Ritesh ratti (10 resultados)

Condición: New.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. Learn the Algorithms Powering Modern AI. Build the Intelligence Behind Real-World Decisions.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.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 This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Learn the Algorithms Powering Modern AI. Build the Intelligence Behind Real-World Decisions.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.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 This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. 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.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn the Algorithms Powering Modern AI. Build the Intelligence Behind Real-World Decisions.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.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.…