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    Machine Learning Essentials You Always Wanted to Know

    Posted By: DexterDL
    Machine Learning Essentials You Always Wanted to Know

    Machine Learning Essentials You Always Wanted to Know (Self-Learning Management Series)
    English | 2025 | ISBN: 1636513794 | 290 pages | True EPUB | 9.95 MB


    A Beginner's Guide to Machine Learning with Hands-on Coding and Real-World Applications
    Covers key algorithms and techniques
    Ideal for students and professionals
    Hands-on implementation included
    Master the fundamentals of ML and take the first step towards a career in AI!

    In today’s rapidly evolving world, machine learning (ML) is no longer just for researchers or data scientists. From personalized recommendations on streaming platforms to fraud detection in banking, ML powers many aspects of our daily lives. As industries increasingly adopt AI-driven solutions, learning machine learning has become a valuable skill. Yet, many find the subject overwhelming, often intimidated by its mathematical complexity. That’s where Machine Learning Essentials You Always Wanted to Know (Machine Learning Essentials) comes in. This beginner-friendly guide offers a structured, step-by-step approach to understanding machine learning concepts without unnecessary jargon. Whether you are a student, a professional looking to transition into AI, or simply curious about how machines learn, this book provides a clear and practical roadmap to mastering ML.

    Authored by Dhairya Parikh, an experienced data engineer who returned to academia to refine his expertise, this book bridges the gap between theory and real-world application. It simplifies the core concepts of ML, breaking them down into digestible explanations paired with hands-on coding exercises to help you apply what you learn.

    What You’ll Learn:
    ,
    The fundamentals of machine learning and how it powers modern technology
    The three key types of ML—Supervised, Unsupervised, and Reinforcement Learning
    How to combine algorithms, data, and models to develop AI-driven solutions
    Practical coding techniques to build and implement machine learning models