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)

Advanced Clustering

In this chapter, we're going to discuss some advanced clustering algorithms that can be employed when K-means (as well as other similar methods) fails to cluster a dataset. In Chapter 9, Clustering Fundamentals, we have seen that such models are based on the assumption of convex clusters that can be surrounded by a hyperspherical boundary. In this way, simple distance metrics can be employed to determine the correct labeling. Unfortunately, many real-life problems are based on concave and irregular structures that are wrongly split by K-means or a Gaussian mixture.

We will also explain two famous online algorithms that can be chosen whenever the dataset is too large to fit into the memory or when the data is streamed in a real-time flow. Surprisingly, even if these models work with a limited number of samples, their performance is only slightly worse than...