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
This book takes you through the ML lifecycle.
- Before the project starts
- Data Collection
- Feature Engineering
on cheeky 275 pages of distilled insight.
Hands-on Machine Learning with Scikit-learn, Keras, and Tensorflow
by Aurelién Geron
On an end-to-end project, in detail, learn about:
- Decision Trees
- Random Forests
- Different Neural Nets
With all the highlights and pitfalls
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.
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 $$$