Introduction to Deep Learning with Keras

It covers regression, binary and multiclass classification, and a first look at deeper models, in a project-driven way. The reason it uses Keras matters: Keras is the high-level layer that lets you assemble and train a working network in a few readable lines, so you build intuition for how networks behave before wrestling with lower-level frameworks. Think of it as the bridge between 'I can run scikit-learn' and 'I can build a neural net from scratch'.

It suits people who already know some Python and ML and want to start actually building networks, and it's not for beginners to either or anyone after deep theory. Access is via DataCamp's subscription (roughly $14/month on the annual plan, first chapter free); the completion certificate documents the learning rather than certifying it. Keras remains a mainstream, beginner-friendly way into deep learning (as of 2026).

Introduction to Deep Learning with Keras moves from scikit-learn into neural networks: regression for predicting asteroid trajectories, binary classification for fake-bill detection, multiclass and multi-label problems, and a first look at autoencoders, CNNs, and LSTMs in the final chapter. It explicitly assumes you've completed Supervised Learning with scikit-learn first.