- 456 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding.
Features
- Concepts of Machine learning from basics to algorithms to implementation
- Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers
- Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications
- Ethics of machine learning including Bias, Fairness, Trust, Responsibility
- Basics of Deep learning, important deep learning models and applications
- Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises
The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
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Information
Table of contents
- Cover Page
- Halftitle Page
- Title Page
- Copyright Page
- Contents
- Preface
- Author Biography
- 1 Introduction
- 2 Understanding Machine Learning
- 3 Mathematical Foundations and Machine Learning
- 4 Foundations and Categories of Machine Learning Techniques
- 5 Machine Learning: Tools and Software
- 6 Classification Algorithms
- 7 Probabilistic and Regression Based Approaches
- 8 Performance Evaluation and Ensemble Methods
- 9 Unsupervised Learning
- 10 Sequence Models
- 11 Reinforcement Learning
- 12 Machine Learning Applications: Approaches
- 13 Domain Based Machine Learning Applications
- 14 Ethical Aspects of Machine Learning
- 15 Introduction to Deep Learning and Convolutional Neural Networks
- 16 Other Models of Deep Learning and Applications of Deep Learning
- A1. Solutions
- Index