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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

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

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

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

Forecast the IPO Market Using Logistic Regression

Forecast the IPO Market Using Logistic Regression

Data cleansing and feature engineering

Binary classification with logistic regression

Generating the importance of a feature from our model

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

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

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

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

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

Build a Recommendation Engine

Build a Recommendation Engine

Collaborative filtering

Content-based filtering

Building a recommendation engine

What's Next?

Summary of the projects

Other Books You May Enjoy

Other Books You May Enjoy

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Extending the model

At this point, we have only examined the relationship between the ZIP code, bedrooms, and rental price. And while our model had some explanatory benefit, we had a minimal dataset and far too few features to adequately examine the complex world of real estate valuation.

Fortunately, however, if we were to add more data and features to the model, we could use the exact same framework to expand our analysis.

Some possible future extensions to explore would be utilizing data for restaurants and bars available from APIs such as Foursquare or Yelp, or walkability and transportation-proximity measures from providers such as Walk Score.

There are a number of ways to extend the model, and I suggest if you do pursue working on a project such as this that you explore a variety of measures. More data is released every day and, with it, models can only improve.

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