Python Machine Learning Cookbook
eBook - ePub

Python Machine Learning Cookbook

  1. 304 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Python Machine Learning Cookbook

About this book

100 recipes that teach you how to perform various machine learning tasks in the real world

About This Book

  • Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
  • Learn about perceptrons and see how they are used to build neural networks
  • Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques

Who This Book Is For

This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.

What You Will Learn

  • Explore classification algorithms and apply them to the income bracket estimation problem
  • Use predictive modeling and apply it to real-world problems
  • Understand how to perform market segmentation using unsupervised learning
  • Explore data visualization techniques to interact with your data in diverse ways
  • Find out how to build a recommendation engine
  • Understand how to interact with text data and build models to analyze it
  • Work with speech data and recognize spoken words using Hidden Markov Models
  • Analyze stock market data using Conditional Random Fields
  • Work with image data and build systems for image recognition and biometric face recognition
  • Grasp how to use deep neural networks to build an optical character recognition system

In Detail

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Style and approach

You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.

Tools to learn more effectively

Saving Books

Saving Books

Keyword Search

Keyword Search

Annotating Text

Annotating Text

Listen to it instead

Listen to it instead

Information

Python Machine Learning Cookbook


Table of Contents

Python Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why Subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Getting ready
How to do it…
How it works…
There's more…
See also
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. The Realm of Supervised Learning
Introduction
Preprocessing data using different techniques
Getting ready
How to do it…
Mean removal
Scaling
Normalization
Binarization
One Hot Encoding
Label encoding
How to do it…
Building a linear regressor
Getting ready
How to do it…
Computing regression accuracy
Getting ready
How to do it…
Achieving model persistence
How to do it…
Building a ridge regressor
Getting ready
How to do it…
Building a polynomial regressor
Getting ready
How to do it…
Estimating housing prices
Getting ready
How to do it…
Computing the relative importance of features
How to do it…
Estimating bicycle demand distribution
Getting ready
How to do it…
There's more…
2. Constructing a Classifier
Introduction
Building a simple classifier
How to do it…
There's more…
Building a logistic regression classifier
How to do it…
Building a Naive Bayes classifier
How to do it…
Splitting the dataset for training and testing
How to do it…
Evaluating the accuracy using cross-validation
Getting ready…
How to do it…
Visualizing the confusion matrix
How to do it…
Extracting the performance report
How to do it…
Evaluating cars based on their characteristics
Getting ready
How to do it…
Extracting validation curves
How to do it…
Extracting learning curves
How to do it…
Estimating the income bracket
How to do it…
3. Predictive Modeling
Introduction
Building a linear classifier using Support Vector Machine (SVMs)
Getting ready
How to do it…
Building a nonlinear classifier using SVMs
How to do it…
Tackling class imbalance
How to do it…
Extracting confidence measurements
How to do it…
Finding optimal hyperparameters
How to do it…
Building an event predictor
Getting ready
How to do it…
Estimating traffic
Getting ready
How to do it…
4. Clustering with Unsupervised Learning
Introduction
Clustering data using the k-means algorithm
How to do it…
Compressing an image using vector quantization
How to do it…
Building a Mean Shift clustering model
How to do it…
Grouping data using agglomerative clustering
How to do it…
Evaluating the performance of clustering algorithms
How to do it…
Automatically estimating the number of clusters using DBSCAN algorithm
How to do it…
Finding patterns in stock market data
How to do it…
Building a customer segmentation model
How to do it…
5. Building Recommendation Engines
Introduction
Building function compositions for data processing
How to do it…
Building machine learning pipelines
How to do it…
How it works…
Finding the nearest neighbors
How to do it…
Constructing a k-nearest neighbors classifier
How to do it…
How it works…
Constructing a k-nearest neighbors regressor
How to do it…
How it works…
Computing the Euclidean distance score
How to do it…
Computing the Pearson correlation score
How to do it…
Finding similar users in the dataset
How to do it…
Generating movie recommendations
How to do it…
6. Analyzing Text Data
Introduction
Preprocessing data using tokenization
How to do it…
Stemming text data
How to do it…
How it works…
Converting text to its base form using lemmatization
How to do it…
Dividing text using chunking
How to do it…
Building a bag-of-words model
How to do it…
How it works…
Building a text classifier
How to do it…
How it works…
Identifying the gender
How to do it…
Analyzing the sentiment of a sentence
How to do it…
How it works…
Identifying patterns in text using topic modeling
How to do it…
How it works…
7. Speech Recognition
Introduction
Reading and plotting audio data
How to do it…
Transforming audio signals into the frequency domain
How to do it…
Generating audio signals with custom parameters
How to do it…
Synthesizing music
How to do it…
Extracting frequency domain features
How to do it…
Building Hidden Markov Models
How to do it…
Building a speech recognizer
How to do it…
8. Dissecting Time Series and Sequential Data
Introduction
Transforming data into the time series format
How to do it…
Slicing time series data
How to do it…
Operating on time series data
How to do it…
Extracting statistics from time series data
How to do it…
Building Hidden Markov Models for sequential data
Getting ready
How to do it…
Building Conditional Random Fields for sequential text data
Getting ready
How to do it…
Analyzing stock market data using Hidden Markov Models
How to do it…
9. Image Content Analysis
Introduction
Operating on images using OpenCV-Python
How to do it…
Detecting edges
How to do it…
Histogram equalization
How to do it…
Detecting corners
How to do it…
Detecting SIFT feature points
How to do it…
Building a Star feature detector
How to do it…
Creating features using visual codebook and vector quantization
How to do it…
Training an image classifier using Extremely Random Forests
How to do it…
Building an object recognizer
How to do it…
10. Biometr...

Table of contents

  1. Python Machine Learning Cookbook

Frequently asked questions

Yes, you can cancel anytime from the Subscription tab in your account settings on the Perlego website. Your subscription will stay active until the end of your current billing period. Learn how to cancel your subscription
No, books cannot be downloaded as external files, such as PDFs, for use outside of Perlego. However, you can download books within the Perlego app for offline reading on mobile or tablet. Learn how to download books offline
Perlego offers two plans: Essential and Complete
  • Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
  • Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Both plans are available with monthly, semester, or annual billing cycles.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 990+ topics, we’ve got you covered! Learn about our mission
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more about Read Aloud
Yes! You can use the Perlego app on both iOS and Android devices to read anytime, anywhere — even offline. Perfect for commutes or when you’re on the go.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app
Yes, you can access Python Machine Learning Cookbook by Prateek Joshi in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.