Author Description

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. Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. 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. What will you learn 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. Who this book