You’d be hard-pressed to find an industry that isn’t going to be affected by AI and machine learning in the coming years so it’s no surprise that machine learning skills are currently in great demand – and will continue to be for the foreseeable future.
In addition to the core bank of skills you should have in your arsenal, namely software engineering, computer science, architecture, programming, data modelling, system design and a deep understanding of algorithms, probabilities and statistics, you will also need to be proficient in a handful of programming languages.
According to 2017 surveys amongst programmers and employers, these are the 5 languages you need to know now for a career in machine learning:
Let’s jump straight in with the complex but increasingly popular statistical computing language: R.
R is a free, open source environment used by many (over 100,000 members in R’s LinkedIn group for starters) and is particularly favoured by researchers, academics, scientists and data analysts for its visualisation and statistical analysis design.
Whilst there can be a steep learning curve, R is perfect for machine learning professionals thanks to the many built-in algorithms, making it a very powerful tool for testing. It may not be the easiest of languages to learn but there is no shortage of free get-started guides and courses available for new and experienced programmers alike.
Python is big news. A record number of employers are seeking Python skills and, according to Stack Overflow’s 2017 survey, it is currently the most popular programming language, particularly amongst data scientists and machine learning engineers.
Python is user-friendly, comprehensive and extremely versatile, which explains it’s continued rise in growth and popularity. The scientific language is ideally suited for machine learning and is supported by many open-source libraries including NumPy, SciPy, Pandas, Matplotlib, and Seaborn to name a few, which provide a host of useful tools and features.
Java is a functional programming language that has been used by developers for years, and because of that it’s subsequently used by developers who have ended up moving into machine learning roles. With regards to machine learning, it’s a language very much favoured in the finance sector and those prioritising cyber security and fraud algorithms.
WEKA, the open-source library for machine learning written in Java, comes with a collection of algorithms, making it ideal for data analysis, predictive modelling, data mining, visualisation and regression.
C/C++ comes with good AI libraries but it is also popular because it allows programmers to write their own libraries and develop their own algorithms rather than use existing ones, making it ideal for trying out new solutions to problems.
It may not be as convenient as the other languages for machine learning but it is known for being faster and more efficient when used by programmers who know what they want to do with it.
MATLAB is used the world over by millions of software-engineers, scientists and developers. The matrix-based language is ideal for data analysis, modelling and developing algorithms, and comes with a full set of statistics and machine learning features, and thousands of pre-built algorithms.
If you already understand scripting languages and you’re strong in maths, you’ll find it relatively easy to pick up MATLAB. There are many resources to learn from and various tutorials online to bring programmers up to speed.