Machine Learning in Three Steps: How to Efficiently Learn It

I have observed two extreme approaches when it comes to aspiring data scientists attempting to learn machine learning algorithms. The first approach involves learning all the intricacies of the algorithms and implementing them from scratch to gain true mastery. The second approach, on the other hand, assumes that the computer will "learn" on its own, rendering the need for the individual to learn the algorithms unnecessary. This leads some to only rely on tools such as the package lazypredict. It is realistic to take an approach between the two extremes when learning machine learning algorithms. However, the question remains, where to start? In this article, I will categorize machine learning algorithms into three categories and provide my humble opinion on what to begin with and what can be skipped. Starting out in machine learning can be overwhelming due to the multitude of available algorithms. Linear regression, support vector machines (SVM), gradient descent, gradient boosting, decision trees, LASSO, ridge, grid search, and many more are some of the algorithms that come to mind when posed with the question.