دانلود کتاب یادگیری ماشین با پایتون

This book is intended to act as a thorough and practical reference to the quickly developing fields of Python-based machine learning, deep learning, natural language processing, and generative artificial intelligence. It seeks to give students a methodical manner to get both conceptual knowledge and hands-on experience in creating intelligent systems. Following a thorough explanation of model assessment metrics including Accuracy, Recall, F1-Score, and associated performance measures, the book methodically covers supervised and unsupervised learning approaches, starting with fundamental data preparation techniques. To guarantee that readers can successfully apply the principles given, emphasis is put on clarity, applicability, and hands-on learning. The investigation of neural network topologies, which serve as the basis for contemporary artificial intelligence, is a major theme of this book. The book offers a methodical and progressive treatment of every subject, from basic models like Perceptrons and Artificial Neural Networks to sophisticated architectures like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Gated Recurrent Units, Radial Basis Function Networks, and Deep Belief Networks. Additionally, popular ensemble methods like XGBoost and LightGBM are examined to emphasize their usefulness in tackling problems in the real world. The book delves deeper into the field of natural language processing, offering a concise and organized summary of text categorization, sentiment analysis, tokenization strategies, and NLP pipelines. It also exposes readers to recent developments in Large Language Models and Generative AI, including systems like LLaMA 2, PaLM 2, Turing-NLG, BLOOM, Gemini, Claude 3, DALL·E, and GPT-5, acknowledging the revolutionary potential of large-scale AI systems. In order to provide readers a comprehensive grasp of the ecosystem supporting contemporary AI applications, the book offers insights into specialized AI hardware in addition to software improvements, as demonstrated by platforms such as Cerebras CS-2. Finally, the book concludes by examining the role of Machine Learning and AI in Big Data environments, with applications spanning diverse domains such as agriculture, e-commerce, healthcare, education, and security analytics. This text book is intended for students, researchers, and professionals who seek a clear, structured, and application-oriented introduction to modern AI technologies through python programming.