I recently read the books "Applied Machine Learning and AI for Engineers" and "Introducing MLOps", and I took some notes to make a quick summary of all the stuff packed into these books. In this post, I'm sharing my takeaways, from the basics of supervised and unsupervised learning to the more complex areas like deep learning and natural language processing, as well as the core ideas behind MLOps.
