English | Size: 1.65 GB
Genre: eLearning
What you’ll learn
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
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
Who this course is for:
Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
Anyone who want to analyze the data
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