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Machine Learning Algorithms

By : Giuseppe Bonaccorso

4.5 (4)

Machine Learning Algorithms

4.5 (4)

By: Giuseppe Bonaccorso

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

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

A Gentle Introduction to Machine Learning

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

Important Elements in Machine Learning

Data formats

Learnability

Statistical learning approaches

Elements of information theory

References

Summary

Feature Selection and Feature Engineering

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 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

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

Naive Bayes

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

References

Summary

Support Vector Machines

Support Vector Machines

Linear support vector machines

scikit-learn implementation

Controlled support vector machines

Support vector regression

References

Summary

Decision Trees and Ensemble Learning

Decision Trees and Ensemble Learning

Binary decision trees

Decision tree classification with scikit-learn

Ensemble learning

References

Summary

Clustering Fundamentals

Clustering Fundamentals

Clustering basics

Evaluation methods based on the ground truth

References

Summary

Hierarchical Clustering

Hierarchical Clustering

Hierarchical strategies

Agglomerative clustering

References

Summary

Introduction to Recommendation Systems

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

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 and Sentiment Analysis in NLP

Topic modeling

Sentiment analysis

References

Summary

A Brief Introduction to Deep Learning and TensorFlow

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

Creating a Machine Learning Architecture

Machine learning architectures

scikit-learn tools for machine learning architectures

References

Summary

Customer Reviews

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Linear regression with scikit-learn and higher dimensionality

The scikit-learn library offers the LinearRegression class, which works with n-dimensional spaces. For this purpose, we're going to use the Boston dataset:

from sklearn.datasets import load_boston

boston = load_boston()

print(boston.data.shape)
(506L, 13L)

print(boston.target.shape)
(506L,)

It has 506 samples with 13 input features and one output. In the following graph, there's a collection of the plots of the first 12 features:

The plot of the first 12 features of the Boston dataset

When working with datasets, it's useful to have a tabular view to manipulate data. Pandas is a perfect framework for this task, and even though it's beyond the scope of this book, I suggest you create a data frame with the pandas.DataFrame(boston.data, columns=boston.feature_names) command and use Jupyter to visualize it...

Visually different images

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