Deep Learning in Python

DataCamp's "Introduction to Deep Learning in Python" is a solid but dated first taste of neural networks: worth four hours for a beginner inside a DataCamp subscription, but it teaches the older Keras 2.0 stack that DataCamp itself has since abandoned for PyTorch. It is a 4-hour, browser-based course by Dan Becker, a contributor to the Keras and TensorFlow libraries, that teaches you to build and tune neural networks for regression and classification using the Keras 2.0 functional API. It carries a strong 4.8/5 rating from 216 reviews on DataCamp's own platform and has been taken by roughly 263,000 learners, so its hands-on, in-browser coding format is well received for getting absolute beginners writing working neural-network code fast. The genuine catch is currency: the course teaches the standalone Keras 2.0 stack, and DataCamp has since rebuilt its flagship "Deep Learning in Python" skill track entirely around PyTorch, signalling this Keras course is now the older path rather than the current industry default. It is a good first taste of the deep-learning workflow (specify, compile, fit, predict) but it is shallow on theory and will not, on its own, prepare you for production or research work.

Worth it as a fast, beginner-friendly first exposure to neural networks in Keras for someone already inside a DataCamp subscription, but it is intentionally introductory (4 hours, Keras 2.0) and the framework it teaches is no longer DataCamp's own recommended track (now PyTorch), so it should be a starting point, not a standalone credential.

Best for: Beginners who can already do supervised machine learning in Python with scikit-learn and want a low-friction, hands-on introduction to how neural networks are specified, compiled, fitted and tuned in Keras; existing DataCamp subscribers wanting a quick 4-hour primer before deeper PyTorch or TensorFlow study.

Skip if: Complete Python beginners; anyone wanting deep mathematical understanding of backpropagation, CNNs, RNNs or transformers; learners targeting modern production/research workflows (where PyTorch now dominates, including DataCamp's own updated track); people who prefer building real end-to-end projects locally rather than guided in-browser exercises.