This material provides an introduction to Machine Learning with a practical example in Python.
Type of Material:
A series of Video Tutorials on YouTube
Recommended Uses:
It can be used as individual self-paced material.
Technical Requirements:
- Web browser and Internet connection.
- Programming environment with Phyton in order to test the exercises
Identify Major Learning Goals:
Upon the completion of the tutorial, students should be able to 1) describe the idea of machine learning; 2) describe the basic workflow of machine learning.
Target Student Population:
It targets undergraduate and postgraduate students.
Prerequisite Knowledge or Skills:
Programming skills and familiarity with Phyton.
Content Quality
Rating:
Strengths:
- Well explained video lecture, with relevant examples.
- It shows exercises and the expected outcome.
Concerns:
It mainly discusses the overview of the machine learning. It can only serve as an introduction.
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
- As the video lecture incorporates examples and exercises, students can test and try the concepts explained.
- it incorporates easy exercises
- it is well explained
The material can effectively raise students' interest to machine learning. It provides the basic concepts to students
Concerns:
- If the students don't have access to a programming environment then the content cannot be tested.
- If they face a problem with the programming they might need guided assistance (e.g. teacher)
Ease of Use for Both Students and Faculty
Rating:
Strengths:
- The video lecture is easy to access on the YouTube website.
The material is easy to learning and visually appealing. It can effectively convey the idea of machine learning to learners.
Concerns:
- Students need to have a programming environment to test the exercises
Other Issues and Comments:
- This is the first video of a series of video lectures.
Creative Commons:
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