Common Errors in Machine Learning due to Poor Statistics Knowledge
You can learn by doing, or you can learn from someone else’s mistakes. This is the case of the later. Certainly useful to avoid the same mistakes.
Continuous Delivery for Machine Learning
This one is pretty heavy read. But if you’re into Martin Fowler’s stuff, you know they are ‘the standard’. This one covers what it takes to apply continuous delivery in machine learning model. The CD for machine learning have similarities to software’s CD, but there are few keys differences as well.
Introduction to Machine Learning in C# with ML.NET
If you want to start learning Machine Learning but don’t know where to start, ML.NET is a good starter. With familiarity of C# and .NET, you could pick up ML.NET fairly quickly. This goes into details on how to get started with ML.NET, even covers Auto-ML!
Identify guiding principles for responsible AI in your business
Everyone thinks AI is cool, futuristic and can solve _almost_ all the problems. But not many think about the consequences, side effects and what it would take to build it right. In another word, a responsible AI. This mini-course go over what we need to consider in building AI.
Multiprocessing vs. Threading in Python: What Every Data Scientist Needs to Know
I like how Sumit gives intro to parallel computing, specifically multi processes vs threading, before he went dive into how it’s applicable in Python for modeling. Worth read even if you skip the Python part.