Theobald Oliver. Machine Learning for Absolute Beginners

Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214.Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition.Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first.But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning.Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment — as a fully grown Simba looking over the Pride Lands of Africa — then this is the book to gently hoist you up and offer you a clear lay of the land.What Is Machine Learning?
ML Categories
The ML Toolbox
Data Scrubbing
Setting Up Your Data
Regression Analysis
Clustering
Bias & Variance
Artificial Neural Networks
Decision Trees
Ensemble Modeling
Building a Model in Python
Model Optimization
Further Resources
Downloading Datasets
Final Word