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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [DesireCourse.Net].url |
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| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx |
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| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx |
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| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx |
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| 1.1 5 Files Needed to Deploy the Model.html |
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| 1.1 Absenteeism_data.csv |
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| 1.1 Absenteeism_preprocessed.csv |
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| 1.1 Arithmetic Operators - Resources.html |
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| 1.1 Audiobooks_data.csv |
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| 1.1 Bais NN Example Part 1.html |
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| 1.1 Business Case Exploring the Dataset.html |
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| 1.1 Comparison Operators - Resources.html |
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| 1.1 Course_Notes_Cluster_Analysis.pdf |
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| 1.1 Course_Notes_Logistic_Regression.pdf |
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| 1.1 Course notes_descriptive_statistics.pdf |
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| 1.1 Course notes_descriptive_statistics.pdf |
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| 1.1 Course notes_hypothesis_testing.pdf |
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| 1.1 Course notes_inferential statistics.pdf |
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| 1.1 Course notes_regression_analysis.pdf |
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| 1.1 Course notes_regression_analysis.pdf |
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| 1.1 Course Notes - Basic Probability.pdf |
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| 1.1 Course Notes - Bayesian Inference.pdf |
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| 1.1 Course Notes - Combinatorics.pdf |
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| 1.1 Course Notes - Probability Distributions.pdf |
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| 1.1 Course Notes - Section 2.pdf |
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| 1.1 Course Notes - Section 6.pdf |
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| 1.1 Defining a Function in Python - Resources.html |
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| 1.1 For Loops - Resources.html |
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| 1.1 Introduction to the If Statement - Resources.html |
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| 1.1 Lists - Resources.html |
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| 1.1 Probability in Finance Solutions.pdf |
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| 1.1 Shortcuts-for-Jupyter.pdf |
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| 1.1 sklearn - Linear Regression - Practical Example (Part 1).html |
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| 1.1 Variables - Resources.html |
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| 1.2 Audiobooks_data.csv |
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| 1.2 df_preprocessed.csv |
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| 1.2 Glossary.xlsx |
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| 1.2 Probability in Finance Homework.pdf |
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| 1.2 Python Introduction - Course Notes.pdf |
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| 1.2 Shortcuts-for-Jupyter.pdf |
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| 1.2 Statistics Glossary.xlsx |
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| 1.3 data_preprocessing_homework.pdf |
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| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
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| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt |
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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. Are You Sure You're All Set.html |
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| 1. Basic NN Example (Part 1).mp4 |
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| 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 |
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| 1. Business Case Exploring the Dataset and Identifying Predictors.srt |
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| 1. Business Case Getting Acquainted with the Dataset.mp4 |
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| 1. Comparison Operators.mp4 |
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| 1. Data Science and Business Buzzwords Why are there so Many.mp4 |
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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. EXERCISE - Age vs Probability.html |
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| 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. Fundamentals of Combinatorics.mp4 |
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| 1. Fundamentals of Probability Distributions.mp4 |
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| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
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| 1. How to Install TensorFlow 2.0.mp4 |
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| 1. Introduction.mp4 |
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| 1. Introduction to Cluster Analysis.mp4 |
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| 1. Introduction to Logistic Regression.mp4 |
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| 1. Introduction to Neural Networks.mp4 |
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| 1. Introduction to Programming.mp4 |
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| 1. Introduction to Regression Analysis.mp4 |
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| 1. K-Means Clustering.mp4 |
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| 1. Lists.mp4 |
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| 1. MNIST The Dataset.mp4 |
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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. Necessary Programming Languages and Software Used in Data Science.mp4 |
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| 1. Null vs Alternative Hypothesis.mp4 |
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| 1. Object Oriented Programming.mp4 |
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| 1. Population and Sample.mp4 |
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| 1. Practical Example Descriptive Statistics.mp4 |
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| 1. Practical Example Hypothesis Testing.mp4 |
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| 1. Practical Example Inferential Statistics.mp4 |
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| 1. Practical Example Linear Regression (Part 1).mp4 |
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| 1. Preprocessing Introduction.mp4 |
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| 1. Probability in Finance.mp4 |
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| 1. Probability in Finance.srt |
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| 1. READ ME!!!!.html |
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| 1. Sets and Events.mp4 |
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| 1. Stochastic Gradient Descent.mp4 |
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| 1. Summary on What You've Learned.mp4 |
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| 1. Techniques for Working with Traditional Data.mp4 |
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| 1. The Basic Probability Formula.mp4 |
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| 1. The IF Statement.mp4 |
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| 1. The Linear Regression Model.mp4 |
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| 1. The Linear Regression Model.srt |
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| 1. The Reason Behind These Disciplines.mp4 |
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| 1. Types of Clustering.mp4 |
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| 1. Types of Data.mp4 |
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| 1. Using Arithmetic Operators in Python.mp4 |
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| 1. Variables.mp4 |
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| 1. Variables.srt |
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| 1. What are Confidence Intervals.mp4 |
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| 1. What are Confidence Intervals.srt |
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| 1. What are Data, Servers, Clients, Requests, and Responses.mp4 |
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| 1. What is a Layer.mp4 |
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| 1. What is a Matrix.mp4 |
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| 1. What is Initialization.mp4 |
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| 1. What is Overfitting.mp4 |
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| 1. What is sklearn and How is it Different from Other Packages.mp4 |
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| 1. What to Expect from the Following Sections.html |
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| 1. What to Expect from this Part.mp4 |
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| 10.10 TensorFlow MNIST 'Time' Solution.html |
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| 10.11 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
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| 10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx |
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| 10.1 Addition and Subtraction of Matrices Python Notebook.html |
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| 10.1 Basic NN Example with TensorFlow Exercise 3 Solution.html |
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| 10.1 Binary predictors.html |
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| 10.1 Feature selection.html |
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| 10.1 Indexing Elements - Resources.html |
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| 10.1 MNIST Learning.html |
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| 10.1 Online p-value calculator.pdf |
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| 10.1 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
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| 10.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html |
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| 10.2 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
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| 10.3 Basic NN Example with TensorFlow (All Exercises).html |
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| 10.3 TensorFlow MNIST '3. Width and Depth' Solution.html |
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| 10.4 Basic NN Example with TensorFlow Exercise 2.3 Solution.html |
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| 10.4 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
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| 10.5 Basic NN Example with TensorFlow Exercise 1 Solution.html |
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| 10.5 TensorFlow MNIST '1. Width' Solution.html |
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| 10.6 Basic NN Example with TensorFlow Exercise 2.2 Solution.html |
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| 10.6 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
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| 10.7 Basic NN Example with TensorFlow Exercise 2.4 Solution.html |
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| 10.7 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
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| 10.8 Basic NN Example with TensorFlow Exercise 4 Solution.html |
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| 10.8 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
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| 10.9 TensorFlow MNIST '2. Depth' Solution.html |
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| 10. A1 Linearity.html |
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| 10. A Breakdown of our Data Science Infographic.html |
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| 10. Addition and Subtraction of Matrices.mp4 |
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| 10. Addition and Subtraction of Matrices.srt |
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| 10. Analyzing the Reasons for Absence.mp4 |
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| 10. Analyzing the Reasons for Absence.srt |
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| 10. Basic NN Example with TF Exercises.html |
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| 10. Binary Predictors in a Logistic Regression.mp4 |
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| 10. Binary Predictors in a Logistic Regression.srt |
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| 10. Business Case Testing the Model.mp4 |
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| 10. Business Case Testing the Model.srt |
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| 10. Central Limit Theorem.html |
165б |
| 10. Discrete Distributions The Bernoulli Distribution.html |
165б |
| 10. Feature Selection (F-regression).mp4 |
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| 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 |
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| 10. Interpreting the Coefficients of the Logistic Regression.srt |
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| 10. Jupyter's Interface.html |
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| 10. Margin of Error.mp4 |
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| 10. Margin of Error.srt |
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| 10. MNIST Learning.mp4 |
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| 10. MNIST Learning.srt |
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| 10. MNIST Solutions.html |
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| 10. Mutually Exclusive Sets.html |
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| 10. Numerical Variables Exercise.html |
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| 10. p-value.mp4 |
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| 10. p-value.srt |
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| 10. Relationship between Clustering and Regression.mp4 |
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| 10. Setting an Early Stopping Mechanism - Exercise.html |
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| 10. Software Integration - Explained.html |
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| 10. Solving Variations without Repetition.html |
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| 10. Techniques for Working with Traditional Methods.mp4 |
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| 10. Techniques for Working with Traditional Methods.srt |
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| 10. The Linear Model with Multiple Inputs.html |
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| 10. Using Seaborn for Graphs.mp4 |
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| 11.1 2.5. The Histogram_lesson.xlsx |
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| 11.1 Bank_data.csv |
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| 11.1 Business Case Testing the Model.html |
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| 11.1 Calculation of P-values.html |
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| 11.1 Combinations With Repetition.pdf |
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| 11.1 Logistic Regression prior to Backward Elimination.html |
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| 11.1 Market segmentation.html |
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| 11.1 MNIST - Exercises.html |
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| 11.1 Python Introduction - Course Notes.pdf |
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| 11.1 TensorFlow Business Case Homework.html |
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| 11.1 TensorFlow MNIST All Exercises.html |
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| 11.2 Binary predictors - exercise.html |
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| 11. A2 No Endogeneity.mp4 |
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| 11. Addition and Subtraction of Matrices.html |
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| 11. A Note on Calculation of P-values with sklearn.html |
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| 11. Backward Elimination or How to Simplify Your Model.mp4 |
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| 11. Backward Elimination or How to Simplify Your Model.srt |
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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 Testing the Model.mp4 |
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| 11. Dependence and Independence of Sets.mp4 |
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| 11. Discrete Distributions The Binomial Distribution.mp4 |
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| 11. How to Interpret the Regression Table.mp4 |
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| 11. Indexing Elements.html |
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| 11. Margin of Error.html |
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| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 |
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| 11. Market Segmentation with Cluster Analysis (Part 1).srt |
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| 11. MNIST Exercises.html |
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| 11. MNIST - Exercises.html |
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| 11. Obtaining Dummies from a Single Feature.mp4 |
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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. 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 |
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| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
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| 12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx |
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| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx |
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| 12.1 Accuracy.html |
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| 12.1 Business Case Final Exercise.html |
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| 12.1 Errors when Adding Matrices Python Notebook.html |
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| 12.1 Market segmentation.html |
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| 12.1 MNIST Testing the Model.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 |
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| 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. 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. Dependence and Independence of Sets.html |
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| 12. Discrete Distributions The Binomial Distribution.html |
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| 12. Errors when Adding Matrices.mp4 |
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| 12. EXERCISE - Obtaining Dummies from a Single Feature.html |
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| 12. How to Interpret the Regression Table.html |
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| 12. Market Segmentation with Cluster Analysis (Part 2).mp4 |
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| 12. MNIST Testing the Model.mp4 |
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| 12. Real Life Examples of Traditional Methods.mp4 |
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| 12. Solving Combinations.html |
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| 12. Standard Error.html |
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| 12. Structuring with Indentation.mp4 |
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| 12. Test for the Mean. Population Variance Unknown.mp4 |
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| 12. Testing the Model We Created.mp4 |
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| 12. Testing the Model We Created.srt |
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| 12. The Histogram.html |
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| 12. The Linear model with Multiple Inputs and Multiple Outputs.html |
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| 13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx |
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| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx |
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| 13.1 Bank_data.csv |
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| 13.1 Multiple linear regression - Exercise.html |
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| 13.1 Poisson - Expected Value and Variance.pdf |
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| 13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf |
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| 13.1 Symmetry Explained.pdf |
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| 13.1 Transpose of a Matrix Python Notebook.html |
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| 13.2 2.5.The-Histogram-exercise-solution.xlsx |
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| 13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx |
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| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx |
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| 13.2 Accuracy of the model - exercise.html |
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| 13.3 2.5.The-Histogram-exercise.xlsx |
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| 13. A3 Normality and Homoscedasticity.mp4 |
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| 13. Calculating the Accuracy of the Model.html |
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| 13. Confidence intervals. Two means. Dependent samples Exercise.html |
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| 13. Decomposition of Variability.mp4 |
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| 13. Discrete Distributions The Poisson Distribution.mp4 |
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| 13. Discrete Distributions The Poisson Distribution.srt |
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| 13. Estimators and Estimates.mp4 |
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