5 Must-Have Machine Learning Skills

Next up, as part of our “5 must-have skills series”, we’d like to share with you the top 5 Machine Learning skills. The below is based on our internal data such as job briefs taken, job specs as well as our regular conversations with clients and candidates alike.

From our perspective, the top 5 most sought-after Machine Learning skills in candidates (not ranked in order) are as follows:

1. Programming and Computer Science Principles
   Knowing the basics like data structures, algorithms and computer architecture is really important and the ability to implement, apply and adapt them while programming. Great ways to improve your skills in this are coding exercises, competitions or hackathons.
2. Statistics and Probability
   Understanding the different probability theories that underpin Machine Learning algorithms as well as the fundamentals of probability is key. Knowledge of the field of statistics is also crucial to build and validate models from data.
3. Modelling and Evaluating Data
   The skill to approximate the structure of a dataset and the ability to see patterns or predict the properties of occurrences that haven’t come up before. It’s also vital to be able to continuously evaluate the model being used to be able to choose a useful appropriate accuracy/error measure and allows you to continually tweak your model.
4. Algorithms and Libraries
   Being able to implement Machine Learning algorithms involves the selection of an appropriate model, the right libraries and packages, a procedure to fit the data and an understanding of how these parameters will affect learning. It’s also important to know the pros and cons of each approach and then potential pitfalls of using a certain model over another.
5. Software Engineering and System Design
   An understanding of how all the pieces work together when developing the ecosystem that fits into a product or service is very important. Overall, a Machine Learning Engineer’s goal is the delivery of a piece of software, so being able to understand the potential pitfalls caused by not being able to scale your algorithms is really important for productivity, quality and collaboration.