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Python Machine Learning Blueprints: Intuitive data projects you can relate to, by Alexander T. Combs
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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
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
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- Sales Rank: #406118 in Books
- Published on: 2016-07-29
- Released on: 2016-07-29
- Original language: English
- Dimensions: 9.25" h x .75" w x 7.50" l, 1.26 pounds
- Binding: Paperback
- 332 pages
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 fulltime lead instructor for a data science immersive program in New York City.
Most helpful customer reviews
9 of 9 people found the following review helpful.
Great book for new to advanced Python developers interested in machine learning.
By Taran Rampersad
I'd been working on some personal projects in Python 3 that related to Machine Learning and Natural Language Processing when I saw the author's post on LinkedIn, so I picked it up. As it happened, it saved me some time and trouble, as well as showed me ways I could have avoided some of the same.
You'll need Python 3.x for this book. The environment I used while working through this book was Anaconda package (as I said, I was already working on things), and I had no problems. You do need a very basic understanding of Python; if you lack this you may be puzzled by some references to 'pip' and 'slicing' - but these are easily remedied by searching, should you need to. I'd rate myself as high-intermediate with Python, and I had no real issues with the book (other than my own lack of sleep in one instance).
The book itself is easy to read, well supported with files, and work through examples that are interesting and well thought out. This is a workbook, and the title is appropriate since these are blueprints. The blueprints include:
* building an app to find underpriced apartments,
* building an app for finding cheap airfares,
* forecasting the IPO Market using logistic regression,
* creating a custom newsfeed,
* predicting when your content will go viral,
* forecasting the stock market with machine learning,
* Build an image similarlity engine,
* Building a chatbot,
* Building a recommendation engine.
I haven't had the time to do everything there, though I've read through all of them - I am still focused on my personal projects. The projects I had fun with were creating the custom newsfeed, building a similarity engine and writing a chatbot. This book made it simple; out there in the Wild of the Internet it's not always clear on how to piece these things together, Just Python 2.x - 3.x can be a challenge for newcomers and experienced alike.
What the author does, successfully, is take a lot of the guesswork and research on the topics in the book and make them easily done by someone with basic Python experience.
I highly recommend this book for anyone that wants to dive into machine learning and is willing to work through the examples - at least the ones that interest them. With code on Github or downloadable from the Publisher's site, it's easy to proceed at the reader's own rate.
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