 Название: Machine Learning
Автор: Saikat Dutt, Subramanian Chandramouli
Издательство: Pearson
Год: 2018
Страниц: 456
Язык: английский
Формат: epub
Размер: 18.0 MB
Repeated requests from Computer Science and IT engineering students who are the readers of our previous books encouraged us to write this book on Machine Learning. The concept of Machine Learning and the huge potential of its application is still niche knowledge and not so well-spread among the student community. So, we thought of writing this book specifically for techies, college students, and junior managers so that they understood the Machine Learning concepts easily. They should not only use the Machine Learning software packages, but understand the concepts behind those packages. The application of Machine Learning is getting boosted day by day. From recommending products to the buyers, to predicting the future real estate market, to helping medical practitioners in diagnosis, to optimizing energy consumption, thus helping the cause of Green Earth, Machine Learning is finding its utility in every sphere of life.
Due care was taken to write this book in simple English and to present the Machine Learning concepts in an easily understandable way which can be used as a textbook for both graduate and advanced undergraduate classes in Machine Learning or as a reference text.
Whom Is This Book For?
Readers of this book will gain a thorough understanding of Machine Learning concepts. Not only students but also software professionals will find a variety of techniques with sufficient discussions in this book that cater to the needs of the professional environments. Technical managers will get an insight into weaving machine learning into the overall software engineering process. Students, developers, and technical managers with a basic background in computer science will find the material in this book easily readable.
This book starts with an introduction to Machine Learning which lays the theoretical foundation for the remaining chapters. Modelling, Feature engineering, and basic probability are discussed as chapters before entering into the world of Machine Learning which helps to grip the Machine Learning concepts easily at a later point of time.
Bonus topics of Machine Learning exercise with multiple examples are discussed in Machine learning language of R & Python. Appendix A discusses Programming Machine Learning in R and Appendix B discusses Programming Machine Learning in Python.
Contents:
1 Introduction to Machine Learning
2 Preparing to Model
3 Modelling and Evaluation
4 Basics of Feature Engineering
5 Brief Overview of Probability
6 Bayesian Concept Learning
7 Supervised Learning: Classification
8 Supervised Learning: Regression
9 Unsupervised Learning
10 Basics of Neural Network
11 Other Types of Learning
Appendix A: Programming Machine Learning in R
Appendix B: Programming Machine Learning in Python
Appendix C: A Case Study on Machine Learning Application: Grouping Similar Service Requests and Classifying a New One
Model Question Paper-1
Model Question Paper-2
Model Question Paper-3
Index
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Machine Learning (2018) 
