From the back cover:
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.
Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.
If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of contents:
- MACHINE LEARNING MODEL FUNDAMENTALS
- INTRODUCTION TO SEMI-SUPERVISED LEARNING
- GRAPH-BASED SEMI-SUPERVISED LEARNING
- BAYESIAN NETWORKS AND HIDDEN MARKOV MODELS
- EM ALGORITHM AND APPLICATIONS
- HEBBIAN LEARNING AND SELF-ORGANIZING MAPS
- CLUSTERING ALGORITHMS
- ENSEMBLE LEARNING
- NEURAL NETWORKS FOR MACHINE LEARNING
- ADVANCED NEURAL MODELS
- GENERATIVE ADVERSARIAL NETWORKS
- DEEP BELIEF NETWORKS
- INTRODUCTION TO REINFORCEMENT LEARNING
- ADVANCED POLICY ESTIMATION ALGORITHMS