Description
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.
Tagline
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms
Key Features
- 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.