New Machine Learning Specialisations for Advanced Practitioners
Today, you don’t need to go to a university or a college to pursue a career in machine learning or any data-driven domain but you need a plan and a roadmap to guide yourself.
Once you have charted your own learning roadmap with a goal in mind, the next step is to screen the right set of courses that fit well into your roadmap and you then start building your foundation around those courses.
And this week, I wanted to share a few advanced-level specializations and courses that are on my list and that can help you with your search for the right course or the right career track.
So, here you go:
Machine Learning in Production Specialization by Andrew Ng
Discussions on topics like ML engineering and MLOps are providing momentum to development in standardizing practices to productionize ML models in the form of tools and techniques. And these novel engineering methods in the machine learning arena are not very well documented and taught.
With this specialization, by a few pioneers of the field, you can train yourself to be capable of writing production-ready, ML-powered applications. The specialization claims to help you with:
Design an ML production system end-to-end
Build data pipelines by gathering, cleaning, and validating datasets.
Establish a model baseline and continuously improve a productionized ML application.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
This is a 5-month long specialization with 4 courses targeting every phase of ML Engineering.
You can go to individual courses on Coursera and audit them to have free access.
Andrew has done a remarkable job in democratizing Machine Learning for everyone and this specialization might be able to do the same for ML Engineering.
I’ll write a detailed review on this once I complete the specialization. Let me know how you find it if you finish it before me.
Course on Transformers from Hugging Face🤗
For all you deep learning enthusiasts, there is a new course on Transformers that is released by Hugging Face. This is looking very promising as I really enjoyed going through Practical Deep Learning for Coders from the same Author(Sylvian Gugger).
Anyone who is looking to dive deeper into training NLP models should definitely check this course out.
The course curriculum covers:
Introduction to transformers, fine-tuning pre-trained models, and sharing the models and tokenizers.
The dataset and the tokenizers library.
How to develop specialized architectures, speed up training, and writing custom training loops.
The amazing part is that the course is absolutely free. Do it at your own pace and try to build a complete application instead of just a trained model.
The best way to learn something is by using it in your own project to solve your own problems.
MLOps Series from Made with ML by Goku Mohandas
I discovered Goku’s content a month ago and I am amazed by the amount of effort he is putting into developing this course on MLOps. It requires a good deal of work to actually hone the skills for each phase of MLOps because there are so many moving parts in an actual product as compared to a model.
He has divided the entire course into a number of subdomains:
Product
Data
Modeling
Scripting
APIs and interfaces
Testing & Reproducibility
Production Systems
I highly recommend following his course, it is absolutely free and you can interact with similar minds by being a part of the community.
Note: A common mistake beginners(myself included) make is they keep hopping from one course to another without actually completing the deliverables of any single one. It is fine if you are exploring but when you set your sight on a course, try to build something out of it rather than simply following the lectures and doing the spoon-fed exercises.
Interesting read of the week - A Project of One’s Own
I read this post titled - A project of One’s Own by Paul Graham(commonly known as the co-founder of Ycombinator) last week and it not just validated but strengthened my notions about working on projects voluntarily even if it is a side gig.
An excellent essay on the importance of working on ambitious projects voluntarily.
Doing projects of your own with complete control and voluntary actions lets you feel the freedom and instigates curiosity to dive deeper.
It teaches you a lot more than any job that someone else has told you to do.
Feel free to reach out if you have any questions, suggestions, or thoughts!