Grokking Machine Learning

Grokking Machine Learning

English | 2021 | ISBN: 978-1617295911 | 512 Pages | PDF | 21 MB

Discover valuable machine learning techniques you can understand and apply using just high-school math.

In Grokking Machine Learning you will learn:

  • Supervised algorithms for classifying and splitting data
  • Methods for cleaning and simplifying data
  • Machine learning packages and tools
  • Neural networks and ensemble methods for complex datasets

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 Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.

Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.

Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data.

What’s inside

  • Supervised algorithms for classifying and splitting data
  • Methods for cleaning and simplifying data
  • Machine learning packages and tools
  • Neural networks and ensemble methods for complex datasets
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