Key Features
- 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
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.
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
About the Author
Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.
Table of Contents
- The Python Machine Learning Ecosystem
- Build an App to Find Underpriced Apartments
- Build an App to Find Cheap Airfares
- Forecast the IPO Market using Logistic Regression
- Create a Custom Newsfeed
- Predict whether Your Content Will Go Viral
- Forecast the Stock Market with Machine Learning
- Build an Image Similarity Engine
- Build a Chatbot
- Build a Recommendation Engine
The book is available here.
DSC Resources
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
