Grokking Machine Learning
Luis G. Serrano
  • MEAP began May 2019
  • Publication in Spring 2020 (estimated)
  • ISBN 9781617295911
  • 350 pages (estimated)
  • printed in black & white

Written in an approachable manner with great use of very illustrative and applicable examples.

Borko Djurkovic
It's time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine learning tools!
Table of Contents detailed table of contents

1 What is machine learning?

1.1 Why this book?

1.2 Is machine learning hard?

1.3 But what exactly is machine learning?

1.3.1 What is the difference between artificial intelligence and machine learning?

1.3.2 What about deep learning?

1.4 Humans use the remember-formulate-predict framework to make decisions (and so can machines!)

1.4.1 How do humans think?

1.4.2 How do machines think?

1.5 What is this book about?

1.6 Summary

2 Types of machine learning

2.1 What is the difference between labelled and unlabelled data?

2.2 What is supervised learning?

2.2.1 Regression models predict numbers

2.2.2 Classification models predict a state

2.3 What is unsupervised learning?

2.3.1 Clustering algorithms split a dataset into similar groups

2.3.2 Dimensionality reduction simplifies data without losing much information

2.3.3 Matrix factorization and other types of unsupervised learning

2.4 What is reinforcement learning?

2.5 Summary

3 Fitting a line close to our points: linear regression

4 Using lines to split our points: the perceptron algorithm

5 A probabilistic approach to splitting points: logistic regression

6 Combining building blocks to gain more power: neural networks

7 Finding the best line separation: neural networks

8 Iterating on a simple method to split data: decision trees

9 Using probability to its maximum: naive Bayes algorithm

10 Combining our models to maximize results: bagging and boosting

11 Putting it all together: evaluating and improving models

Appendixes

Appendix A: The math behind the algorithms

About the Technology

Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Whatever your field, knowledge of machine learning is becoming an essential skill. Python, along with its libraries like NumPy, Pandas, and scikit-learn, has become the go-to language for machine learning.

About the book

In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. You’ll only need high school math to dive into popular approaches and algorithms. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. When you’re done, you’ll have an intuitive understanding of the right approach for any machine learning task or project.

What's inside

  • Different types of machine learning, including supervised and unsupervised learning
  • Algorithms for simplifying, classifying, and splitting data
  • Machine learning packages and tools
  • Hands-on exercises with fully-explained Python code samples

About the reader

For readers with intermediate programming knowledge in Python or a similar language. No machine learning experience or advanced math skills necessary.

About the author

Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

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