Python Machine Learning Projects

Livro digital

Título:
Python Machine Learning Projects

Autor:
Lisa Tagliaferri, Michelle Morales, Ellie Birbeck, Alvin Wan

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
Machine learning becomes much easier to understand when each concept produces something visible. After setting up an isolated Python environment and establishing the basic vocabulary of learning systems, this project-driven collection moves directly into a classifier built with scikit-learn, a TensorFlow neural network that recognizes handwritten digits, and a deep reinforcement learning bot trained for Atari through OpenAI Gym. The chapters connect preparation, model choice, training, evaluation, and iteration without hiding the code behind abstract promises. Readers see how supervised classification differs from neural-network recognition, why bias and variance matter, and how an agent learns from rewards and interaction. The projects also expose the practical rhythm of machine learning: prepare tools and data, build a baseline, observe behavior, diagnose limitations, and improve the result. Written by Lisa Tagliaferri, Michelle Morales, Ellie Birbeck, and Alvin Wan, the collection is especially useful for Python developers who want a concrete entrance into artificial intelligence. It does not ask readers to accept algorithms as magic; it gives them systems they can run, inspect, and modify. By the final project, the reader has crossed from terminology into working implementations and gained a foundation for choosing more ambitious machine learning problems.