Librería Samer Atenea
Kálamo Books
Librería Elías (Asturias)
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Machine learning has become a cornerstone of modern technology, driving innovations across industries such as healthcare, finance, energy, and automation. As organizations increasingly rely on Data-driven decision-making, a strong understanding of machine learning fundamentals is essential for both students and professionals to remain relevant and effective in their roles.This book provides a structured and intuitive learning experience through a Q&A format. It begins with foundational machine learning concepts, followed by detailed coverage of supervised and unsupervised learning techniques. The book explores key algorithms such as logistic regression, support vector machines (SVM), decision trees, and ensemble methods. It then progresses to advanced topics, including neural networks, dimensionality reduction, and modern trends like large language models (LLMs). Dedicated chapters on practical implementation using Python libraries, real-world applications, and interview-focused questions ensure a well-rounded understanding of both theory and practice.By the end of this book, readers will have developed strong conceptual clarity and practical insight into machine learning techniques. They will be equipped to apply these concepts in real-world scenarios, approach problems with confidence, and perform effectively in academic, research, or industry roles.WHAT YOU WILL LEARN● Learn supervised and unsupervised learning techniques with clarity.● Apply dimensionality reduction techniques for efficient data analysis.● Gain insights into neural networks and deep learning fundamentals.● Work with Python libraries for practical machine learning implementation.● Prepare effectively for interviews with structured Q&A practice.WHO THIS BOOK IS FORThis book is primarily intended for undergraduate students pursuing B.Tech, MCA, and related programs in computer science, artificial intelligence, and data science, as well as postgraduate students aiming to build strong fundamentals.
