These are hands-on activities using tools like R, Python, Tensorflow to explore specific models or questions. Some require installation of the tools (like R and R Studio) while others run in the cloud within a browser using interactive environments like Jupyter Notebook.
- Intro to Data Science, part 1
- https://www.youtube.com/watch?v=32o0DnuRjfg
- Intro to Data Science, Github files repository
- https://github.com/EasyD/IntroToDataScience
- Intro to Data Science, part 2
- https://www.youtube.com/watch?v=u6sahb7Hmog
- MentalEdge course: “mentaledge.us” will get you there
Google Machine Learning Crash Course: A multi-session, guided course in supervised learning. Outstanding, but rapid (may be hard to do independently — great with support or in a group). All exercises are web-based. None require downloading or configuring tools.: https://developers.google.com/machine-learning/crash-course/
Linear Regression Model: Prediction of Fuel Efficiency using Auto MPG data (tensorflow in Jupyter Notebook): https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_regression.ipynb