What is Machine Learning?, An overview of Supervised, Unsupervised, and Reinforcement Learning with Python Demos.
Learners completing this course will be able to give definitions and explain the types of problems that can be solved by the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
Course Topics and Approach:
This course gives a gentle introduction to the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning. The goal is to explain the key ideas using examples with many plots and animations and little math, so that the material can be accessed by a wide range of learners. The lectures are supplemented by Python demos, which show machine learning in action. Learners are encouraged to experiment with the course demo codes. Additionally, information about machine learning resources is provided, including sources of data and publicly available software packages.
This course has been designed for ALL LEARNERS!!!
- Course does not go into detail into the underlying math, so no specific math background is required
- No previous experience with machine learning is required
- No previous experience with Python (or programming in general) is required to be able to experiment with the course demo codes
Teaching Style and Resources:
- Course includes many examples with plots and animations used to help students get a better understanding of the material
- All resources, including course codes, Powerpoint presentations, info on additional resources, can be downloaded from the course Github site
There are several options for running the Python demos:
- Run online using Google Colab (With this option, demo codes can be run completely online, so no downloads are required. A Google account is required.)
- Run on local machine using the Anaconda platform (This is probably best approach for those who would like to run codes locally, but don’t have python on their local machine. Demo video shows where to get free community version of Anaconda platform and how to run the codes.)
- Run on local machine using python (This approach may be most suitable for those who already have python on their machines)
- Section 5: update course codes, Powerpoint presentations, and videos so that codes are compatible with more recent versions of the Anaconda platform and plotting package