In many cases, the dimensionality of the input dataset X is high and so is the complexity of every related machine learning algorithm. Moreover, the information is seldom spread uniformly across all the features and, as discussed in the previous chapter, Chapter 2, Important Elements in Machine Learning, there will be high-entropy features together with low-entropy ones, which, of course, don't contribute dramatically to the final outcome. This concept can also be expressed by considering a fundamental assumption of semi-supervised learning, called the manifold assumption. It states (without a formal proof, as it's an empirical hypothesis) that data with high dimensionality normally lies on lower-dimensional manifolds. If the reader is not familiar with the concept of a manifold, it's not necessary for our purpose to provide a complete...
Machine Learning Algorithms - Second Edition
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.
Related Content you might be interested in
Current Title:
Machine Learning Algorithms - Second Edition
Table of Contents (19 chapters)
Preface
Free Chapter
A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Regression Algorithms
Linear Classification Algorithms
Naive Bayes and Discriminant Analysis
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering
Introducing Recommendation Systems
Introducing Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
Introducing Neural Networks
Advanced Deep Learning Models
Creating a Machine Learning Architecture
Other Books You May Enjoy
Customer Reviews