Mastering Machine Learning Algorithms

Explore and master the most important algorithms for solving complex machine learning problems.
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Mastering Machine Learning Algorithms

Giuseppe Bonaccorso

Explore and master the most important algorithms for solving complex machine learning problems.
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Book Details

ISBN 139781788621113
Paperback605 pages

Book Description

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

Chapter 1: Machine Learning Models Fundamentals
Models and Data
Model features
Loss and cost functions
References
Summary
Chapter 2: Introduction to Semi-Supervised Learning
Semi-supervised scenario
Generative Gaussian Mixtures
Contrastive Pessimistic Likelihood Estimation
Semi-supervised Support Vector Machines
Transductive Support Vector Machines
References
Summary
Chapter 3: Graph-Based Semi-Supervised Learning
Chapter 4: Bayesian Networks and Hidden Markov Models
Conditional probabilities and Bayes' Theorem
Bayesian Networks
Hidden Markov models
References
Summary
Chapter 5: EM algorithm and applications
Chapter 6: Hebbian Learning
Chapter 7: Advanced Clustering and Feature Extraction
Chapter 8: Ensemble Learning
Chapter 9: Neural Networks for Machine Learning
Chapter 10: Auto-Encoders
Chapter 11: Advanced Neural Models
Chapter 12: Generative Adversarial Networks
Chapter 13: Deep Belief Networks
Chapter 14: Introduction to Reinforcement Learning
Chapter 15: Policy Estimation Algorithms

What You Will Learn

  • Explore how a ML model can be trained, optimized, and evaluated
  • Understand how to create and learn static and dynamic probabilistic models
  • Successfully cluster high-dimensional data and evaluate model accuracy
  • Discover how artificial neural networks work and how to train, optimize, and validate them
  • Work with Autoencoders and Generative Adversarial Networks
  • Apply label spreading and propagation to large datasets
  • Explore the most important Reinforcement Learning techniques

Authors

Table of Contents

Chapter 1: Machine Learning Models Fundamentals
Models and Data
Model features
Loss and cost functions
References
Summary
Chapter 2: Introduction to Semi-Supervised Learning
Semi-supervised scenario
Generative Gaussian Mixtures
Contrastive Pessimistic Likelihood Estimation
Semi-supervised Support Vector Machines
Transductive Support Vector Machines
References
Summary
Chapter 3: Graph-Based Semi-Supervised Learning
Chapter 4: Bayesian Networks and Hidden Markov Models
Conditional probabilities and Bayes' Theorem
Bayesian Networks
Hidden Markov models
References
Summary
Chapter 5: EM algorithm and applications
Chapter 6: Hebbian Learning
Chapter 7: Advanced Clustering and Feature Extraction
Chapter 8: Ensemble Learning
Chapter 9: Neural Networks for Machine Learning
Chapter 10: Auto-Encoders
Chapter 11: Advanced Neural Models
Chapter 12: Generative Adversarial Networks
Chapter 13: Deep Belief Networks
Chapter 14: Introduction to Reinforcement Learning
Chapter 15: Policy Estimation Algorithms

Book Details

ISBN 139781788621113
Paperback605 pages
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