Every organization from Facebook to Federal Bank employs machine learning experts today — Facebook for myriad functions, like the complex computer vision algorithms to read images/videos to the blind; Federal Bank’s FedRecruit for 360-degree narrative assessment of job applicants. Machine learning careers are growing, and will only continue to do so. But, how to build a career path in machine learning?

As future technologies like machine learning are evolving rapidly — job roles are not yet clearly defined and salaries vary across a wide range — it can be difficult for aspiring ML professionals to find their way without accurate information and pointed direction. To help you find your way, we asked Springboard mentors for their top career advice. Here are the questions they say you should be asking yourself.

How to Build a Career Path in Machine Learning?

Q1: What career is right for me?

Machine Learning is a subset of artificial intelligence (AI) where statistical models and algorithms are built to make computer systems perform a specific task without explicit instructions. Or human intervention. This naturally has a wide range of applications across industries like healthcare, manufacturing, retail, finance, etc. Some of the most sought after machine learning job roles are:

Machine learning engineer: A machine learning engineer’s typical day involves collating and analyzing data, building and deploying ML algorithms and models. This job, in addition to being a fast-growing tech role, also commands an attractive salary.

Deep learning engineer: In this specialized area in ML, a deep learning engineer builds ML models using deep learning techniques/algorithms such as a convolutional neural network (CNN), Deep Boltzmann Machine (DBM), long short term memory networks (LSTM), etc.

Computer vision / NLP / computational linguistics specialists: These roles involve working on specific machine learning areas, based on data they analyze — images/video in case of computer vision; spoken/written text for natural language programming (NLP); speech for computational linguistics, etc.

Machine learning designer: In the emerging field of human-centered ML, professionals build models/algorithms in response/preparation to human input. It could be Netflix / Youtube’s recommendation engine or even Samsung’s new personal care rolling robot Ballie. In fact, Ballie even works with canine input!

Q2: What do I need to learn for a career in machine learning?

To thrive as a machine learning engineer, you need a combination of skills and familiarity with tools. 

  • Programming with Python, Java, etc. Python is the best language you could learn to start building models. But, also build experience with other tools and ML libraries like sci-kit learn, TensorFlow, PyTorch, etc.
  • Statistical analysis with R and SAS.
  • Data wrangling with Pandas and Numpy.
  • Data modeling with Power Designer and ER/Studio.
  • Deep learning, neural network principles and engineering frameworks with Keras and PyTorch.

Q3: Where can I go to learn about them?

The internet is a great place to start. Subreddits about machine learning are a treasure trove. So are DataTau, and the ML section in StackExchange.

Also, there are some excellent books which give you both theoretical context and practical advice such as ‘Machine Learning Yearning’ by Stanford Professor Andrew Ng, ‘The Hundred Page Machine Learning Book’ by Gartner’s Andriy Burkov, ‘Machine Learning for Hackers’ by Computational Expert Drew Conway and Facebook’s John Myles White.  

Q4: How do I get a job?

Once you have a basic understanding, apply it!

Practice your learning: Write code and build machine learning models using the open datasets from Kaggle, KDNuggets etc. Also create a portfolio on these sites, where you can showcase your personal projects to potential employers.

Compete: Take part in competitions and ML hackathons from Kaggle, DrivenData, Codalab etc. You can also develop and submit algorithms to places like Facebook’s bot for workplace initiative. You will get a feeling for real-life challenges and the pressure of working under a deadline. 

Build a resume: Prepare a resume that showcases your skills in ML, including any in programming, statistics, and data modelling. Also, include your certifications, and links to your personal portfolio / GitHub page.  

Prepare for the interview: Go the extra mile to impress. Build an ML-based solution to one of your prospective employer’s problems and demo it at the interview. If that seems a bit much, don’t fret. Just read up about the company and prepare some conversation starters.

Q5: How do I accelerate my machine learning career?

Building a career in an emerging field like ML can be challenging, even for experienced software professionals. Which is why Springboard’s courses go beyond just teaching the theory and practice of the subject. All our online learning courses are mentor-led and project-driven: They give you hands-on experience as well as personalized career counseling in navigating the brave new world of machine learning.

If you’re serious about a career in machine learning or artificial intelligence, consider Springboard’s AI/Machine Learning Career Track now that offers a 1:1 mentoring-led and project-driven program and comes along with a job guarantee.