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Machine learning and artificial intelligence are two of the major buzzwords we hear in the tech industry. The two seek to disrupt the way businesses run, how humans interact, and how everyday tasks function. This game-changing technology is exciting, and engineers want to get in on the action.

Artificial intelligence is the study of developing machines that can perform certain human-like functions, such as learning and reasoning, on its own. Machine learning refers to the underlying programs that allow such machines to derive patterns from data. It lays the groundwork for things such as personalization of ads on websites, self-driving cars, speech recognition on phones, and web search results. Machine learning engineers develop algorithms that can not only compute the enormous amounts of data we input, but actually learn and react to them.

With the increase of applications for machine learning, the demand for engineers skilled in data science will also increase. What does it take to get a job in machine learning? How can you make the transition to machine learning?

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What should I do if I want to transition into machine learning?

Many machine learning jobs will require a graduate degree (MS, or PhD) in Computer Science. If you have earned a BS in Computer Science, you can supplement your application with years of experience working as an engineer in a professional setting. The strong mathematical aspect to data and statistics in the  requires someone who is well versed in probability and statistics fundamentals.

Software engineers that want to make the transition can do so with some preparation. If you are well-versed in a particular language, consider taking the time to learn the frameworks and libraries that accompany that language. Many of these libraries are open source, making it even easier for you to access and learn. Once you get the basics down, head over to GitHub to put your skills to the test.

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Common technical skills needed for a job in machine learning

Python, C/C++, and R are all widely used programming languages used in machine learning. Python’s dependable nature makes complex algorithm development simple. What Python lacks in speed efficiency, it makes up for in ease of development and resources. The library for Python is extensive. If you are a Python programmer who wants to make the transition into machine learning consider learning the framework Scikit-learn. Scikit-learn is open source library that focuses on using regression to learn past patterns and clustering to group similar data together.

R programming language has many advantages when it comes to machine learning. R is a language used specifically for statistical computing and data manipulation. There are many extensions for additional functionality and to support complex algorithms. On paper, this seems to be the obvious choice for machine learning, but engineers can be deterred by rumors of a steep learning curve. A seasoned engineer with the desire to learn R will find that any challenges presented initially will be offset by the benefits of knowing the language.

C/C++ is often used at the core of machine learning, especially for embedded systems used in autonomous cars. However, when you’re looking to implement complex algorithms, it may not be the best choice. The speed of development and expansive libraries available for C/C++ have helped to maintain its importance in the study of artificial intelligence.  If you are already well versed in C/C++, it may be helpful to learn frameworks such as Shogun and Vowpal Wabbit. VW was developed in part by Yahoo! Research and is now sponsored by Microsoft Research.

Finally, there’s Java. Whichever programming language you use in machine learning, you’ll find that it’s absolutely necessary to have extensive libraries to augment your project, and Java certainly has that. WEKA, Rapidminder and Spark are all great resources for an engineer using Java. For example, WEKA has algorithms written in Java that can be used directly in your code for data mining.

Making the transition to machine learning can be extremely rewarding for your engineering career. It’s expected that we’ll continue to see the influence artificial intelligence on innovations throughout the Silicon Valley. Learning these tools, frameworks and libraries now will give you a leg up in your future job search. Contact us today if you’re looking for a role in Machine Learning at 408-260-2600. 

 

 

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