Let Us Learn Machine Learning (Paperback)
Idioma: inglés
Editorial: Bpb Publications, 2026
- Tapa blanda
- Nuevo
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Vendedor de AbeBooks desde el 29 de junio de 2022
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EUR 42,49
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Paperback. 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, Naive 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 FORThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
N° de ref. del artículo 9788167080042
- Título
- Let Us Learn Machine Learning (Paperback)
- Autor
- Yashavant Kanetkar
- Editorial
- Bpb Publications
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 8167080048
- ISBN 13
- 9788167080042
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
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
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.
“Acerca de” puede pertenecer a otra edición de este título.
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