20 Questions to Ace Before Getting a Machine Learning Job. An Introduction to machine learning thanks to a tweet
Call it a lucky find on Twitter. It definitively is. Santiago tweeted 20 questions you need to ace before getting a machine learning job. I figured I’d use these questions to understand developers’ work better and maybe get a glimpse into future applications.
The first questions were about various basic concepts of machine learning. Let’s imagine, for example, that we are given a puzzle as a gift. How do you put it together? Do you need the finished puzzle poster as a basis, or do you put the edge together first? Do you try to sort the colors? Depending on what kind of information you have, it makes sense to choose a different method and, therefore, a different algorithm.
The same applies to deep learning methods such as supervised, unsupervised, semi-supervised, and reinforcement learning. A scientist decides on a training model for its algorithms depending on the available data and the research question. He named this part of his tweet the “warming up” phase. A key takeaway for non-techies:
In this phase, he asks questions concerning supervised learning methods. How do we learn when supervised? The teacher labels things, and we understand these things to be true (e.g., knowing all presidents by heart). In machine learning, this means that the algorithm learns on a labeled dataset to evaluate the training data. So when the question arises when to use classification over regression problems, the two main areas where supervised learning is useful are being brought up. According to Isha Salian, “classification problems ask the algorithm to predict a discrete value, identifying the input data as a member of a particular class or group, regression problems look at continuous data.” Therefore, we need to remember as non-techies:
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You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.