Free Python Machine Learning Blueprints: Intuitive Data Projects You Can Relate To Ebooks Online

                
              
Free 
Python Machine Learning Blueprints: Intuitive Data Projects You Can Relate To Ebooks Online
Key FeaturesPut machine learning principles into practice to solve real-world problemsGet to grips with Python's impressive range of Machine Learning libraries and frameworksFrom retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipelineBook DescriptionMachine 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 learnExplore and use Python's impressive machine learning ecosystemSuccessfully evaluate and apply the most effective models to problemsLearn the fundamentals of NLP - and put them into practiceVisualize data for maximum impact and clarityDeploy machine learning models using third party APIsGet to grips with feature engineeringAbout the AuthorAlexander 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 ContentsThe Python Machine Learning EcosystemBuild an App to Find Underpriced ApartmentsBuild an App to Find Cheap AirfaresForecast the IPO Market using Logistic RegressionCreate a Custom NewsfeedPredict whether Your Content Will Go ViralForecast the Stock Market with Machine LearningBuild an Image Similarity EngineBuild a ChatbotBuild a Recommendation Engine
File Size: 66113 KB
Print Length: 332 pages
Publisher: Packt Publishing; 1 edition (July 29, 2016)
Publication Date: July 29, 2016
Sold by:  Digital Services LLC
Language: English
ASIN: B01CID6IGQ
Text-to-Speech:  Enabled
X-Ray:     Not Enabled
Word Wise: Not Enabled
Lending: Not Enabled
Enhanced Typesetting:  Not Enabled
Best Sellers Rank: #299,703 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #65 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory   #125 in Kindle Store > Kindle eBooks > Computers & Technology > Programming > Python   #337 in Books > Computers & Technology > Programming > Languages & Tools > Python
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.
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