Author: "Indranath Chatterjee"

Machine Learning and Its Application: A Quick Guide for Beginners

eBook: US $69 Special Offer (PDF + Printed Copy): US $117
Printed Copy: US $83
Library License: US $276
ISBN: 978-1-68108-941-6 (Print)
ISBN: 978-1-68108-940-9 (Online)
Year of Publication: 2021
DOI: 10.2174/97816810894091210101
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Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering.

Key features:

  • - 8 organized chapters on core concepts of machine learning for learners
  • - Accessible text for beginners unfamiliar with complex mathematical concepts
  • - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics
  • - Advanced topics such as deep learning and feature engineering provide additional information
  • - Introduces readers to python programming with examples of code for understanding and practice
  • - Includes a summary of the text and a dedicated section for references

Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.


Review 1

" The book focuses on practical aspects of machine learning and avoids rigorous mathematics. It explains the ML techniques with the help of concrete examples and highlights the circumstances when a specific technique would be useful. The book also includes a discussion on feature engineering. The discussion on reinforcement learning techniques is particularly inviting. Extensive discussion on neural networks prepares the students for the study of deep learning techniques." - Professor Naveen Kumar, Department of Computer Science, University of Delhi, India