Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch 1st Edition by Jason Brownlee B0DM1PTZB2

I. Introduction

1. Welcome

1.1 Audience

1.2 Algorithm Descriptions

1.3 Book Structure

1.4 What This Book is Not

1.5 How To Best Use this Book

1.6 Summary

II. Background

2. How to Talk About Data in Machine Learning

2.1 Data As You Know It

2.2 Statistical Learning Perspective

2.3 Computer Science Perspective

2.4 Models and Algorithms

2.5 Summary

3. Algorithms Learn a Mapping From Input to Output

3.1 Learning a Function

3.2 Learning a Function to Make Predictions

3.3 Techniques for Learning a Function

3.4 Summary

4. Parametric and Nonparametric Machine Learning Algorithms

4.1 Parametric Machine Learning Algorithms

4.2 Nonparametric Machine Learning Algorithms

4.3 Summary

5. Supervised, Unsupervised, and Semi-Supervised Learning

5.1 Supervised Machine Learning

5.2 Unsupervised Machine Learning

5.3 Semi-Supervised Machine Learning

5.4 Summary

6. The Bias-Variance Trade-Off

6.1 Overview of Bias and Variance

6.2 Bias Error

6.3 Variance Error

6.4 Bias-Variance Trade-Off

6.5 Summary

7. Overfitting and Underfitting

7.1 Generalization in Machine Learning

7.2 Statistical Fit

7.3 Overfitting in Machine Learning

7.4 Underfitting in Machine Learning

7.5 A Good Fit in Machine Learning

7.6 How to Limit Overfitting

7.7 Summary

III. Linear Algorithms

8. Crash-Course in Spreadsheet Math

8.1 Arithmetic

8.2 Statistical Summaries

8.3 Random Numbers

8.4 Flow Control

8.5 More Help

8.6 Summary

9. Gradient Descent for Machine Learning

9.1 Gradient Descent

9.2 Batch Gradient Descent

9.3 Stochastic Gradient Descent

9.4 Tips for Gradient Descent

9.5 Summary

10. Linear Regression

10.1 Isn’t Linear Regression from Statistics?

10.2 Many Names of Linear Regression

10.3 Linear Regression Model Representation

10.4 Linear Regression Learning the Model

10.5 Gradient Descent

10.6 Making Predictions with Linear Regression

10.7 Preparing Data for Linear Regression

10.8 Summary

11. Simple Linear Regression Tutorial

11.1 Tutorial Data Set

11.2 Simple Linear Regression

11.3 Making Predictions

11.4 Estimating Error

11.5 Shortcut

11.6 Summary

12. Linear Regression Tutorial Using Gradient Descent

12.1 Tutorial Data Set

12.2 Stochastic Gradient Descent

12.3 Simple Linear Regression with Stochastic Gradient Descent

12.4 Summary