Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

Summary

A linear model classifies samples using separating hyperplanes, so a problem is linearly separable if it's possible to find a linear model whose accuracy overcomes a predetermined threshold. Logistic regression is one of most famous linear classifiers, based on the principle of maximizing the probability of a sample belonging to the right class. SGD classifiers are a more generic family of algorithms, identified by the different loss functions that are adopted. SGD allows partial fitting, particularly when the amount of data is too large to be loaded in memory. A Perceptron is a particular instance of SGD, representing a linear neural network that cannot solve the XOR problem (for this reason, multi-layer perceptrons became the first choice for non-linear classification). However, in general, its performance is comparable to a logistic regression model.

The performances...