Grokking Machine Learning
Luis G. Serrano
  • MEAP began May 2019
  • Publication in Summer 2021 (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!

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 is a research scientist in quantum artificial intelligence at Zapata Computing. He has worked previously as a Machine Learning Engineer at Google, as a Lead Artificial Intelligence Educator at Apple, and as the Head of Content in Artificial Intelligence and Data Science at Udacity. Luis has a PhD in mathematics from the University of Michigan, a bachelor’s and master’s in mathematics from the University of Waterloo, and worked as a postdoctoral researcher at the Laboratoire de Combinatoire et d’Informatique Mathématique at the University of Quebec at Montreal. Luis maintains a popular YouTube channel about machine learning with over 75,000 subscribers and over 3 million views, and is a frequent speaker at artificial intelligence and data science conferences.

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