Python Machine Learning Blueprints: Intuitive data projects you can relate to

An approachable guide to applying advanced machine learning methods to everyday problems

Python Machine Learning Blueprints: Intuitive data projects you can relate to

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Alexander T. Combs

5 customer reviews
An approachable guide to applying advanced machine learning methods to everyday problems
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Book Details

ISBN 139781784394752
Paperback332 pages

Book Description

Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it?

Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice.

You’ll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling.

That way you’re never left floundering in theory – you’ll be simply collecting and analyzing data in a way that makes a real impact.

Table of Contents

Chapter 1: The Python Machine Learning Ecosystem
The data science/machine learning workflow
Python libraries and functions
Setting up your machine learning environment
Summary
Chapter 2: Build an App to Find Underpriced Apartments
Sourcing the apartment listing data
Inspecting and preparing the data
Modeling the data
Summary
Chapter 3: Build an App to Find Cheap Airfares
Sourcing airfare pricing data
Retrieving the fare data with advanced web scraping techniques
Parsing the DOM to extract pricing data
Sending real-time alerts using IFTTT
Putting it all together
Summary
Chapter 4: Forecast the IPO Market using Logistic Regression
The IPO market
Feature engineering
Binary classification
Feature importance
Summary
Chapter 5: Create a Custom Newsfeed
Creating a supervised training set with the Pocket app
Using the embed.ly API to download story bodies
Natural language processing basics
Support vector machines
IFTTT integration with feeds, Google Sheets, and e-mail
Setting up your daily personal newsletter
Summary
Chapter 6: Predict whether Your Content Will Go Viral
What does research tell us about virality?
Sourcing shared counts and content
Exploring the features of shareability
Building a predictive content scoring model
Summary
Chapter 7: Forecast the Stock Market with Machine Learning
Types of market analysis
What does research tell us about the stock market?
How to develop a trading strategy
Summary
Chapter 8: Build an Image Similarity Engine
Machine learning on images
Working with images
Finding similar images
Understanding deep learning
Building an image similarity engine
Summary
Chapter 9: Build a Chatbot
The Turing test
The history of chatbots
The design of chatbots
Building a chatbot
Summary
Chapter 10: Build a Recommendation Engine
Collaborative filtering
Content-based filtering
Hybrid systems
Building a recommendation engine
Summary

What You Will Learn

  • Explore and use Python's impressive machine learning ecosystem
  • Successfully evaluate and apply the most effective models to problems
  • Learn the fundamentals of NLP - and put them into practice
  • Visualize data for maximum impact and clarity
  • Deploy machine learning models using third party APIs
  • Get to grips with feature engineering

Authors

Table of Contents

Chapter 1: The Python Machine Learning Ecosystem
The data science/machine learning workflow
Python libraries and functions
Setting up your machine learning environment
Summary
Chapter 2: Build an App to Find Underpriced Apartments
Sourcing the apartment listing data
Inspecting and preparing the data
Modeling the data
Summary
Chapter 3: Build an App to Find Cheap Airfares
Sourcing airfare pricing data
Retrieving the fare data with advanced web scraping techniques
Parsing the DOM to extract pricing data
Sending real-time alerts using IFTTT
Putting it all together
Summary
Chapter 4: Forecast the IPO Market using Logistic Regression
The IPO market
Feature engineering
Binary classification
Feature importance
Summary
Chapter 5: Create a Custom Newsfeed
Creating a supervised training set with the Pocket app
Using the embed.ly API to download story bodies
Natural language processing basics
Support vector machines
IFTTT integration with feeds, Google Sheets, and e-mail
Setting up your daily personal newsletter
Summary
Chapter 6: Predict whether Your Content Will Go Viral
What does research tell us about virality?
Sourcing shared counts and content
Exploring the features of shareability
Building a predictive content scoring model
Summary
Chapter 7: Forecast the Stock Market with Machine Learning
Types of market analysis
What does research tell us about the stock market?
How to develop a trading strategy
Summary
Chapter 8: Build an Image Similarity Engine
Machine learning on images
Working with images
Finding similar images
Understanding deep learning
Building an image similarity engine
Summary
Chapter 9: Build a Chatbot
The Turing test
The history of chatbots
The design of chatbots
Building a chatbot
Summary
Chapter 10: Build a Recommendation Engine
Collaborative filtering
Content-based filtering
Hybrid systems
Building a recommendation engine
Summary

Book Details

ISBN 139781784394752
Paperback332 pages
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