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Machine Learning Bookcamp

Build a portfolio of real-life projects
Alexey Grigorev
  • MEAP began January 2020
  • Publication in November 2021 (estimated)
  • ISBN 9781617296819
  • 475 pages (estimated)
  • printed in black & white
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An amazing introduction to learning machine learning by doing projects.

Joseph Perenia
Look inside
The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. By the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

about the technology

Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that’s exactly what you’ll be doing in Machine Learning Bookcamp.

about the book

In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.

what's inside

  • Code fundamental ML algorithms from scratch
  • Collect and clean data for training models
  • Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow
  • Apply ML to complex datasets with images and text
  • Deploy ML models to a production-ready environment

about the reader

For readers with existing programming skills. No previous machine learning experience required.

about the author

Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.

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This book not only explains how to use the different machine-learning algorithms with real-life examples but also provides very clear explanations on how to measure the quality of the predictors.

Monica Guimaraes

The greatest thing about the book - it provides hands-on experience. Step-by-step instructions are easy to follow and plenty of pictures instil a sense of confidence.

Ksenia Legostay

A step by step, comprehensive introduction to the world of machine learning using Python

Oliver Korten

I appreciate the author's approach to how ML can be useful or not. It does not try to sell you on the idea of why you should have ML constantly in use or use outright false claims.

Nathan Delboux

Machine Learning Bookcamp is a great resource to understand core ML topics, including associated concepts and means of implementation using contemporary libraries.

Dan Sheikh

An excellent approach to teaching ML by example, and a great book for people who want to begin / switch their careers to data science.

Amaresh Rajasekharan

This book breaks machine learning into manageable bite sized chunks, the concepts are super easy to digest.

Ben McNamara