Ultimate Machine Learning Algorithms with Python

Ritesh Ratti

ISBN 10: 9349887320 / ISBN 13: 9789349887329

Published by Orange Education Pvt Ltd, 2026

About this Item

Ultimate Machine Learning Algorithms with Python | Ritesh Ratti | Taschenbuch | Englisch | 2026 | Orange Education Pvt Ltd | EAN 9789349887329 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 135496735

Bibliographic Details

• Title: Ultimate Machine Learning Algorithms with Python
• Publisher: Orange Education Pvt Ltd
• Publication Date: 2026
• Language: English
• ISBN 10: 9349887320
• ISBN 13: 9789349887329
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 20D millimeters 235 millimeters width by 191 millimeters height by 20 millimeters depth
• Weight: 701 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Learn the Algorithms Powering Modern AI. Build the Intelligence Behind Real-World Decisions.

Book Description

Ultimate Machine Learning Algorithms with Python bridges the gap between mathematical understanding and practical implementation, presenting every major algorithm with both theoretical rigour and plain-language intuition, so that readers at any level can build real-world competence.

You begin with supervised learning fundamentals - linear and logistic regression, decision trees, SVMs, and neural networks - before advancing to ensemble methods including Random Forests, XGBoost, and CatBoost. The book then moves into unsupervised learning through clustering, dimensionality reduction, and anomaly detection, with evaluation methods covered in depth for both paradigms. Every algorithm is grounded in a Python implementation using scikit-learn and industry-standard tooling.

What you will learn

● Apply supervised learning algorithms to regression and classification problems.

● Implement clustering and dimensionality reduction for unsupervised tasks.

● Build ensemble models using Random Forests, XGBoost, and CatBoost.

● Evaluate models using appropriate metrics for each algorithm type.

● Develop end-to-end projects in fraud detection and recommendation systems.

● Select, tune, and explain ML models for real business problems.

Table of Contents

1. Introduction to Machine Learning Algorithms

2. Regression Algorithms

3. Classification Algorithms

4. Ensembling Methods

5. Evaluation Methods for Supervised Learning Algorithms

6. Clustering Algorithms

7. Dimensionality Reduction

8. Evaluation Methods for Unsupervised Learning Algorithms

9. Building Recommender Systems

10. Building Anomaly Detection System

11. Building Spam Email Classification

12. Conclusion and Future Trends

Index