Machine Learning Basics

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Machine Learning Basics: Building Intelligent Systems is a foundational guide to understanding the principles and practices of machine learning. It covers core concepts such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation. The book emphasizes practical applications, offering step-by-step explanations for implementing algorithms like decision trees, neural networks, and clustering methods. With a focus on problem-solving, it bridges theoretical knowledge and real-world use cases, making it an accessible resource for beginners and a solid refresher for experienced practitioners.