✅ Last checked on March 21, 2026 by WebHelperApp
Description
What You’ll Learn
- Overview of Supervised, Unsupervised, and Reinforcement Learning
Requirements
- Interest in machine learning
Description
Course Outcome:
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.
Course Audience:
This course has been designed for ALLÂ 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)Â 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
Python Demos:
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)
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