An Introduction to Machine Learning

Book Description Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms.  Many of the mathematical concepts are explained in an intuitive manner.  The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with.  

• Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic;
• Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning;
• Not tied to any specific software language or hardware implementation. 

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Content

1. Machine Learning Definition and Basics
2. Learning Models
3. Regressions
4. Improving Further
5. Classification
6. Clustering
7. Random Forests
8. Testing the Algorithm and the Network
9. (Artificial) Neural Networks
10. Natural Language Processing
11. Deep Learning
12. Principal Component Analysis
13. Anomaly Detection
14. Recommender Systems
15. Convolution
16. Components of Reinforcement Learning
17. Reinforcement Learning Algorithms
18. Designing a Machine Learning System