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244915866
Ultimate Machine Learning Algorithms with Python: Master Supervised, Unsupervised, Ensemble, and Deep Learning Models with Python, Scikit-Learn, Real
Lambar abu:
244915866
XOF 40990
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- 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 DescriptionUltimate 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 Contents1. Introduction to Machine Learning Algorithms2. Regression Algorithms3. Classification Algorithms4. Ensembling Methods5. Evaluation Methods for Supervised Learning Algorithms6. Clustering Algorithms7. Dimensionality Reduction8. Evaluation Methods for Unsupervised Learning Algorithms9. Building Recommender Systems10. Building Anomaly Detection System11. Building Spam Email Classification12. Conclusion and Future Trends Index
| Publisher | Orange Education Pvt Ltd |
| Publication date | May 23, 2026 |
| Language | English |
| Print length | 374 pages |
| ISBN-10 | 9349887169 |
| ISBN-13 | 978-9349887169 |
| Item Weight | 15.9 ounces (450.77 grams) |
| Dimensions | 7.5 x 0.85 x 9.25 inches (19.1 x 2.2 x 23.5 cm) |
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