Basic Course Description
This course is for you if you want to have a real feel of the clustering algorithms without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of classroom theory on the subject but could never got a change or figure out how to implement and solve data science problems with it.
The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in Python which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide.
Below is the brief outline of this course.
- Segment 1: Introduction to course
- Segment 2: KMeans Clustering
- Segment 3: Mean Shift Clustering
- Segment 4: DBSCAN Clustering
- Segment 5: Hierarchical Clustering
- Segment 6: HDBSCAN Clustering
- Segment 7: Applications of Clustering
Your Benefits and Advantages:
If you do not find the course useful, you are covered with 20 days money back guarantee, full refund, no questions asked!
- You will be sure of receiving quality contents since the instructors has already many courses on Data Science on Simpliv
- You have lifetime access to the course.
- You have instant and free access to any updates i add to the course.
- You have access to all Questions and discussions initiated by other students.
- You will receive my support regarding any issues related to the course.
Check out the curriculum and Freely available lectures for a quick insight.
It's time to take Action!
Click the "Take This Course" button at the top right now!
..Time is limited and Every second of every day is valuable...
We are excited to see you in the course!
Dr. Nouman Azam
Who this course is for:
- Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
- Anyone who want to analyze the data
- You should have a little know how of python and jupytor
- Python must be installed on your computer
- How to implement different clustering algorithms in python
- How to handle issues of varying cluster sizes, densities, shapes and noise
- When to use a specific algorithm
- Take away code templates
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