Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

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.
Table of Contents (28 chapters)
26
Other Books You May Enjoy
27
Index

Machine Learning Model Fundamentals

Machine learning models are mathematical tools that allow us to uncover synthetic representations of external events, with the purpose of gaining better understanding and predicting future behavior. Sometimes these models have only been defined from a theoretical viewpoint, but advances in research now allow us to apply machine learning concepts to better understand the behavior of complex systems such as deep neural networks. In this chapter, we're going to introduce and discuss some fundamental elements. Skilled readers may already know these elements, but here we offer several possible interpretations and applications.

In particular, in this chapter, we're discussing the main elements of:

  • Defining models and data
  • Understanding the structure and properties of good datasets
  • Scaling datasets, including scalar and robust scaling
  • Normalization and whitening
  • Selecting training, validation and test sets, including cross-validation
  • The features of a machine learning model
  • Learnability
  • Capacity, including Vapnik-Chervonenkis capacity
  • Bias, including underfitting
  • Variance, including overfitting and the Cramér-Rao bound