These are my favorite data science resources that I have studied in the past but I re-visit all the time to recall an specific topic or concept.
ISLR - Introduction to Statistical Learning in R1.
http://www-bcf.usc.edu/~gareth/ISL/ ▪ See the download book PDF link.
2.
https://github.com/JWarmenhoven/ISLR-python ▪ This has the exercises in python.
Coursera1. Machine Learning, Andrew Ng, Stanford.
https://www.coursera.org/learn/machine-learning2. Data Science Specialization (10 four-week courses all used R), John Hopkins University.
https://www.coursera.org/specializations/jhu-data-science3. Deep Learning Specialization (5 courses ~3 months, python, tensor flow, keras,….), Andrew Ng,
https://www.coursera.org/specializations/deep-learning3Blue1Brown1.
https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw2. This is a youtube channel that has the best explanations I've seen:
▪ Essense of Calculus
▪ Essense of Linear Algebra
▪ Neural Networks
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JORGE CASTANON
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Original Message:
Sent: 11-01-2018 09:32
From: Ena Bevrnja
Subject: IBM Community Give Back Campaign
Hello community members!
In the spirit of giving back this holiday season, the Data Science community will be sponsoring the Give Back Campaign over the next few weeks. The Give Back Campaign is open to all members of IBM Community and will be hosted here in the Global Data Science Forum.
To participate in giving back to the community:
- Upload your favorite and/or most valuable resource as an attachment to this thread. This could be your tried and true data tool, a shout out to your mentor, a book that you recommend, or a method that has given you successful results.
- Recommend the posts in the thread you found most valuable.
The member who receives the most recommends will receive an IBM Data Science gift bag with some great goodies! If you upload multiple resources, all your recommends will be tallied for a total score. The winner will be announced November 26th!
Go ahead - upload those resources, be creative and have fun. Let's spread the knowledge around!
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Ena Bevrnja
IBM Community Team
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#GlobalAIandDataScience
#GlobalDataScience