From ML Algorithms to GenAI & LLMs
Master ML Algorithms and Generative AI & LLMs with Python from scratch!: Master MAlgorithms and Generative AI & LLMs with Python from scratch!
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From ML Algorithms to GenAI & LLMs, Written by Aman Kharwal, founder of Statso.io, is the second edition of the book - Machine Learning Algorithms: Handbook. This book offers a comprehensive and expanded guide through the evolving world of machine learning and generative AI. Whether you are an experienced data scientist or just starting, this edition delivers practical insights and clear explanations of essential concepts like regression, classification, clustering, deep learning, and time series forecasting. This edition introduces two new chapters: "Mastering GenAI and LLMs" and "Understandi...
From ML Algorithms to GenAI & LLMs, Written by Aman Kharwal, founder of Statso.io, is the second edition of the book - Machine Learning Algorithms: Handbook. This book offers a comprehensive and expanded guide through the evolving world of machine learning and generative AI. Whether you are an experienced data scientist or just starting, this edition delivers practical insights and clear explanations of essential concepts like regression, classification, clustering, deep learning, and time series forecasting. This edition introduces two new chapters: "Mastering GenAI and LLMs" and "Understanding GANs for Generative AI with a Hands-on Project", which provide deep dives into large language models and generative adversarial networks (GANs). With hands-on Python code snippets and real-world project examples, the book bridges the gap between theory and application, offering you the tools to apply machine learning techniques effectively. Additional highlights include performance evaluation methods, data preprocessing techniques, feature engineering, and a quick reference appendix for tuning machine learning models. The book equips you with the necessary skills to navigate modern machine learning and AI, which makes it an essential resource for anyone interested in the field.
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