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 30 day 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 udemy.
- 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
Student Testimonials for Dr. Nouman Azam!
This is the second Udemy class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.
Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!
The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!
Student Testimonials! who are also instructors in the MATLAB category
"Concepts are explained very well, Keep it up Sir...!!!"
Engr Muhammad Absar Ul Haq instructor of course " Matlab keystone skills for Mathematics (Matrices & Arrays) "
Dr. Nouman Azam is an Assistant Professor in Computer Science. He teaches online courses related to MATLAB Programming to more than 10,000 students on different online platforms.
The focus in these courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In my courses, you will find topics such as MATLAB programming, designing GUI's, data analysis and visualization.
Machine learning techniques using MATLAB is one of my favorite topics. During my research career I explore the use of MATLAB in implementing machine learning techniques such as bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems and medical decision making.
StartCode and Data
StartKMeans intuition (12:18)
StartChoosing the right number of clusters (15:35)
StartKMeans in Python (Part 1) (18:35)
StartKMeans in Python (Part 2) (9:41)
StartKMeans Limitations - (Part 1 - Clusters with different sizes) (10:30)
StartKMeans Limitations - (Part 2 - Clusters with non spherical shapes) (10:37)
StartKMeans Limitations - (Part 3 - Clusters with varying densities) (5:22)