Software Integration - Explained.html |
158б |
10. Solving Variations without Repetition.html |
158б |
10. Techniques for Working with Traditional Methods.mp4 |
123.51Мб |
10. Techniques for Working with Traditional Methods.srt |
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10. Techniques for Working with Traditional Methods.vtt |
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10. The Linear Model with Multiple Inputs.html |
158б |
10. Using Seaborn for Graphs.mp4 |
12.25Мб |
10. Using Seaborn for Graphs.srt |
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11.10 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
165б |
11.11 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
165б |
11.1 2.5. The Histogram_lesson.xlsx.xlsx |
18.63Кб |
11.1 Binary predictors - exercise.html |
134б |
11.1 Calculation of P-values.html |
134б |
11.1 Combinations With Repetition.pdf.pdf |
207.41Кб |
11.1 Logistic Regression prior to Backward Elimination.html |
226б |
11.1 Market segmentation.html |
134б |
11.1 Python Introduction - Course Notes.pdf.pdf |
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11.1 TensorFlow Business Case Homework.html |
134б |
11.1 TensorFlow MNIST '3. Width and Depth' Solution.html |
160б |
11.2 Bank_data.csv.csv |
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11.2 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
157б |
11.3 TensorFlow MNIST '2. Depth' Solution.html |
150б |
11.4 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
162б |
11.5 TensorFlow MNIST 'Time' Solution.html |
162б |
11.6 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
162б |
11.7 TensorFlow MNIST '1. Width' Solution.html |
150б |
11.8 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
172б |
11.9 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
172б |
11. A2 No Endogeneity.mp4 |
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11. Addition and Subtraction of Matrices.html |
158б |
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 |
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11. Backward Elimination or How to Simplify Your Model.vtt |
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11. Binary Predictors in a Logistic Regression - Exercise.html |
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11. Business Case A Comment on the Homework.mp4 |
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11. Business Case A Comment on the Homework.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. Dependence and Independence of Sets.vtt |
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11. Discrete Distributions The Binomial Distribution.mp4 |
65.52Мб |
11. Discrete Distributions The Binomial Distribution.srt |
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11. Discrete Distributions The Binomial Distribution.vtt |
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11. How to Interpret the Regression Table.mp4 |
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11. How to Interpret the Regression Table.srt |
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11. How to Interpret the Regression Table.vtt |
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11. Indexing Elements.html |
158б |
11. Margin of Error.html |
158б |
11. Market Segmentation with Cluster Analysis (Part 1).mp4 |
43.01Мб |
11. Market Segmentation with Cluster Analysis (Part 1).srt |
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11. Market Segmentation with Cluster Analysis (Part 1).vtt |
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11. MNIST Solutions.html |
2.19Кб |
11. Obtaining Dummies from a Single Feature.mp4 |
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11. Obtaining Dummies from a Single Feature.srt |
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11. p-value.html |
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11. Python 2 vs Python 3.mp4 |
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11. Solving Combinations.mp4 |
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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 |
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11. The Histogram.mp4 |
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11. The Histogram.srt |
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11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.32Мб |
11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
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11. The Linear model with Multiple Inputs and Multiple Outputs.vtt |
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12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx |
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12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx |
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12.1 Accuracy.html |
134б |
12.1 Errors when Adding Matrices Python Notebook.html |
220б |
12.1 Market segmentation.html |
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12.1 Structure Your Code with Indentation - Resources.html |
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12.1 Summary table with p-values.html |
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12.1 TensorFlow Business Case Homework.html |
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12. A2 No Endogeneity.html |
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12. Business Case Final Exercise.html |
439б |
12. Calculating the Accuracy of the Model.mp4 |
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12. Confidence intervals. Two means. Dependent samples.mp4 |
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12. Confidence intervals. Two means. Dependent samples.srt |
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12. Confidence intervals. Two means. Dependent samples.vtt |
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12. Creating a Summary Table with p-values.mp4 |
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12. Creating a Summary Table with p-values.srt |
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12. Creating a Summary Table with p-values.vtt |
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12. Dependence and Independence of Sets.html |
158б |
12. Discrete Distributions The Binomial Distribution.html |
158б |
12. Errors when Adding Matrices.mp4 |
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12. Errors when Adding Matrices.srt |
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12. Errors when Adding Matrices.vtt |
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12. EXERCISE - Obtaining Dummies from a Single Feature.html |
129б |
12. How to Interpret the Regression Table.html |
158б |
12. Market Segmentation with Cluster Analysis (Part 2).mp4 |
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12. Market Segmentation with Cluster Analysis (Part 2).srt |
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12. Market Segmentation with Cluster Analysis (Part 2).vtt |
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12. Real Life Examples of Traditional Methods.mp4 |
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12. Real Life Examples of Traditional Methods.srt |
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12. Solving Combinations.html |
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12. Standard Error.html |
158б |
12. Structuring with Indentation.mp4 |
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12. Structuring with Indentation.srt |
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12. Test for the Mean. Population Variance Unknown.mp4 |
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