{"product_id":"supervised-learning-algorithms-a-students-practical-guide-9798297784673","title":"Supervised learning Algorithms: A student's practical guide","description":"\u003cp\u003e • Author(s): Anshuman Mishra\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Algorithms\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eOverview: What This Book Offers\u003cbr\u003e\u003cb\u003e\"Supervised Learning Algorithms: A Student's Practical Guide\"\u003c\/b\u003e is designed to be a complete, student-friendly resource spanning from fundamentals to real-world applications. It blends solid theory, intuitive explanation, hands-on practice, and deep reflections on ethical and future directions in machine learning. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eWhy This Book Matters: Benefits of Study\u003ci\u003e1. \u003cb\u003eAccurate, Reliable Predictions\u003c\/b\u003e\u003c\/i\u003eSupervised learning trains models using labeled data, empowering them to make precise, dependable predictions once they're well-trained .\u003ci\u003e2. \u003cb\u003eClarity and Interpretability\u003c\/b\u003e\u003c\/i\u003eMany algorithms-like linear regression and decision trees-are transparent and easy to interpret. This clarity is especially vable in domains like education, healthcare, or finance, where understanding model decisions is critical ..\u003ci\u003e3. \u003cb\u003eWide-Ranging Applicability\u003c\/b\u003e\u003c\/i\u003eFrom discovering spam emails to forecasting housing prices, supervised learning spans classification and regression tasks and touches nearly every industry-healthcare, finance, tech, agriculture, climate science, robotics, and more .\u003ci\u003e4. \u003cb\u003ePractically Accessible for Students\u003c\/b\u003e\u003c\/i\u003eIts structured nature-train on labeled data, test, iterate-makes supervised learning ideal for learners. Simple yet powerful, it's accessible for beginners using intuitive models, and expandable toward advanced techniques like neural networks over time .\u003ci\u003e5. \u003cb\u003eEnhances Critical Thinking and Creativity\u003c\/b\u003e\u003c\/i\u003eWorking with real-world datasets and building predictive models fosters analytical thinking and creativity-skills highly valued in academics and industry .\u003ci\u003e6. \u003cb\u003eAutomates and Scales Tasks\u003c\/b\u003e\u003c\/i\u003eSupervised models can automate repetitive tasks-like grading assignments or detecting fraudulent transactions-freeing time and energy for more complex, human-driven work .\u003ci\u003e7. \u003cb\u003eSupports Active and Adaptive Learning\u003c\/b\u003e\u003c\/i\u003eEducational systems powered by supervised learning enable personalized instruction and adapt in real time-improving learning outcomes and engagement. \u003cp\u003e\u003c\/p\u003eHow This Book Facilitates Learning\u003ci\u003e\u003cb\u003eStructured Learning Approach\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eClear progression\u003c\/b\u003e from foundational math to advanced models, tuned with your preferred \"Chapter-X \/ X.X\" structure for clarity and easy navigation.\u003c\/li\u003e\n\u003cli\u003eFoundations (Chapters 1-2) ensure students build competency before diving deeper.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003e\u003cb\u003eBalanced Theory and Practical Application\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\n\u003cli\u003eEach concept is explained theoretically and reinforced with intuitive, accessible examples.\u003c\/li\u003e\n\u003cli\u003eHands-on Python exercises using libraries like scikit-learn make learning practical and code-first.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003e\u003cb\u003eAlgorithmic Breadth\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\n\u003cli\u003eCovers essential models: regression and classification basics, ensemble methods, boosting (XGBoost \/ LightGBM), and hyperparameter tuning.\u003c\/li\u003e\n\u003cli\u003eEncourages experimentation and a deep understanding of model behavior.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003e\u003cb\u003eModel Evation and Best Practices\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\n\u003cli\u003eTeaches rigorous evation using metrics (accuracy, precision, F1-score), validation strategies, ROC\/AUC understanding, and discussions on overfitting vs. generalization.\u003c\/li\u003e\n\u003cli\u003eAlso addresses ethical AI, model interpretability, and fairness concerns.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003e\u003cb\u003eCase Studies and Real Projects\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\n\u003cli\u003eIn-depth projects: housing price prediction, spam filtering, churn analysis, handwriting recognition.\u003c\/li\u003e\n\u003cli\u003eHands-on experience is supplemented with dataset access, real-world insights, and optional competitions or extension ideas.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003e\u003cb\u003eFuture-Focused Discussions\u003c\/b\u003e\u003c\/i\u003e\u003cul\u003e\u003cli\u003eCovers current trends and research, interpretability, ethical considerations, and explores how supervised learning fits into the evolving landscape of AI.\u003c\/li\u003e\u003c\/ul\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46333745365143,"sku":"9798297784673","price":1379.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798297784673.webp?v=1768670468","url":"https:\/\/atlanticbooks.com\/products\/supervised-learning-algorithms-a-students-practical-guide-9798297784673","provider":"Atlantic Books","version":"1.0","type":"link"}