Feature engineering is the first step in a machine learning pipeline and involves all the techniques adopted to clean existing datasets, increase their signal-noise ratio, and reduce their dimensionality. Most algorithms have strong assumptions about the input data, and their performances can be negatively affected when raw datasets are used. Moreover, the data is seldom isotropic; there are often features that determine the general behavior of a sample, while others that are correlated don't provide any additional pieces of information. So, it's important to have a clear view of a dataset and know the most common algorithms used to reduce the number of features or select only the best ones.
Machine Learning Algorithms
Machine Learning Algorithms
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
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Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
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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
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