The 4 courses that I took as a self-taught ML engineer
By: Mykhailo Kushnir
Tags: Self Improvement, Online Learning, Python, Education, Machine Learning

Sharing my online learning experience from being a newbie to being a more educated newbie.

Photo by AltumCode on Unsplash

Preconditions

My university specialization was rather distant from machine learning. It was mostly related to designing automation systems with some soft touch of programming and algorithms. While it wasn’t directly helpful in my further career, it gave me a good base for self-learning in various ways of Computer Science.

While I knew some of the good old algorithms from my students’ days, I’ve had not much understanding of existing frameworks and modern architectures, so lack of this knowledge dictated my selection of courses. Looking back on it now, I think it was a rather lucky and useful choice, so feel free to copy it if you’re at the beginning of this path.

Natural Language Processing from HSE University

Course: https://www.coursera.org/learn/language-processing

Price: 49$/month

Estimated duration to pass: 4–5 weeks

Probably, the best course I’ve paid for because it gave me a solid understanding of the main principles in NLP, like language modeling, POS tagging, sequence to sequence tasks, etc.

It is also structured very conveniently with graduation exercise as building useful application (telegram bot) in the end. In my own course about data scraping, I have the same layout of material, as I think that just learning stuff in our days is not enough — one needs to get a working tool in the end.

I’ve made a detailed review of this course in my first medium post, and I’d still recommend this course for novices.

How to Win a Data Science Competition from HSE University

Course: https://www.coursera.org/learn/competitive-data-science?specialization=aml

Price: 49$/month

Estimated duration to pass: 5–6 weeks

I’ve never been a Kaggle competition participant and never really aimed for it, but this course still was beneficial to me. It is provided by well-known Kaggle grandmasters, and its idea was to teach unconventional approaches to data learning tasks. Yes, sometimes it means “cheats”. In practical Machine Learning, it’s sometimes important to understand when you can replace a sophisticated model with just a good enough EDA. This course gives tips on how to discover such cases.

Fastai Course V3

Course: https://course19.fast.ai/

Price: Free

Estimated duration to pass: 8–10 weeks

That would be the first and only free course on this list and an appreciation to the open-source community for designing such a huge piece of work. Despite being created by fastai organization, it is not concentrated around their products like ULMFiT, but rather gives you a good explanation around common ML practics.

Despite many advancements in the ML branch. In this course you’ll dive deep into concepts like:

Regularization

Learning rate search

Dynamic learning rate

and others;

It is also useful for novices as it provides tutorials around environment setup on Google Cloud Platform. If you don't have good enough resources at home, using cloud-based solutions is optimal at the begging and taking this course is worth just learning that particular topic.

Natural Language Processing Specialization by DeepLearning.ai

Specialization: https://www.coursera.org/learn/sequence-models-in-nlp

Price: 49$/month

Estimated duration to pass: <4 weeks per course

One of the recent ones. Gives you an understanding of modern architectures like transformers. As in most deeplearning.ai courses you’d have to do the “dirty work” of designing the network with your own hands. It is also quite complex in terms of exercises you’d have to pass, so if you’re up to something challenging — feel free to jump in.

Summary

At the end of the day, my self-education resources were biased towards stuff needed (NLP) at my current place of work. I’m fully aware that it’s not complete in any sense and this blog is a particular example of my everlasting strive to improve. Please, let me know what you think about this list and share your own learning experience.