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. Request Inspection Copy Sample Chapter(s) Chapter 1: Introduction 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.