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Machine Learning for Beginners
Publisher:
BPB Publications
| Author:
Harsh Bhasin
| Language:
English
| Format:
Paperback
Publisher:
BPB Publications
Author:
Harsh Bhasin
Language:
English
Format:
Paperback
₹719
Save: 10%
In stock
Ships within:
1-4 Days
In stock
ISBN:
Page Extent:
384
The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning.
The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics.
By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios.
Description
The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning.
The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics.
By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios.
About Author
Dr. Harsh Bhasin is a researcher and practitioner. Dr. Bhasin is currently associated with the Centre for Health Innovations, Manav Rachna International Institution of Research and Studies. Dr. Bhasin has completed his Ph. D. in Diagnosis and Conversion Prediction of Mild Cognitive Impairment Using Machine Learning from Jawaharlal Nehru University, New Delhi. He worked as a Deep Learning consultant for various firms and taught at various Universities including Jamia Hamdard, MRU and DTU.
He has authored 11 books including Programming in C#, Oxford University Press, 2014; Algorithms, Oxford University Press, 2015; Python for Beginners, New Age International, 2018; Python Basics, Mercury, 2019; Machine Learning, BPB, 2020, to name a few.
Dr. Bhasin has authored more than 40 papers published in conferences and renowned journals including Alzheimer’s and Dementia, Soft Computing, Springer, BMC Medical Informatics & Decision Making, AI & Society, etc. He is the reviewer of a few renowned journals and has been the editor of a few special issues. He is a recipient of a distinguished fellowship.
His areas of expertise include Deep Learning, Algorithms, and Medical Imaging. Outside work, he is deeply interested in Hindi Poetry: the progressive era, and Hindustani Classical Music: percussion instruments.
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