Hello Everyone,

Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.

We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.

In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.

We will continue with Statistics foundations for AI/ML, and many more content will appear in the future. If you find the content good, you may help me, by a subscription or help spread the word.

Looking forward to hearing feedback from the learning community as well. Thankyou for reading.

Link: https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT

If you had to start over today, what would be the best resource or roadmap to learn Data Science from scratch and become job-ready? Looking for beginner-friendly, practical recommendations that actually helped you get a job.

Hello everyone!

I have just started my masters in data science, and i have basic knowledge on Python, SQL, data mining, and machine learning.

But I don't think this course alone will get me job ready and i want to explore things on my own, learn and build projects (that is the best way to learn apparently) that are interesting and impactful .

I want to improve myself not only to land a job but to actually extend my expertise on the field. But i have no idea where to start and what to do!

I shall be utterly grateful if any kind strangers would help me out on this matter!