Ultimate Machine Learning Algorithms with Python: Master Supervised, Unsupervised, Ensemble, and Deep Learning Models with Python, Scikit-Learn, Real ... and Production ML Workflows (English Edition) - Tapa blanda

Sinopsis

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

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Comprehensive ML algorithm coverage from mathematical foundations to deployment.
● Intuition-driven explanations paired with hands-on Python implementation.
● Guided capstone projects across fraud detection, anomaly, and recommendation systems.

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.

The final section puts theory into practice through guided projects — building a fraud detection system, a recommender engine, and a spam classifier — before closing with emerging trends and ethical considerations in ML. By the end of the book, you will be able to select the right algorithm for any problem, tune models for production performance, and communicate results clearly to technical and business stakeholders alike.

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

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