Do you learn best with a well-written book and want to dive into MLE?

Prepare for Machine Learning Engineering 101 Book Edition!

Check out these 3 that pack incredible value for little money.

Machine Learning Engineering

by Burkov

This book takes you through the ML lifecycle.

  1. Before the project starts
  2. Data Collection
  3. Feature Engineering
  4. Training
  5. Deployment

on cheeky 275 pages of distilled insight.

Pick up the book Machine learning Engineering!

Hands-on Machine Learning with Scikit-learn, Keras, and Tensorflow

by Aurelién Geron

On an end-to-end project, in detail, learn about:

  • SVMs
  • Decision Trees
  • Random Forests
  • Different Neural Nets

With all the highlights and pitfalls

Pick up the Hands-on Machine Learning book!

Python Machine Learning

by Sebastian Raschka

From preprocessing to building webapps.

From logistic regression to GANs and reinforcement learning.

These 725-ish pages pack a punch backed by Scikit-learn and Tensorflow 2.

Pick up the Python Machine Learning book!


We explored 3 excellent books for aspiring machine learning engineers.

  • Machine Learning Engineering – High-level concepts
  • Hands-on Machine Learning – Applied and project-based
  • Python Machine Learning – Spanning the whole ML landscape

So much value for such little $$$