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Statistical Techniques for Machine Learning by Ajit Singh
English | July 5, 2025 | ISBN: N/A | ASIN: B0FGW3TXRF | 221 pages | EPUB | 0.20 Mb
This textbook, "Statistical Techniques for Machine Learning," is born from a desire to bridge this gap. It is crafted specifically for the next generation of technologists-the B.Tech and M.Tech students who will build the future. Our philosophy is simple: to demystify the statistical principles that are the lifeblood of machine learning. We eschew overly dense mathematical notation in favor of intuitive explanations, practical examples, and a clear line of sight from statistical theory to real-world ML application.
Key Features of This Book:
1. NEP 2020 & AICTE Compliant: The structure and content directly support the policy's goals of fostering multidisciplinary skills, critical thinking, and experiential, problem-solving-based learning.
2. Logically Structured in 10 Chapters: Offers a complete and concise learning path covering the entire spectrum of statistical techniques relevant for a B.Tech/M.Tech curriculum, from fundamentals to advanced topics.
3. Practical-First Approach: Emphasizes learning through application. Theoretical concepts are immediately followed by simple, step-by-step practical examples that are easy to understand and replicate.
4. Conceptual Clarity over Rote Memorization: Focuses on building intuition behind the mathematics. It answers the "why" behind the formulas, enabling students to reason about model choice and behavior.
5. Balanced Mathematical Rigor: Presents necessary equations and derivations in an accessible manner, without overwhelming the reader, ensuring the book is approachable for students from diverse mathematical backgrounds.
6. Future-Ready Content: Includes dedicated sections and chapters on modern techniques such as ensemble methods (Gradient Boosting), Bayesian statistics, and the statistical underpinnings of deep learning, preparing students for both industry and research.
7. Globally Relevant: While rooted in the Indian educational framework, the topics and depth of coverage are benchmarked against the curricula of top international universities, ensuring students receive a world-class education.
The pedagogical approach is built on a progressive structure, starting with foundational chapters on probability and inference before moving to the core supervised and unsupervised learning paradigms. The book concludes with advanced topics, ensuring students are exposed to the state-of-the-art. Every chapter is infused with simple, practical examples, often using relatable datasets to illustrate complex ideas, making learning both effective and engaging.
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