Author: Indranath Chatterjee

Machine Learning and Its Application: A Quick Guide for Beginners

eBook: US $69 Special Offer (PDF + Printed Copy): US $83
Printed Copy: US $117
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

Introduction

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.

Preface

- Pp. i-iii (3)
Indranath Chatterjee
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About the Author

- Pp. iv
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Dedication

- Pp. v

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Introduction to Machine Learning

- Pp. 1-18 (18)
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Supervised Machine Learning: Classification

- Pp. 19-71 (53)
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Unsupervised Machine Learning: Clustering

- Pp. 72-113 (42)
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Regression: Prediction

- Pp. 114-137 (24)
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Reinforcement Learning

- Pp. 138-169 (32)
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Deep Learning: A New Approach to Machine Learning

- Pp. 170-255 (86)
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Feature Engineering

- Pp. 256-289 (34)
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Applications of Machine Learning and Deep Learning

- Pp. 290-331 (42)
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Conclusions

- Pp. 332-334 (3)
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References

- Pp. 335-336 (2)

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Subject Index

- Pp. 337-343 (7)

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