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

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

A Gentle Introduction to Machine Learning

Introduction - classic and adaptive machines

Only learning matters

Beyond machine learning - deep learning and bio-inspired adaptive systems

Machine learning and big data

Further reading

Summary

Important Elements in Machine Learning

Data formats

Learnability

Statistical learning approaches

Elements of information theory

References

Summary

Feature Selection and Feature Engineering

scikit-learn toy datasets

Creating training and test sets

Managing categorical data

Managing missing features

Data scaling and normalization

Feature selection and filtering

Principal component analysis

Atom extraction and dictionary learning

References

Summary

Linear Regression

Linear models

A bidimensional example

Linear regression with scikit-learn and higher dimensionality

Ridge, Lasso, and ElasticNet

Robust regression with random sample consensus

Polynomial regression

Isotonic regression

References

Summary

Logistic Regression

Linear classification

Logistic regression

Implementation and optimizations

Stochastic gradient descent algorithms

Finding the optimal hyperparameters through grid search

Classification metrics

ROC curve

Summary

Naive Bayes

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

References

Summary

Support Vector Machines

Linear support vector machines

scikit-learn implementation

Controlled support vector machines

Support vector regression

References

Summary

Decision Trees and Ensemble Learning

Binary decision trees

Decision tree classification with scikit-learn

Ensemble learning

References

Summary

Clustering Fundamentals

Clustering basics

Evaluation methods based on the ground truth

References

Summary

Hierarchical Clustering

Hierarchical strategies

Agglomerative clustering

References

Summary

Introduction to Recommendation Systems

Naive user-based systems

Content-based systems

Model-free (or memory-based) collaborative filtering

Model-based collaborative filtering

References

Summary

Introduction to Natural Language Processing

NLTK and built-in corpora

The bag-of-words strategy

A sample text classifier based on the Reuters corpus

References

Summary

Topic Modeling and Sentiment Analysis in NLP

Topic modeling

Sentiment analysis

References

Summary

A Brief Introduction to Deep Learning and TensorFlow

Deep learning at a glance

A brief introduction to TensorFlow

A quick glimpse inside Keras

References

Summary

Creating a Machine Learning Architecture

Machine learning architectures

scikit-learn tools for machine learning architectures

References

Summary

Customer Reviews

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Model-free (or memory-based) collaborative filtering

As with the user-based approach, let's consider having two sets of elements: users and items. However, in this case, we don't assume that they have explicit features. Instead, we try to model a user-item matrix based on the preferences of each user (rows) for each item (columns). For example:

In this case, the ratings are bounded between 1 and 5 (0 means no rating), and our goal is to cluster the users according to their rating vector (which is, indeed, an internal representation based on a particular kind of feature). This allows producing recommendations even when there are no explicit pieces of information about the user. However, it has a drawback, called cold-startup, which means that when a new user has no ratings, it's impossible to find the right neighborhood, because he/she can belong to virtually any cluster.

Once the clustering is done, it's easy to check which products (not rated yet) have the higher rating for a given user and...

Machine Learning Algorithms

Machine Learning Algorithms

Overview of this book

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Machine Learning Algorithms

Model-free (or memory-based) collaborative filtering