Biblioteca UNEATLANTICO

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Part I. The Fundamentals of Machine Learning -- Chapter 1. The Machine Learning Landscape -- Chapter 2. End-to-End Machine Learning Project -- Chapter 3. Classification -- Chapter 4. Training Models -- Chapter 5. Decision Trees -- Chapter 6. Ensemble Learning and Random Forests -- Chapter 7. Dimensionality Reduction -- Chapter 8. Unsupervised Learning Techniques -- Part II. Neural Networks and Deep Learning -- Chapter 9. Introduction to Artificial Neural Networks -- Chapter 10. Building Neural Networks with PyTorch -- Chapter 11. Training Deep Neural Networks -- Chapter 12. Deep Computer Vision Using Convolutional Neural Networks -- Chapter 13. Processing Sequences Using RNNs and CNNs -- Chapter 14. Natural Language Processing with RNNs and Attention -- Chapter 15. Transformers for Natural Language Processing and Chatbots -- Chapter 16. Vision and Multimodal Transformers -- Chapter 17. Speeding Up Transformers -- Chapter 18. Autoencoders, GANs, and Diffusion Models -- Chapter 19. Reinforcement Learning

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.

With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.

Understand ML basics, including concepts like overfitting and hyperparameter tuning
Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation
Learn techniques for unsupervised learning, such as clustering and anomaly detection
Build advanced architectures like transformers and diffusion models with PyTorch
Harness the power of pretrained models—including LLMs—and learn to fine-tune them
Train autonomous agents using reinforcement learning