Let Us Learn Machine Learning: The step-by-step Guide to Machine Learning (English Edition) - Softcover

Kanetkar, Yashavant; Singh, Nitish

Softcover

ISBN 10: 8167080048 ISBN 13: 9788167080042

Publisher: BPB Publications, 2026

Synopsis

Data is everywhere, but data by itself has little value unless we can learn from it. Let us Learn Machine Learning takes you on a step-by-step journey from raw data to intelligent predictions. Beginning with data preparation and exploration, the book explains how to build, evaluate, and improve machine learning models using techniques such as Linear Regression, Logistic Regression, SVM, KNN, Naive Bayes, Decision Trees, Bagging, Boosting, and more. Along the way, you will learn how to engineer features, select the right models, reduce dimensionality, and deal with real-world challenges such as overfitting and imbalanced datasets.

Written in a simple, practical style, this book focuses on developing intuition as much as technical skill, making machine learning accessible to students, developers, and professionals alike.

Each chapter contains:

● Lucid explanation of the concept.

● Well thought-out, fully working programming examples.

● End-of-chapter exercises to practice the skills learned in the chapter.

What you will learn

● Build a complete, end-to-end ML pipeline - ingest data from CSV, SQL, APIs, and web scraping; clean and preprocess it; and explore it through univariate, bivariate, and multivariate EDA.

● Engineer better features - apply encoding, feature scaling (standardization and normalization), transformations (log, square-root, Box-Cox), missing-value imputation, and outlier detection.

● Master the core supervised algorithms - Linear and Logistic Regression, SVM, KNN, Naïve Bayes, and Decision Trees, each built up from intuition to math to working Python code.

● Control overfitting and boost accuracy - understand the bias-variance trade-off, apply Ridge/Lasso/Elastic Net regularization, and combine models with bagging, Random Forests, and boosting (XGBoost, LightGBM, CatBoost).

● Evaluate, tune, and go beyond labels - choose the right metrics (precision, recall, F1, ROC-AUC, R²), use cross-validation and hyperparameter tuning while avoiding data leakage, and uncover hidden structure with dimensionality reduction (PCA) and clustering (K-Means, hierarchical).

Who this book is for

This book is for anyone beginning their machine learning journey - undergraduate and graduate students, software developers and engineers, data analysts, aspiring data scientists, and working professionals switching careers. If you know basic Python and high-school math and want to build real intuition alongside practical skills, this book is for you. No advanced mathematics required.

Table of Contents

1. Introduction To Machine Learning

2. End-to-End ML Project

3. Data Ingestion

4. Data Processing

5. Exploratory Data Analysis

6. Feature Engineering - I

7. Feature Engineering - II

8. Linear Models

9. Bias Variance Trade-off

10. Regularization

11. Logistic Regression

12. Model Evaluation

13. Support Vector Machine

14. K-Nearest Neighbor

15. Naïve Bayes Classifier

16. Decision Trees

17. Bagging And Boosting

18. Model Selection

19. Dimensionality Reduction

20. Clustering

About this title

About the Authors

Yashavant Kanetkar's programming books and online courses in C, C++, Java, Python and Data Structures have become a legend. He has created, molded and groomed millions of software careers in the last three decades. In recognition of his contribution to the software community, he has been conferred with "Distinguished Alumni Award" by IIT Kanpur.He has also received "Best .NET Technical Contributor Award" and "Most Valuable Professional Award" from Microsoft for five successive years.

Nitish Singh is an educator, entrepreneur, and the founder of CampusX, one of India's leading onlinelearning platforms for Data Science, Machine Learning, and Artificial Intelligence. Through his teaching, he has helped hundreds of thousands of learners build practical skills in Python, data analysis, machine learning, deep learning, and modern AI technologies.Known for his ability to simplify complex concepts and explain them through intuition, real-world examples, and hands-on implementation, Nitish has dedicated his career to making high-quality technical education accessible to everyone.

Edition details

• Publisher: BPB Publications
• Publication date: 2026
• Language: English
• ISBN 10: 8167080048
• ISBN 13: 9788167080042
• Binding: Paperback
• Number of pages: 574