Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models.
The book covers everything you need to know about algorithm families and helps you build must-have skills such as building interpretable models and avoiding bias in data. By the end of the book, you’ll be able to confidently use conda and Anaconda Navigator to manage dependencies, and you’ll have gained a thorough understanding of the end-to-end data science workflow.
This free chapter, “Dealing with Common Data Problems,” covers the following topics:
- Dealing with too much data
- Finding and correcting incorrect data entries
- Working with categorical values with one-hot encoding
- Feature scaling
- Working with date formats
You can purchase the full book here.
About the Author
Dan Meador”Professional nerd” is Dan Meador’s response whenever anyone asks what he does for a living. It wasn’t always so clear-cut when he played football for the Arkansas Razorbacks as he was getting his degree in computer engineering, but nowadays the pendulum has swung pretty clearly into the “nerd” classification. Over a decade working in Fortune 5 companies and later finding startups to be more liking, he’s seen firsthand how the power of data can help ask better questions and guide better solutions.
He holds a patent for his work on AI systems and has been able to grow his experience in AI/ML by building AutoML solutions. His journey has also taken him to the Pentagon where he was able to present his work on AI systems. Dan currently is an engineering manager at Anaconda helping the conda team be a steward of open source.