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Beginning Data Science with Python and Jupyter / Villa, Chris.
- Format:
- Video
- Author/Creator:
- Villa, Chris, author.
- Galea, Alex, author.
- Language:
- English
- Subjects (All):
- Machine learning.
- Information visualization.
- Python (Computer program language).
- Electronic data processing.
- Genre:
- Electronic videos.
- Physical Description:
- 1 online resource (1 video file, approximately 2 hr., 49 min.)
- Edition:
- 1st edition
- Place of Publication:
- Packt Publishing, 2018.
- System Details:
- video file
- Summary:
- Perform reproducible data analyses with these data exploration tools About This Video Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets In Detail Getting started with data science doesn’t have to be an uphill battle. This step-by-step video course is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You’ll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.We'll start with understanding the basics of Jupyter and its standard features. You'll be analyzing an example of a data analytics report. After analyzing a data analytics report, next step is to implement multiple classification algorithms. We’ll then show you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. Finish up by learning to visualize these data interactively
- Participant:
- Presenter, Chris Dalla Villa.
- Notes:
- Online resource; Title from title screen (viewed September 28, 2018)
- Title from resource description page (Safari, viewed November 26, 2018).
- OCLC:
- 1076490700
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