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Jason Brownlee
Master Machine Learning Algorithms
Discover How They Work and Implement Them From Scratch
i
Master Machine Learning Algorithms
©Copyright 2016 Jason Brownlee. All Rights Reserved.
Edition, v1.1
http://MachineLearningMastery.com
Contents
Preface iii
I Introduction
1 Welcome 2
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
ii
iii
5.3 Semi-Supervised Machine Learning
5.4 Summary
6 The Bias-Variance Trade-O
6.1 Overview of Bias and Variance
6.2 Bias Error
6.3 Variance Error
6.4 Bias-Variance Trade-O
6.5 Summary
7 Over tting and Under tting
7.1 Generalization in Machine Learning
7.2 Statistical Fit
7.3 Over tting in Machine Learning
7.4 Under tting in Machine Learning
7.5 A Good Fit in Machine Learning
7.6 How To Limit Over tting
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 Name
Master Machine Learning Algorithms - Discover how they work_Jason Brownlee.pdf