9781838820299-1838820299-Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition

Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition

ISBN-13: 9781838820299
ISBN-10: 1838820299
Author: Bonaccorso, Giuseppe
Publication date: 2020
Publisher: Packt Publishing
Format: Paperback 798 pages
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Book details

ISBN-13: 9781838820299
ISBN-10: 1838820299
Author: Bonaccorso, Giuseppe
Publication date: 2020
Publisher: Packt Publishing
Format: Paperback 798 pages

Summary

Acknowledged authors Bonaccorso, Giuseppe wrote Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition comprising 798 pages back in 2020. Textbook and eTextbook are published under ISBN 1838820299 and 9781838820299. Since then Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition textbook was available to sell back to BooksRun online for the top buyback price of $ 10.41 or rent at the marketplace.

Description

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems

Key Features
  • Updated to include new algorithms and techniques
  • Code updated to Python 3.8 & TensorFlow 2.x
  • New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications
Book Description

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.

You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

What you will learn
  • Understand the characteristics of a machine learning algorithm
  • Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains
  • Learn how regression works in time-series analysis and risk prediction
  • Create, model, and train complex probabilistic models
  • Cluster high-dimensional data and evaluate model accuracy
  • Discover how artificial neural networks work – train, optimize, and validate them
  • Work with autoencoders, Hebbian networks, and GANs
Who this book is for

This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Table of Contents
  1. Machine Learning Model Fundamentals
  2. Loss functions and Regularization
  3. Introduction to Semi-Supervised Learning
  4. Advanced Semi-Supervised Classifiation
  5. Graph-based Semi-Supervised Learning
  6. Clustering and Unsupervised Models
  7. Advanced Clustering and Unsupervised Models
  8. Clustering and Unsupervised Models for Marketing
  9. Generalized Linear Models and Regression
  10. Introduction to Time-Series Analysis
  11. Bayesian Networks and Hidden Markov Models
  12. The EM Algorithm
  13. Component Analysis and Dimensionality Reduction
  14. Hebbian Learning
  15. Fundamentals of Ensemble Learning
  16. Advanced Boosting Algorithms
  17. Modeling Neural Networks
  18. Optimizing Neural Networks
  19. Deep Convolutional Networks
  20. Recurrent Neural Networks
  21. Auto-Encoders
  22. Introduction to Generative Adversarial Networks
  23. Deep Belief Networks
  24. Introduction to Reinforcement Learning
  25. Advanced Policy Estimation Algorithms
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