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Data Science is an expansive domain, often appearing as an intricate interplay of Computer Science, algorithms, data, business acumen, mathematics, and statistics. Navigating this complex landscape can be challenging, and keeping pace with its rapid evolution can be overwhelming. If you’re embarking on a Machine Learning journey, you might be daunted by the prospect of mastering all these individual areas: statistics, mathematics, visualizations, computer science, and various tools. This book systematically addresses these concerns, offering practical examples and frameworks to effectively tackle specific problems within Applied Machine Learning.