Machine Learning (ML) has become a very important area of research widely used in various industries.

This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.

The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks.

Contents:

• Introduction


• Basics of Python


• Basic Mathematical Computations


• Statistics and Probability-based Learning Model


• Prediction Function and Universal Prediction Theory


• The Perceptrons and SVM


• Activation Functions and Universal Approximation Theory


• Automatic Differentiation and Autograd


• Solution Existence Theory and Optimization Techniques


• Loss Functions for Regression


• Loss Functions and Models for Classification


• Multiclass Classification


• Multilayer Perceptron (MLP) for Regression and Classification


• Overfitting and Regularization


• Convolutional Neutral Network (CNN) for Classification and Object Detection


• Recurrent Neural Network (RNN)and Sequence Feature Models


• Unsupervised Learning Techniques


• Reinforcement Learning (RL)

Readership: Researchers, professionals, academics, undergraduate and graduate students in AI and machine learning.