Machine Learning with Python

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.

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