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Python Machine Learning Blueprints

Book Description

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

About This Book

  • Put machine learning principles into practice to solve real-world problems

  • Get to grips with Python's impressive range of Machine Learning libraries and frameworks

  • From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline

  • Who This Book Is For

    Python programmers and data scientists - put your skills to the test with this practical guide dedicated to real-world machine learning that makes a real impact.

    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

  • In Detail

    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.

    Style and approach

    Packed with real-world projects, this book takes you beyond the theory to demonstrate how to apply machine learning techniques to real problems.

    Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

    Table of Contents

    1. Python Machine Learning Blueprints
      1. Python Machine Learning Blueprints
      2. Credits
      3. About the Author
      4. About the Reviewer
      5. www.PacktPub.com
        1. eBooks, discount offers, and more
          1. Why subscribe?
          2. Free access for Packt account holders
      6. Preface
        1. What this book covers
        2. What you need for this book
        3. Who this book is for
        4. Conventions
        5. Reader feedback
        6. Customer support
        7. Downloading the example code
        8. Errata
        9. Piracy
        10. Questions
      7. 1. The Python Machine Learning Ecosystem
        1. The data science/machine learning workflow
          1. Acquisition
          2. Inspection and exploration
          3. Cleaning and preparation
          4. Modeling
          5. Evaluation
          6. Deployment
        2. Python libraries and functions
          1. Acquisition
          2. Inspection
            1. The Jupyter notebook
            2. Pandas
            3. Visualization
              1. The matplotlib library
              2. The seaborn library
          3. Preparation
            1. Map
            2. Apply
            3. Applymap
            4. Groupby
          4. Modeling and evaluation
            1. Statsmodels
            2. Scikit-learn
          5. Deployment
        3. Setting up your machine learning environment
        4. Summary
      8. 2. Build an App to Find Underpriced Apartments
        1. Sourcing the apartment listing data
          1. Pulling listing data using import.io
        2. Inspecting and preparing the data
          1. Analyzing the data
          2. Visualizing the data
        3. Modeling the data
          1. Forecasting
          2. Extending the model
        4. Summary
      9. 3. Build an App to Find Cheap Airfares
        1. Sourcing airfare pricing data
        2. Retrieving the fare data with advanced web scraping techniques
        3. Parsing the DOM to extract pricing data
          1. Identifying outlier fares with clustering techniques
        4. Sending real-time alerts using IFTTT
        5. Putting it all together
        6. Summary
      10. 4. Forecast the IPO Market using Logistic Regression
        1. The IPO market
          1. What is an IPO?
          2. Recent IPO market performance
          3. Baseline IPO strategy
        2. Feature engineering
        3. Binary classification
        4. Feature importance
        5. Summary
      11. 5. Create a Custom Newsfeed
        1. Creating a supervised training set with the Pocket app
          1. Installing the Pocket Chrome extension
          2. Using the Pocket API to retrieve stories
        2. Using the embed.ly API to download story bodies
        3. Natural language processing basics
        4. Support vector machines
        5. IFTTT integration with feeds, Google Sheets, and e-mail
          1. Setting up news feeds and Google Sheets through IFTTT
        6. Setting up your daily personal newsletter
        7. Summary
      12. 6. Predict whether Your Content Will Go Viral
        1. What does research tell us about virality?
        2. Sourcing shared counts and content
        3. Exploring the features of shareability
          1. Exploring image data
          2. Exploring the headlines
          3. Exploring the story content
        4. Building a predictive content scoring model
        5. Summary
      13. 7. Forecast the Stock Market with Machine Learning
        1. Types of market analysis
        2. What does research tell us about the stock market?
        3. How to develop a trading strategy
          1. Extending our analysis period
          2. Building our model with a support vector regression
            1. Evaluating our model's performance
          3. Modeling with Dynamic Time Warping
        4. Summary
      14. 8. Build an Image Similarity Engine
        1. Machine learning on images
        2. Working with images
        3. Finding similar images
        4. Understanding deep learning
        5. Building an image similarity engine
        6. Summary
      15. 9. Build a Chatbot
        1. The Turing test
        2. The history of chatbots
        3. The design of chatbots
        4. Building a chatbot
        5. Summary
      16. 10. Build a Recommendation Engine
        1. Collaborative filtering
          1. User-to-user filtering
          2. Item-to-item filtering
        2. Content-based filtering
        3. Hybrid systems
        4. Building a recommendation engine
        5. Summary