Book Image

Python Machine Learning Blueprints - Second Edition

By : Alexander Combs, Michael Roman
Book Image

Python Machine Learning Blueprints - Second Edition

By: Alexander Combs, Michael Roman

Overview of this book

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

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Python Machine Learning Blueprints - Second Edition
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Jan, 2019 | 378 pages
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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
The Python Machine Learning Ecosystem
The Python Machine Learning Ecosystem
Data science/machine learning workflow
Python libraries and functions for each stage of the data science workflow
Setting up your machine learning environment
Summary
2
Build an App to Find Underpriced Apartments
Build an App to Find Underpriced Apartments
Sourcing apartment listing data
Inspecting and preparing the data
Visualizing our data
Visualizing the data
Modeling the data
Extending the model
Summary
3
Build an App to Find Cheap Airfares
Build an App to Find Cheap Airfares
Sourcing airfare pricing data
Retrieving fare data with advanced web scraping
Parsing the DOM to extract pricing data
Identifying outlier fares with anomaly detection techniques
Sending real-time alerts using IFTTT
Putting it all together
Summary
4
Forecast the IPO Market Using Logistic Regression
Forecast the IPO Market Using Logistic Regression
The IPO market
Data cleansing and feature engineering
Binary classification with logistic regression
Generating the importance of a feature from our model
Summary
5
Create a Custom Newsfeed
Create a Custom Newsfeed
Creating a supervised training set with Pocket
Using the Embedly API to download story bodies
Basics of Natural Language Processing
Support Vector Machines
IFTTT integration with feeds, Google Sheets, and email
Setting up your daily personal newsletter
Summary
6
Predict whether Your Content Will Go Viral
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
7
Use Machine Learning to Forecast the Stock Market
Use Machine Learning to Forecast the Stock Market
Types of market analysis
What does research tell us about the stock market?
How to develop a trading strategy
Building the regression model
Summary
8
Classifying Images with Convolutional Neural Networks
Classifying Images with Convolutional Neural Networks
Image-feature extraction
Convolutional neural networks
Building a convolutional neural network to classify images in the Zalando Research dataset, using Keras
Summary
9
Building a Chatbot
Building a Chatbot
The Turing Test
The history of chatbots
The design of chatbots
Building a chatbot
Sequence-to-sequence modeling for chatbots
Summary
10
Build a Recommendation Engine
Build a Recommendation Engine
Collaborative filtering
Content-based filtering
Hybrid systems
Building a recommendation engine
Summary
11
What's Next?
What's Next?
Summary of the projects
Summary
12
Other Books You May Enjoy
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Basics of Natural Language Processing

If machine learning models only operate on numerical data, how can we transform our text into a numerical representation? That is exactly the focus of Natural Language Processing (NLP). Let's take a brief look at how this is done.

We'll begin with a small corpus of three sentences:

  1. The new kitten played with the other kittens
  2. She ate lunch
  3. She loved her kitten

We'll first convert our corpus into a bag-of-words (BOW) representation. We'll skip preprocessing for now. Converting our corpus into a BOW representation involves taking each word and its count to create what's called a term-document matrix. In a term-document matrix, each unique word is assigned to a column, and each document is assigned to a row. At the intersection of the two is the count:

Sr. no.

the

new

kitten

played

with

other

kittens...

Previous Section
End of Section 4
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