Day 36 of 100 Days of AI

I’m travelling for the next 6 days without my laptop so I’ll keep the posts very short given I’m on mobile.

No laptop means no code. However, I’ll work through this wonderful visual introduction to ML from the YouTube channel, StatQuest.

Key takeaways from the first two chapters:

• Supervised machine learning is broadly just about two things: classification predictions (e.g a binary prediction of whether a particular email is spam or not) and regressions (e.g. given some number of variables, can we predict a house price?)
• Unsupervised machine learning goes beyond that (e.g. clustering algorithms, neural networks).
  
• There are so many machine learning techniques that choosing which ones to use very much depends on the problem. We can also use techniques like cross-validation (k-fold, or leave-one-out etc) to measure which technique provides the best models.