5 Must Have Machine Learning Skills

Amazon, Google, and Microsoft offer Machine Learning as a service for businesses to explore and derive more meaning out of their data. The algorithms and tools behind their complex systems are now available to be used and applied by any individual or small organisation to build more powerful applications.

Microsoft offers Cortana Analytics which “is a fully managed big data management and advanced analytics suite that enables you to transform your data into intelligent action.” It includes a powerful machine learning and Hadoop-based advanced analytics for driving action in real time. It is intended to help organisations predict outcomes and prescribe decisions.

As machine learning capabilities take centre stage in most transformation projects, we take a look at the top 5 most sought-after Machine Learning skills in candidates (not ranked in order).

  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
    A software design engineer (SDE) is a key member of a software development company. He or she helps identify problems and figures out how to create programs that will automate or ease those situations. 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.