An Algorithmic Perspective Second Edition Chapman Hall Crc Machine Learning: A Comprehensive Exploration

Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

by Stephen Marsland

4.2 out of 5

Language	:	English
Paperback	:	186 pages
Item Weight	:	9.3 ounces
Dimensions	:	5 x 0.42 x 8 inches
File size	:	28211 KB
Screen Reader	:	Supported
Print length	:	457 pages
X-Ray for textbooks	:	Enabled

FREE

Machine learning has revolutionized various industries, from healthcare to finance, by enabling computers to learn from data without explicit programming. 'An Algorithmic Perspective Second Edition Chapman Hall Crc Machine Learning' provides a comprehensive overview of the field, delving into the core algorithms, techniques, and applications of machine learning. This book is an invaluable resource for students, researchers, and practitioners seeking to enhance their understanding of this rapidly evolving domain.

Key Features

This second edition of 'An Algorithmic Perspective' offers several key features that set it apart from other machine learning books:

• Algorithmic Focus: Unlike many machine learning books that emphasize high-level concepts, this book takes an algorithmic approach, providing a deep understanding of the underlying algorithms and their implementation details.
• Comprehensive Coverage: The book covers a wide range of machine learning topics, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.
• Practical Applications: The book includes numerous real-world examples and case studies that demonstrate the practical applications of machine learning in various domains.
• Hands-on Exercises: Each chapter features hands-on exercises that allow readers to apply the concepts and algorithms discussed in the chapter.

Target Audience

'An Algorithmic Perspective Second Edition Chapman Hall Crc Machine Learning' is designed for a diverse audience, including:

• Undergraduate and graduate students: The book serves as a comprehensive textbook for students pursuing courses in machine learning.
• Researchers: The book provides an in-depth exploration of machine learning algorithms and techniques, making it a valuable reference for researchers.
• Practitioners: The book offers practical insights and real-world examples, making it an essential guide for practitioners looking to implement machine learning solutions.

Detailed Chapter Summary

The book consists of 13 chapters, each covering a specific aspect of machine learning:

1. : Provides an overview of machine learning, its history, and its applications.
2. Linear Regression: Explores the fundamental concepts of linear regression, including model fitting, evaluation, and regularization.
3. Logistic Regression: Introduces logistic regression for binary classification problems, covering topics such as model fitting, evaluation, and overfitting.
4. Decision Trees: Discusses decision tree algorithms, including their construction, pruning, and ensemble methods.
5. Support Vector Machines: Explores support vector machines for both classification and regression tasks, emphasizing kernel functions and optimization techniques.
6. Unsupervised Learning: Covers unsupervised learning methods, such as clustering, dimensionality reduction, and anomaly detection.
7. Reinforcement Learning: Introduces reinforcement learning, including Markov decision processes, value functions, and policy optimization.
8. Deep Learning: Provides an overview of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
9. Ensemble Methods: Discusses ensemble methods, such as bagging, boosting, and random forests, highlighting their advantages and applications.
10. Feature Engineering: Explores the importance of feature engineering in machine learning, covering techniques for feature selection, transformation, and creation.
11. Model Evaluation and Selection: Provides guidance on model evaluation, selection, and hyperparameter tuning.
12. Machine Learning in Practice: Offers practical insights into implementing machine learning solutions in real-world scenarios.
13. Case Studies: Includes several case studies that demonstrate the applications of machine learning in various domains.

'An Algorithmic Perspective Second Edition Chapman Hall Crc Machine Learning' is an exceptional resource for anyone seeking a comprehensive understanding of machine learning algorithms, techniques, and applications. Its algorithmic focus, comprehensive coverage, practical examples, and hands-on exercises make it an invaluable guide for students, researchers, and practitioners alike. Whether you are new to machine learning or looking to deepen your knowledge, this book will provide you with the foundation and insights you need to succeed in this rapidly evolving field.

Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

by Stephen Marsland

4.2 out of 5

Language	:	English
Paperback	:	186 pages
Item Weight	:	9.3 ounces
Dimensions	:	5 x 0.42 x 8 inches
File size	:	28211 KB
Screen Reader	:	Supported
Print length	:	457 pages
X-Ray for textbooks	:	Enabled

FREE