This course will introduce you to the world of unsupervised machine learning through clustering & dimensionality reduction techniques. The contents of the course are concise yet comprehensive, also challenging yet straightforward. At first, you will be given a solid mathematical understanding of each algorithm. Also, we will take you through each calculation of each step of each algorithm using toy datasets. Then hands-on sessions will show you how to use the algorithms with python and real data. Finally, there will be quizzes to check your understanding.
Prerequisites:
College-level algebra and geometry will be enough for the theoretical parts. For example, you should be able to calculate the distance between two points, and you should know the equations of a straight line. After that, for hands-on sessions, you should have a basic familiarity with Python and its libraries like Sklearn, Pandas, Matplotlib, etc. Even if you do not have any of the mentioned prerequisite knowledge, you are guaranteed to understand the concepts and intuitions behind everything we will discuss.
Course Outline:
- Introduction
- Types of Machine Learning
- Clustering Algorithms
- KMeans Clustering
- Hands-on KMeans Clustering with Python
- Hierarchical Clustering
- Hands-on Hierarchical Clustering with Python
- Dimensionality Reduction
- Principal Component Analysis
- Hands-on Principal Component Analysis with Python
- Conclusion
Benefits of the Machine Learning: Unsupervised course:
- 11 lessons in 1 hour
- Self-assessment at the end of the course
- Forum for further discussion & problem solving
- Certificates from HRDI & Skill.Jobs
GoEdu Learner’s Manual
Course Features
- Lectures 11
- Quizzes 1
- Duration 1.5 Hours
- Skill level All levels
- Language Bengali
- Students 4
- Certificate Yes
- Assessments Self
