Machine Learning for Beginners
Publishing Date: August 2020
Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation.
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms
- Understand the types of Machine learning.
- Get familiar with different Feature extraction methods.
- Get an overview of how Neural Network Algorithms work.
- Learn how to implement Decision Trees and Random Forests.
- The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.
- Learn how to prepare Data for Machine Learning.
- Learn how to implement learning algorithms from scratch.
- Use scikit-learn to implement algorithms.
- Use various Feature Selection and Feature Extraction methods.
- Learn how to develop a Face recognition system.
The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.
- An introduction to Machine Learning
- The beginning: Pre-Processing and Feature Selection
- Neural Networks- I
- Neural Networks-II
- Support Vector machines
- Decision Trees
- Feature Extraction
- A1. Cheat Sheets
- A2. Face Detection
Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.
Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.
His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning.