When I first started learning about ML, I found myself at the disappointing dead ends of an endless maze. For a long time, I never quite successfully got to the machine learning components because I was always stuck in the maze of for loops, while loops, comprehensions, containers, and functions. At the time, the lack of progress was demotivating. My many dead python repos silently testify of this.
To form a good mental model before beginning the long and arduous journey of learning ML, we must first get an overall view and then dig into more detailed objectives. Akin to mapping out a forest, getting the view of the landscape of the forest is the first step, followed by looking at the forest’s trees (i.e. trunk and big branches), and then and only then, you pay attention to the leaves and the twigs of the forest.

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A Product Manager’s Guide to Machine Learning: Cloud Machine Learning
1.40 GEEK