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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 Bais NN Example Part 1.html |
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1.1 Business Case Exploring the Dataset.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 - Basic Probability.pdf |
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1.1 Course Notes - Bayesian Inference.pdf |
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1.1 Course Notes - Probability Distributions.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. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
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1. A Practical Example What You Will Learn in This Course.mp4 |
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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. 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 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. Finding the Job - What to Expect and What to Look for.mp4 |
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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 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. 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 Reason Behind These Disciplines.mp4 |
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1. Types of Clustering.mp4 |
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1. Using Arithmetic Operators in Python.mp4 |
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1. Variables.mp4 |
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1. What are Confidence Intervals.mp4 |
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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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10.6 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. 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. Analyzing the Reasons for Absence.mp4 |
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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. Business Case Testing the Model.mp4 |
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10. Central Limit Theorem.html |
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10. Discrete Distributions The Bernoulli Distribution.html |
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10. Feature Selection (F-regression).mp4 |
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10. Indexing Elements.mp4 |
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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. MNIST Learning.mp4 |
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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. 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. 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. 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. 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 Linear model with Multiple Inputs and Multiple Outputs.mp4 |
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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. Business Case Final Exercise.html |
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12. Calculating the Accuracy of the Model.mp4 |
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12. Calculating the Accuracy of the Model.srt |
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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. Errors when Adding Matrices.srt |
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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. Market Segmentation with Cluster Analysis (Part 2).srt |
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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. 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. A3 Normality and Homoscedasticity.srt |
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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. Decomposition of Variability.srt |
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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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13. Estimators and Estimates.srt |
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13. Graphical Representation of Simple Neural Networks.mp4 |
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13. Graphical Representation of Simple Neural Networks.srt |
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13. Histogram Exercise.html |
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13. How is Clustering Useful.mp4 |
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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. Multiple Linear Regression - Exercise.html |
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13. Saving the Model and Preparing it for Deployment.mp4 |
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13. Saving the Model and Preparing it for Deployment.srt |
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13. SOLUTION - Obtaining Dummies from a Single Feature.html |
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13. Structuring with Indentation.html |
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13. Symmetry of Combinations.mp4 |
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13. Symmetry of Combinations.srt |
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13. Test for the Mean. Population Variance Unknown Exercise.html |
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13. The Conditional Probability Formula.mp4 |
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13. The Conditional Probability Formula.srt |
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13. Transpose of a Matrix.mp4 |
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13. Transpose of a Matrix.srt |
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14.1 2.6. Cross table and scatter plot.xlsx |
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14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx |
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14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx |
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14.1 Dot Product Python Notebook.html |
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14.1 Exercise - part 1.html |
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14.1 Feature scaling.html |
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14.2 iris_dataset.csv |
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14. A4 No Autocorrelation.mp4 |
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14. A4 No Autocorrelation.srt |
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14. ARTICLE - A Note on 'pickling'.html |
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2. Introduction to Programming.html |
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7.1 Notable Built-In Functions in Python - Resources.html |
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7.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf |
289.12KB |
7.1 TensorFlow Business Case Model Outline.html |
134B |
7.1 TensorFlow MNIST Part 5 with Comments.html |
159B |
7.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx |
15.24KB |
7.2 365_DataScience_Diagram.pdf |
323.08KB |
7.2 How to choose the number of clusters.html |
134B |
7.2 Multiple Linear Regression with sklearn.html |
158B |
7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx |
41.11KB |
7.3 Multiple Linear Regression with sklearn with Comments.html |
172B |
7. Adam (Adaptive Moment Estimation).mp4 |
22.36MB |
7. Adam (Adaptive Moment Estimation).srt |
3.33KB |
7. Add Comments.mp4 |
11.27MB |
7. Add Comments.srt |
3.87KB |
7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 |
26.67MB |
7. Arrays in Python - A Convenient Way To Represent Matrices.srt |
6.13KB |
7. Backpropagation.mp4 |
34.95MB |
7. Backpropagation.srt |
4.46KB |
7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 |
38.50MB |
7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt |
7.36KB |
7. Built-in Functions in Python.mp4 |
22.02MB |
7. Built-in Functions in Python.srt |
4.21KB |
7. Business Case Load the Preprocessed Data - Exercise.html |
79B |
7. Business Case Model Outline.mp4 |
53.13MB |
7. Business Case Model Outline.srt |
6.94KB |
7. Business Intelligence (BI) Techniques.mp4 |
89.95MB |
7. Business Intelligence (BI) Techniques.srt |
8.63KB |
7. Categorical Variables Exercise.html |
81B |
7. Communication between Software Products through Text Files.mp4 |
60.34MB |
7. Communication between Software Products through Text Files.srt |
5.47KB |
7. Conditional Statements, Functions, and Loops.mp4 |
9.48MB |
7. Conditional Statements, Functions, and Loops.srt |
2.41KB |
7. Continuing with BI, ML, and AI.mp4 |
108.99MB |
7. Continuing with BI, ML, and AI.srt |
11.87KB |
7. Creating a Summary Table with the Coefficients and Intercept.mp4 |
38.88MB |
7. Creating a Summary Table with the Coefficients and Intercept.srt |
6.62KB |
7. Dictionaries.mp4 |
41.69MB |
7. Dictionaries.srt |
8.43KB |
7. Discrete Distributions The Uniform Distribution.mp4 |
24.40MB |
7. Discrete Distributions The Uniform Distribution.srt |
2.73KB |
7. Dropping a Column from a DataFrame in Python.mp4 |
61.77MB |
7. Dropping a Column from a DataFrame in Python.srt |
7.81KB |
7. Dummy Variables - Exercise.html |
713B |
7. Events and Their Complements.mp4 |
59.16MB |
7. Events and Their Complements.srt |
6.71KB |
7. How to Choose the Number of Clusters - Exercise.html |
87B |
7. Importing Modules in Python.mp4 |
19.94MB |
7. Importing Modules in Python.srt |
4.81KB |
7. Installing Python and Jupyter.mp4 |
51.00MB |
7. Installing Python and Jupyter.srt |
8.84KB |
7. Interpreting the Result and Extracting the Weights and Bias.mp4 |
30.28MB |
7. Interpreting the Result and Extracting the Weights and Bias.srt |
6.23KB |
7. MNIST Batching and Early Stopping.mp4 |
12.85MB |
7. MNIST Batching and Early Stopping.srt |
2.92KB |
7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html |
79B |
7. Multiple Linear Regression with sklearn.mp4 |
20.08MB |
7. Multiple Linear Regression with sklearn.srt |
4.17KB |
7. OLS Assumptions.mp4 |
21.86MB |
7. OLS Assumptions.srt |
3.03KB |
7. Python Packages Installation.mp4 |
40.59MB |
7. Python Packages Installation.srt |
5.62KB |
7. Solving Variations with Repetition.mp4 |
34.01MB |
7. Solving Variations with Repetition.srt |
3.47KB |
7. Student's T Distribution.html |
165B |
7. The Linear Model (Linear Algebraic Version).mp4 |
28.45MB |
7. The Linear Model (Linear Algebraic Version).srt |
3.88KB |
7. The Standard Normal Distribution.html |
165B |
7. Type I Error and Type II Error.html |
165B |
7. Understanding Logistic Regression Tables.mp4 |
30.56MB |
7. Understanding Logistic Regression Tables.srt |
5.56KB |
7. Union of Sets.mp4 |
57.20MB |
7. Union of Sets.srt |
5.53KB |
8.1 1.02. Multiple linear regression.csv |
1.07KB |
8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx |
11.44KB |
8.1 3.11. Population variance unknown, t-score_lesson.xlsx |
10.78KB |
8.1 3.4.Standard-normal-distribution-exercise-solution.xlsx |
24.04KB |
8.1 4.4. Test for the mean. Population variance known_lesson.xlsx |
10.96KB |
8.1 Bank_data.csv |
19.55KB |
8.1 Basic NN Example with TensorFlow (Part 3).html |
154B |
8.1 Business Case Learning and Interpreting.html |
134B |
8.1 Customizing a TensorFlow 2 Model.html |
134B |
8.1 First regression in Python.html |
134B |
8.1 Iterating over Dictionaries - Resources.html |
134B |
8.1 MNIST Outline the Model.html |
134B |
8.1 sklearn - Linear Regression - Practical Example (Part 5).html |
134B |
8.1 TensorFlow Business Case Optimization.html |
134B |
8.1 TensorFlow MNIST Part 6 with Comments.html |
159B |
8.1 Tensors Notebook.html |
148B |
8.2 3.11. The t-table.xlsx |
15.85KB |
8.2 3.4.Standard-normal-distribution-exercise.xlsx |
11.99KB |
8.2 Multiple Linear Regression and Adjusted R-squared.html |
187B |
8.2 Understanding logistic regression.html |
134B |
8.3 Multiple Linear Regression and Adjusted R-squared with Comments.html |
201B |
8. Add Comments.html |
165B |
8. Backpropagation Picture.mp4 |
19.50MB |
8. Backpropagation Picture.srt |
3.97KB |
8. Basic NN Example with TF Loss Function and Gradient Descent.mp4 |
32.52MB |
8. Basic NN Example with TF Loss Function and Gradient Descent.srt |
4.83KB |
8. Business Case Learning and Interpreting the Result.mp4 |
31.19MB |
8. Business Case Learning and Interpreting the Result.srt |
6.27KB |
8. Business Case Optimization.mp4 |
41.53MB |
8. Business Case Optimization.srt |
6.60KB |
8. Business Intelligence (BI) Techniques.html |
165B |
8. Calculating the Adjusted R-Squared in sklearn.mp4 |
30.88MB |
8. Calculating the Adjusted R-Squared in sklearn.srt |
6.27KB |
8. Communication between Software Products through Text Files.html |
165B |
8. Confidence Intervals; Population Variance Unknown; T-score.mp4 |
32.20MB |
8. Confidence Intervals; Population Variance Unknown; T-score.srt |
5.71KB |
8. Continuing with BI, ML, and AI.html |
165B |
8. Customizing a TensorFlow 2 Model.mp4 |
22.92MB |
8. Customizing a TensorFlow 2 Model.srt |
4.11KB |
8. Dictionaries.html |
165B |
8. Discrete Distributions The Uniform Distribution.html |
165B |
8. Events and Their Complements.html |
165B |
8. EXERCISE - Dropping a Column from a DataFrame in Python.html |
866B |
8. First Regression in Python.mp4 |
44.57MB |
8. First Regression in Python.srt |
7.91KB |
8. How to Iterate over Dictionaries.mp4 |
29.65MB |
8. How to Iterate over Dictionaries.srt |
7.93KB |
8. Importing Modules in Python.html |
165B |
8. Interpreting the Coefficients for Our Problem.mp4 |
52.38MB |
8. Interpreting the Coefficients for Our Problem.srt |
7.89KB |
8. MNIST Learning.mp4 |
46.69MB |
8. MNIST Learning.srt |
10.19KB |
8. MNIST Outline the Model.mp4 |
28.23MB |
8. MNIST Outline the Model.srt |
7.20KB |
8. Numerical Variables - Frequency Distribution Table.mp4 |
25.85MB |
8. Numerical Variables - Frequency Distribution Table.srt |
4.36KB |
8. OLS Assumptions.html |
165B |
8. Practical Example Linear Regression (Part 5).mp4 |
57.89MB |
8. Practical Example Linear Regression (Part 5).srt |
10.59KB |
8. Pros and Cons of K-Means Clustering.mp4 |
37.71MB |
8. Pros and Cons of K-Means Clustering.srt |
4.61KB |
8. Python Functions.html |
165B |
8. Solving Variations with Repetition.html |
165B |
8. Test for the Mean. Population Variance Known.mp4 |
54.23MB |
8. Test for the Mean. Population Variance Known.srt |
8.14KB |
8. The Linear Model.html |
165B |
8. The Standard Normal Distribution Exercise.html |
81B |
8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 |
13.80MB |
8. Understanding Jupyter's Interface - the Notebook Dashboard.srt |
3.73KB |
8. Understanding Logistic Regression Tables - Exercise.html |
87B |
8. Union of Sets.html |
165B |
8. What is a Tensor.mp4 |
22.53MB |
8. What is a Tensor.srt |
3.61KB |
9.1 3.11.The-t-table.xlsx |
15.85KB |
9.1 365_DataScience.png |
6.92MB |
9.1 4.4. Test for the mean. Population variance known_exercise.xlsx |
11.03KB |
9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf |
182.38KB |
9.1 Basic NN Example with TensorFlow (Complete).html |
156B |
9.1 Basic NN with TensorFlow.html |
134B |
9.1 Business Case Setting an Early Stopping Mechanism.html |
134B |
9.1 Calculating the Adjusted R-Squared.html |
134B |
9.1 First regression in Python - Exercise.html |
134B |
9.1 Line Continuation - Resources.html |
134B |
9.1 Logistic Regression prior to Custom Scaler.html |
219B |
9.1 MNIST Select the Loss and the Optimizer.html |
134B |
9.1 TensorFlow Business Case Interpretation.html |
134B |
9.1 TensorFlow MNIST Complete Code with Comments.html |
152B |
9.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx |
11.10KB |
9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx |
11.22KB |
9.3 3.11. Population variance unknown, t-score_exercise.xlsx |
10.62KB |
9. A1 Linearity.mp4 |
12.61MB |
9. A1 Linearity.srt |
2.36KB |
9. A Breakdown of our Data Science Infographic.mp4 |
67.75MB |
9. A Breakdown of our Data Science Infographic.srt |
5.10KB |
9. Backpropagation - A Peek into the Mathematics of Optimization.html |
539B |
9. Basic NN Example with TF Model Output.mp4 |
37.39MB |
9. Basic NN Example with TF Model Output.srt |
7.93KB |
9. Basic NN with TensorFlow Exercises.html |
1.29KB |
9. Business Case Interpretation.mp4 |
25.75MB |
9. Business Case Interpretation.srt |
2.94KB |
9. Business Case Setting an Early Stopping Mechanism.mp4 |
49.82MB |
9. Business Case Setting an Early Stopping Mechanism.srt |
7.82KB |
9. Calculating the Adjusted R-Squared in sklearn - Exercise.html |
76B |
9. Central Limit Theorem.mp4 |
62.89MB |
9. Central Limit Theorem.srt |
5.63KB |
9. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html |
81B |
9. Discrete Distributions The Bernoulli Distribution.mp4 |
34.14MB |
9. Discrete Distributions The Bernoulli Distribution.srt |
3.85KB |
9. First Regression in Python Exercise.html |
1.33KB |
9. Linear Regression - Exercise.html |
503B |
9. MNIST Results and Testing.mp4 |
62.78MB |
9. MNIST Results and Testing.srt |
8.17KB |
9. MNIST Select the Loss and the Optimizer.mp4 |
13.91MB |
9. MNIST Select the Loss and the Optimizer.srt |
3.02KB |
9. Mutually Exclusive Sets.mp4 |
25.39MB |
9. Mutually Exclusive Sets.srt |
2.52KB |
9. Numerical Variables - Frequency Distribution Table.html |
165B |
9. Prerequisites for Coding in the Jupyter Notebooks.mp4 |
30.59MB |
9. Prerequisites for Coding in the Jupyter Notebooks.srt |
7.79KB |
9. Real Life Examples of Business Intelligence (BI).mp4 |
29.54MB |
9. Real Life Examples of Business Intelligence (BI).srt |
2.13KB |
9. Software Integration - Explained.mp4 |
63.70MB |
9. Software Integration - Explained.srt |
6.71KB |
9. SOLUTION - Dropping a Column from a DataFrame in Python.html |
113B |
9. Solving Variations without Repetition.mp4 |
43.15MB |
9. Solving Variations without Repetition.srt |
4.53KB |
9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 |
41.19MB |
9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt |
5.03KB |
9. Test for the Mean. Population Variance Known Exercise.html |
81B |
9. The Linear Model with Multiple Inputs.mp4 |
25.12MB |
9. The Linear Model with Multiple Inputs.srt |
3.09KB |
9. To Standardize or not to Standardize.mp4 |
30.11MB |
9. To Standardize or not to Standardize.srt |
5.88KB |
9. Understanding Line Continuation.mp4 |
2.36MB |
9. Understanding Line Continuation.srt |
1.13KB |
9. What do the Odds Actually Mean.mp4 |
32.28MB |
9. What do the Odds Actually Mean.srt |
4.79KB |
9. What is a Tensor.html |
165B |