Book Image

Machine Learning Algorithms

Book Image

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

Related Content you might be interested in

Current Title:

Small book image
Machine Learning Algorithms
Jul, 2017 | 360 pages
No titles found
Table of Contents (22 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
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
2
Important Elements in Machine Learning
Important Elements in Machine Learning
Data formats
Learnability
Statistical learning approaches
Elements of information theory
References
Summary
3
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
4
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
5
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
6
Naive Bayes
Naive Bayes
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
References
Summary
7
Support Vector Machines
Support Vector Machines
Linear support vector machines
scikit-learn implementation
Controlled support vector machines
Support vector regression
References
Summary
8
Decision Trees and Ensemble Learning
Decision Trees and Ensemble Learning
Binary decision trees
Decision tree classification with scikit-learn
Ensemble learning
References
Summary
9
Clustering Fundamentals
Clustering Fundamentals
Clustering basics
Evaluation methods based on the ground truth
References
Summary
10
Hierarchical Clustering
Hierarchical Clustering
Hierarchical strategies
Agglomerative clustering
References
Summary
11
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
12
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
13
Topic Modeling and Sentiment Analysis in NLP
Topic Modeling and Sentiment Analysis in NLP
Topic modeling
Sentiment analysis
References
Summary
14
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
15
Creating a Machine Learning Architecture
Creating a Machine Learning Architecture
Machine learning architectures
scikit-learn tools for machine learning architectures
References
Summary

About the Reviewers

Manuel Amunategui is the VP of data science at SpringML, a start-up offering Google Cloud, TensorFlow, and Salesforce enterprise solutions. Prior to that, he worked as a quantitative developer on Wall Street for a large equity options market-making firm and as a software developer at Microsoft. He holds master's degrees in predictive analytics and international administration.

He is a data science advocate, blogger/vlogger (http://amunategui.github.io) and trainer on Udemy.com and O'Reilly Media, and technical reviewer at Packt.

Doug Ortiz is a senior big data architect at ByteCubed who has been architecting, developing, and integrating enterprise solutions throughout his career. Organizations that leverage his skill set have been able to rediscover and reuse their underutilized data via existing and emerging technologies such as Microsoft BI Stack, Hadoop, NoSQL databases, SharePoint, and related tool sets and technologies. He is also the founder of Illustris, LLC and can be reached at [email protected].

Some interesting aspects of his profession are that he has experience in integrating multiple platforms and products, big data, data science certifications, R, and Python certifications. Doug also helps organizations gain a deeper understanding of and value their current investments in data and existing resources, turning them into useful sources of information. He has improved, salvaged, and architected projects by utilizing unique and innovative techniques. His hobbies include yoga and scuba diving.

Lukasz Tracewski is a software developer and a scientist, specializing in machine learning, digital signal processing, and cloud computing. Being an active member of open source community, he is also an author of numerous research publications. He has worked for 6 years as a software scientist in high-tech industry in the Netherlands, first in photolithography and later in electron microscopy, helping to build algorithms and machines that reach physical limits of throughput and precision. Currently, he leads a data science team in the financial industry.

For 4 years now, Lukasz has been using his skills pro bono in conservation science, involved in topics such as classification of bird species from audio recordings or satellite imagery analysis. He inhales carbon dioxide and exhales endangered species in his spare time.

Previous Section
Next Chapter