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The demand for machine learning engineers continues to grow. Tom Merritt lists five skills necessary to land the job.
Artificial intelligence (AI)… you hear everybody claim it—you may even know a few actual products that really use it. If you know your stuff, you understand that machine learning (ML) engineers are in demand to help develop it. So, how do you become one? Here are five skills you’ll need to know to become a machine learning engineer.
SEE: The top 10 languages for machine learning hosted on GitHub (free PDF) (TechRepublic)
- Computer science and programming: No, my friend, the AI cannot program itself—yet. Data structures, algorithms, complexity, computer architecture—if you understand multidimensional arrays, optimization, NP-complete problems, and distributed processing, you have a good start.
- Probability and statistics: From means and medians to binomial distributions to Hidden Markov Models, you’re going to have to ace that stats exam to make some good ML.
- Data modeling and evaluation: You don’t need to be a data scientist per se, but it helps. Be prepared to learn all kinds of accuracy and error measurements and evaluation strategies, among other things.
- Applying ML algorithms and libraries: Can you take those publicly available APIs and select the right model and learning procedure to fit the data? This is where you take all those theories you just learned and apply them.
- Software engineering and system design: Once you’ve practically implemented your algorithms and libraries, you’re probably not done. Your software still needs to work with a larger system of products and services to be of any use. Library calls, REST APIs, database queries—you need to know that stuff too.
Now this may all seem like a lot, but each part feeds into the next, and at the end, you have a thing that creates faces from scratch—or whatever useful goal you were trying to attain.
Get more tips in Alison DeNisco Rayome’s article How to become a machine learning engineer: A cheat sheet.
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