Paolo Perrotta @nusco
edited by Katharine Dvorak @katied
Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don’t encounter in their regular work. The good news is that it doesn’t have to be that hard. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what’s really going on. Iterate on your design, and add layers of complexity as you go.
Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.
Start from the beginning and code your way to machine learning mastery.
"Let me say that I think this is a brilliant book. It takes the reader step by step through the thinking behind machine learning. Combine that with Paolo’s fun approach and this is the book I’d suggest every machine learning neophyte start with."
Russ Olsen, Author, Getting Clojure and Eloquent Ruby
- Full details: https://pragprog.com/book/pplearn/programming-machine-learning
- View this book’s portal and details on how to post errata and suggestions here.
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