Search icon
Close icon
Search icon
CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Buy 2 products and save 10%
Buy 3 products and save 15%
Buy 5 products and save 20%
Savings automatically calculated. No voucher code required
Profile icon
Account
Close icon
Sign in
New User?
Create Account
Your Subscription
Your Owned Titles
Your Account
Your Orders
Change country
Modal Close icon
Country selected
Country selected
United States
Country selected
United Kingdom
Country selected
India
Country selected
Germany
Country selected
France
Country selected
Canada
Country selected
Russia
Country selected
Spain
Country selected
Brazil
Country selected
Australia
Country selected
Argentina
Country selected
Austria
Country selected
Belgium
Country selected
Bulgaria
Country selected
Chile
Country selected
Colombia
Country selected
Cyprus
Country selected
Czechia
Country selected
Denmark
Country selected
Ecuador
Country selected
Egypt
Country selected
Estonia
Country selected
Finland
Country selected
Greece
Country selected
Hungary
Country selected
Indonesia
Country selected
Ireland
Country selected
Italy
Country selected
Japan
Country selected
Latvia
Country selected
Lithuania
Country selected
Luxembourg
Country selected
Malaysia
Country selected
Malta
Country selected
Mexico
Country selected
Netherlands
Country selected
New Zealand
Country selected
Norway
Country selected
Philippines
Country selected
Poland
Country selected
Portugal
Country selected
Romania
Country selected
Singapore
Country selected
Slovakia
Country selected
Slovenia
Country selected
South Africa
Country selected
South Korea
Country selected
Sweden
Country selected
Switzerland
Country selected
Taiwan
Country selected
Thailand
Country selected
Turkey
Country selected
Ukraine
Country selected
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO
TOP
You're reading from
Machine Learning Algorithms
Product type
Book
Published in
Jul 2017
Publisher
Packt
ISBN-13
9781785889622
Pages
360 pages
Edition
1st Edition
Languages
Python
Concepts
Machine Learning
Toc
Table of Contents
(22) Chapters
close
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. 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
Data formats
Learnability
Statistical learning approaches
Elements of information theory
References
Summary
3. 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 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
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
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
References
Summary
7. 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
Binary decision trees
Decision tree classification with scikit-learn
Ensemble learning
References
Summary
9. Clustering Fundamentals
Clustering basics
Evaluation methods based on the ground truth
References
Summary
10. Hierarchical Clustering
Hierarchical strategies
Agglomerative clustering
References
Summary
11. 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
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
Sentiment analysis
References
Summary
14. 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
Machine learning architectures
scikit-learn tools for machine learning architectures
References
Summary
References
Russel S., Norvig P.,
Artificial Intelligence: A Modern Approach
, Pearson
Zhang H.,
The Optimality of Naive Bayes, AAAI 1
, no. 2 (2004): 3
Papoulis A.,
Probability, Random Variables and Stochastic Processes
, McGraw-Hill
The rest of the chapter is locked
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Start free trial
Previous Section
Section 5 of 6
Next Section
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Sign up now
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Start free trial
Renews at
$15.99/month
. Cancel anytime
Other recommended products
Related to this chapter
Left arrow icon
Machine Learning Algorithms
Read more
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.
Read more
Aug 2018
17 hours 24 minutes
Hands-On Unsupervised Learning with Python
Read more
Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.
Read more
Feb 2019
12 hours 52 minutes
Mastering Machine Learning Algorithms
Read more
A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques.
Read more
Jan 2020
26 hours 36 minutes
Mastering Machine Learning Algorithms
Read more
This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
Read more
May 2018
19 hours 12 minutes
Machine Learning with scikit-learn Quick Start Guide
Read more
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize and evaluate all the important machine learning algorithms that scikit-learn provides.
Read more
Oct 2018
5 hours 44 minutes
scikit-learn Cookbook
Read more
scikit-learn has evolved as a robust library for machine learning applications in python with support for a wide range of supervised and unsupervised learning algorithms. This edition brings to you the various enhancements to its model implementations, API and bug fixes in the latest major release of scikit-learn to support Python. This book covers easy to follow recipes right from mathematical operations to implementing various supervised, unsupervised and deep learning algorithms with scikit-learn. Get practical hands-on knowledge to implement various models and algorithms like Multi-Layer Perceptrons, time-series split, MAE criterion for regression, criteria for gradient boosting, Classifier, Regressor, and much more.
Read more
Nov 2017
12 hours 28 minutes
Ensemble Machine Learning Cookbook
Read more
This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference to implement a wide range of tasks and solve the common and uncommon problems in ensemble machine learning domain.
Read more
Jan 2019
11 hours 12 minutes
Supervised Machine Learning with Python
Read more
A supervised learning task infers a function from flagged training data and maps an input to an output based on sample input-output pairs. In this book, you will learn various machine learning techniques (such as linear and logistic regression) and gain the practical knowledge you need to quickly and powerfully apply algorithms to new problems.
Read more
May 2019
5 hours 24 minutes
Hands-On Ensemble Learning with Python
Read more
Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models.
Read more
Jul 2019
9 hours 56 minutes
Python Data Mining Quick Start Guide
Read more
This book is an introduction to data mining and its practical demonstration of working with real-world data sets. With this book, you will be able to extract useful insights using common Python libraries. You will also learn key stages like data loading, cleaning, analysis, visualization to build an efficient data mining pipeline.
Read more
Apr 2019
6 hours 16 minutes
Mastering Machine Learning with scikit-learn
Read more
This book examines machine learning models including k-nearest neighbors, logistic regression, naive Bayes, random forests, and support vector machines. You will work through document classification, image recognition, and other example problems.
Read more
Jul 2017
8 hours 28 minutes
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Read more
This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.
Read more
Jul 2020
12 hours 48 minutes
Right arrow icon
Personalised recommendations for you
Based on your interests and search pattern
Left arrow icon
Et al.
Read more
Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.
Read more
Aug 2023
7 hours 40 minutes
Generative AI with LangChain
Read more
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Read more
Dec 2023
12 hours 0 minutes
Generative AI with LangChain
Read more
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Read more
Dec 2023
12 hours 0 minutes
Generative AI with LangChain
Read more
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Read more
Dec 2023
12 hours 0 minutes
Generative AI with LangChain
Read more
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Read more
Dec 2023
12 hours 0 minutes
Mastering Tableau 2023
Read more
This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.
Read more
Aug 2023
22 hours 48 minutes
Building AI Applications with ChatGPT APIs
Read more
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Read more
Sep 2023
8 hours 36 minutes
Building AI Applications with ChatGPT APIs
Read more
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Read more
Sep 2023
8 hours 36 minutes
Data Engineering with AWS
Read more
Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.
Read more
Oct 2023
21 hours 12 minutes
Modern Data Architecture on AWS
Read more
Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.
Read more
Aug 2023
14 hours 0 minutes
Practical Guide to Applied Conformal Prediction in Python
Read more
Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.
Read more
Dec 2023
8 hours 0 minutes
TinyML Cookbook
Read more
With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.
Read more
Nov 2023
22 hours 8 minutes
Right arrow icon