Machine Learning
Price: 710.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780190127275
Publication date:
30/04/2021
Paperback
512 pages
Price: 710.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780190127275
Publication date:
30/04/2021
Paperback
512 pages
This book on Machine Learning is designed as a textbook for undergraduate and post-graduate students of engineering. It provides a comprehensive coverage of fundamentals of machine learning.
A perfect balance between theoretical and mathematical exposition is provided with several numerical examples, review questions, and Python programs. It will also be useful for engineering professionals and IT employees who want to learn the basics of the subject.
Rights: World Rights
Description
Spread over 16 chapters, the book starts with an overview of machine learning and discusses the need for understanding data and necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, and decision rule-based classification algorithms. The book provides an introduction to Bayesian learning and probabilistic graphical models. Important topics such as support vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, and genetic algorithms are discussed in depth. It ends with the latest developments in deep learning.
Table of contents
- Introduction to Machine Learning
- Understanding Data
- Basics of Learning Theory
- Similarity-based Learning
- Regression Analysis
- Decision Tree Learning
- Rule–based Learning
- Bayesian Learning
- Probabilistic Graphical Models
- Artificial Neural Networks
- Support Vector Machines
- Ensemble Learning
- Clustering Algorithms
- Reinforcement Learning
- Genetic Algorithms
- Deep Learning
Features
- Adopts an ‘Algorithmic Approach’ to illustrate the concepts of machine learning in a simple language with 100+ numerical problems
- Adapts ‘Minimal Mathematics Strategy’ with more emphasis on understanding the basics of machine learning
- Has ‘Comprehensive Coverage’ of all topics that are relevant to machine learning with 100+ figures and Python codes
- Provides ‘Simple Explanation’ to topics such as clustering, support vector machines, genetic algorithms, artificial neural networks, ensemble learning, and deep learning
- Contains ‘Appendices’ that discuss the basics of Python and Python packages such as NumPy, Pandas, Scikit-learn, Matplotlib, SciPy, and Keras
- Includes a ‘Laboratory Manual’ with examples illustrated through Python and its packages
- Comes with ‘Useful Pedagogical Features’ such as Crossword and Word Search
Online Resources
For faculty:
- Chapter PPTs
- Solution Manual
- Python Programs
- Lab Manual
- Crosswords and Word Search to understand the subject better
Description
Spread over 16 chapters, the book starts with an overview of machine learning and discusses the need for understanding data and necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, and decision rule-based classification algorithms. The book provides an introduction to Bayesian learning and probabilistic graphical models. Important topics such as support vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, and genetic algorithms are discussed in depth. It ends with the latest developments in deep learning.
Read MoreTable of contents
- Introduction to Machine Learning
- Understanding Data
- Basics of Learning Theory
- Similarity-based Learning
- Regression Analysis
- Decision Tree Learning
- Rule–based Learning
- Bayesian Learning
- Probabilistic Graphical Models
- Artificial Neural Networks
- Support Vector Machines
- Ensemble Learning
- Clustering Algorithms
- Reinforcement Learning
- Genetic Algorithms
- Deep Learning
