These are hot topics and every now and again people ask if they help you get a job. My answer is: I don't know!

Here is a quote from someone seemingly working in the field. I am only the messenger, so stay calm

It would be nice to get feedback from those working in the field.

In the past, it takes 15 years for a technology to emerge from the laboratories to production.

Here is a quote from someone seemingly working in the field. I am only the messenger, so stay calm

It would be nice to get feedback from those working in the field.

*Speaking as someone who has done machine learning and data science for businesses for almost 10 years now: no, absolutely not. In fact, you should forget that deep learning exists. Most of what you are hearing about deep learning is hype from the marketing departments of large companies rather than a realistic assessment of its importance. It is almost never used in production anywhere, and "knowing deep learning" will not get you a job, unless you've been grad students of people with names like LeCun, Bengio and Hinton. Deep learning is extremely computationally inefficient, and solving useful problems requires physical construction of special computers (video card toasters).*

Learn linear algebra, classical statistics, gradient based optimization, filters (wavelets, Kalman filters), linear/logistic regression, decision trees, then ensemble techniques like Gradient Boosted decision trees and Random Forests. From there, you'll have a solid baseline to go into something more advanced, and you'll actually be useful to someone who might give you a job.

Learn linear algebra, classical statistics, gradient based optimization, filters (wavelets, Kalman filters), linear/logistic regression, decision trees, then ensemble techniques like Gradient Boosted decision trees and Random Forests. From there, you'll have a solid baseline to go into something more advanced, and you'll actually be useful to someone who might give you a job.

In the past, it takes 15 years for a technology to emerge from the laboratories to production.

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