Machine Learning 360

Building core concepts in statistical / machine learning to succeed in job interviews and beyond. Weekly video lectures and office hours available.

Course Summary

This course builds conceptual understanding of machine learning and its commonly used techniques.  With a focus on understanding what a particular tool does and why, this course is not a cookbook or recipe of practical machine learning. We believe, building the foundation is a precursor to success in job interviews and beyond. This course will build that foundation.

The course will closely follow the contents of Gareth James's Introduction to Statistical Learning book referenced below.

COURSE SCHEDULE
Weekly recorded video and scheduled office hours.
This is a self-paced course. You learn at your convenience. To make the most out of this course, try to separate 2 hours each week to watch the lectures and review the assigned readings.

VIDEO POSTING SCHEDULE
Each week, the lectures will be posted on this YouTube playlist
https://youtube.com/playlist?list=PLoQX2L-ATsPnuu-Cf_fl-YY43-NJaWZxO

All the lecture videos and reading materials will also be available in this course environment. Thus, you only need to watch out for email notifications from this course. Make sure to check your spam folder in case you do not receive an email from me

OFFICE HOURS
Since the lectures are self-paced, you would need someone to help you if you have any questions about the lectures. Fortunately, I will be holding office hours to answer any questions you may have. Detailed schedule will be posted later.

HOW TO CONTACT ME
Make sure to read all the FAQ and course related notifications first. You can email me directly at [email protected] if you have questions that are not covered. I will try to respond within 24-48 hours if it needs a response. If you do not receive my response, make sure to read the FAQ section below.

Course Curriculum

References

 An Introduction to Statistical Learning

Gareth James
Daniela Witten
Trevor Hastie
Rob Tibshirani

https://www.statlearning.com/

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Second Edition

​Trevor Hastie
Robert Tibshirani
Jerome Friedman

https://web.stanford.edu/~hastie/ElemStatLearn/

Enayetur Raheem, Ph.D.

ডেটা সায়েন্টিস্ট ও পরিসংখ্যানবিদ। পরিসংখ্যান পাঠশালা-র প্রতিষ্ঠাতা ও প্রধান প্রশিক্ষক। বর্তমানে প্রিন্সিপাল ডেটা সায়েন্টিস্ট হিসেবে আমেরিকায় একটি বায়োটেক কোম্পানিতে কর্মরত।

ড. রহীম আমেরিকায় বেশ কয়েকটি বিশ্ববিদ্যালয়ে গ্র্যাজুয়েট লেভেলে পরিসংখ্যান, হেলথ ইনফরমেটিক্স ও মেশিন লারনিং কোর্স পড়িয়েছেন। বিশ্ববিদ্যালয় এবং হেলথকেয়ার ইন্ডাস্ট্রি মিলিয়ে দেড়যুগেরও বেশী অর্জিত অভিজ্ঞতা তিনি নবীন শিক্ষার্থীদের মাঝে শেয়ার করতে চান।

Frequently Asked Questions (FAQ)

The primary mode of lecture will be in Bangla. The course is targeted for Bangla speaking audience and the core objective is the conceptual understanding. Needless to say, one can truly understand something only in his/her native language.

However, the content of the course and all the materials are in English. 

The course is 100% free. No catch. No string attached.

This course is most appropriate for advanced undergraduates (3 year and beyond) or master's students in mathematics, statistics, physics, or related quantitative disciplines.

Anyone in other disciplines may also benefit out of this course if they wish to use statistical learning / machine learning in their projects.

This course may not be advanced enough for those who already know the concepts.

This course will explain the core concepts in great details. Teaching programming is not the goal. Students who wish to implement the concepts learned in this course would be expected to write computer programs or follow programs written by others.

The course will use codes written in R and Python. Both will be used to demonstrate the concepts. The course will not teach how to program in R or Python or any language, for that matter.

Please email [email protected] if you have questions not covered in the FAQ.

Course Pricing

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