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Saying ML is "just linear algebra and calculus" is a gross simplification. As a dev, you can pick up some popular ML frameworks and learn the basics relatively
by prions 8y ago
Saying ML is "just linear algebra and calculus" is a gross simplification.
As a dev, you can pick up some popular ML frameworks and learn the basics relatively quickly. The difficulty in this field comes from the amount of theoretical knowledge you need to interpret your results.
All these data science bootcamps/learn quick schemes are like teaching a blind person to drive a racecar. He can work the pedals and the steering wheel - but has no idea where he's going.
- Puer 8y agoExactly this. Implementing algorithms that work on well-defined clean data isn't difficult. Working with naive problems where the right algorithm isn't clear or can't be directly implemented is difficult. In many cases cost of implementation is significant. You can't just apply a model to millions of points of data and blindly expect to get anywhere without fundamentally understanding the data you're working with first. That's what separates data scientists from data engineers, and the ability to take a naive problem and work with it to a conclusion in a manner that's understandable both from a technical and business point of view is exactly why having a PhD is typically a barrier of entry because that's all academics in data science do all day!