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)
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In this chapter, we discussed some fundamental concepts shared by almost any machine learning model.

In the first part, we introduced the data generating process, as a generalization of a finite dataset, and discussed the structure and properties of a good dataset. We discussed some common preprocessing strategies and their properties, such as scaling, normalizing, and whitening. We explained the most common strategies to split a finite dataset into a training block and a validation set, and we introduced cross-validation, with some of the most important variants, as one of the best approaches to avoid the limitations of a static split.

In the second part, we discussed the features of a machine learning model, and the concept of learnability. We discussed the main properties of an estimator: capacity, bias, and variance. We also introduced the Vapnik-Chervonenkis theory, which is a mathematical formalization of the concept of representational capacity, and we analyzed the effects of high biases and high variances. In particular, we discussed effects called underfitting and overfitting, defining the relationship with high bias and high variance.

In the next chapter, Chapter 2, Loss functions and Regularization , we're going to introduce loss and cost functions, which provide a simple and effective tool to fit machine learning models by minimizing an error measure or maximizing a specific objective.