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Mastering Machine Learning Algorithms

You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

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. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Further reading

Alpaydin E., Introduction to Machine Learning, The MIT Press, 2010
Breiman L., Random Forests, Machine Learning, 45, 2001
Friedman J., Hastie T., Tibshirani R., Additive Logistic Regression: A Statistical View of Boosting, Annals of Statistics, 28/1998
Zhu J., Rosset S., Zou H., Hastie T., Multi-Class AdaBoost, Statistics, and Its Inference, 02/2009
Drucker H., Improving Regressors Using Boosting Techniques, ICML 1997
Starttech Educational Services LLP, Decision Trees, Random Forests, AdaBoost and XGBoost in Python, Packt Publishing, 2019
Lundberg S. M., Lee S., A Unified Approach to Interpreting Model Predictions, Advances in Neural Information Processing Systems 30, NIPS, 2017
Bonaccorso G., Machine Learning Algorithms Second Edition, Packt Publishing, 2018

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Authors (2)

Bonaccorso

Bonaccorso

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

See other products by Bonaccorso

Giuseppe Bonaccorso

Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

See other products by Giuseppe Bonaccorso

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