Before ending this chapter, I want to introduce the reader to a very powerful algorithm called t-Distributed Stochastic Neighbor Embedding (t-SNE), which can be employed to visualize high-dimensional dataset also in 2D plots. In fact, one the hardest problems that every data scientist has to face is to understand the structure of a complex dataset without the support of graphs. This algorithm has been proposed by Van der Maaten and Hinton (in Visualizing High-Dimensional Data Using t-SNE, Van der Maaten L.J.P., Hinton G.E., Journal of Machine Learning Research 9 (Nov), 2008), and can be used to reduce the dimensionality trying to preserve the internal relationships. A complete discussion is beyond the scope of this book (but the reader can check out the aforementioned paper and Mastering Machine Learning Algorithms, Bonaccorso...
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Book Overview & Buying
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Table Of Contents
Machine Learning Algorithms
By :
Machine Learning Algorithms
By:
Overview of this book
In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.
In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.
On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
Table of Contents (16 chapters)
Preface
Free Chapter
A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Linear Regression
Logistic Regression
Naive Bayes
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Hierarchical Clustering
Introduction to Recommendation Systems
Introduction to Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
A Brief Introduction to Deep Learning and TensorFlow
Creating a Machine Learning Architecture
