Programming Machine Learning: From Coding to Deep Learning

A Practical Route from Code to Deep Learning

Paolo Perrotta wrote Programming Machine Learning for developers who want to understand machine learning by building it, not by wading through abstract math first. The book starts with simple, runnable programs and gradually raises the stakes, so concepts like linear regression, gradient descent, and classification become concrete tools you can see working in code.

Instead of treating machine learning as a black box, the book shows how each algorithm is assembled, trained, and tested. You move from your first learning program to image recognition, then onward to neural networks and the ideas behind deep learning. The emphasis stays on intuition and implementation, with just enough math to explain why the code behaves the way it does.

What You’ll Work Through 🧠

• How supervised learning differs from traditional programming
• Coding linear regression and adding a bias term
• Walking the gradient and tuning learning rates
• Working in higher dimensions with matrix math
• Building classifiers with sigmoid functions
• Training on real handwritten-digit data
• Assembling perceptrons into neural networks

Why Programmers Appreciate This Approach 💻

The book meets you where you are: a coder who wants to learn by doing. Each chapter pairs explanation with hands-on exercises, and the “What You Just Learned” sections help you consolidate new ideas before moving on. The tone is friendly and direct, without dumbing down the underlying concepts. If you’ve ever wanted to peek inside a machine learning model and understand what makes it tick, this is a guided tour that keeps the code front and center.

Who It’s For

This is a strong fit for software developers, students, and technical professionals who are new to machine learning and want a coding-first introduction. It’s also useful for readers who have tried math-heavy resources and need a more practical, incremental path into the subject. No prior machine learning experience is assumed, but comfort with programming is expected.

Build Your Foundation Step by Step ⚙️

By the end, you’ll have a clearer mental model of how learning systems are built from the ground up—and a solid base for exploring deep learning further. Whether you read cover to cover or work through the exercises at your own pace, the book is designed to turn curiosity into working knowledge.