In our increasingly technology-fuelled world, the global machine learning market continues to thrive and experience steady growth. It was valued at $15.44 billion in 2021 and is projected to hit $209.91 billion by 2029, at a compound annual growth rate of 38.8%.

On top of automating key processes and reducing labour costs, machine learning also boosts productivity, improves user journeys and helps with decision-making.

Thus, corporations and businesses across various industries have been investing large sums of money to deploy machine learning systems and technologies.

An example of this is Google Translate, which found a 60% reduction in error rates after using a machine learning-powered translation engine. Apart from that, Netflix saves $1 billion annually due to its machine learning system which suggests content for viewers based on the previous shows they’ve watched.

With more and more companies embracing machine learning, the demand for it is likely to continue soaring. Hence, creating many career opportunities for those looking to enter this field.

If you’re thinking of dipping your toes in this exciting industry or are curious to find out more, here’s what you need to know about machine learning before taking the plunge.

What Is Machine Learning?

A type of artificial intelligence (AI), machine learning enables software applications to carry out data analytics and identify patterns to make accurate predictions without being specifically programmed to do so.

Machine learning devices work with algorithms that tap into historical data and past experiences to predict new output values. Such algorithms learn directly from data rather than rely on any predetermined equation that may serve as a model.

During the learning process, the performance of machine learning algorithms improves when the number of available samples increases – as there is more data for them to work with and analyse.

When fed with new data, machine learning applications can learn, adapt, develop and grow independently as they operate autonomously without human interference. Today, machine learning is essential for problem-solving across various areas such as algorithmic trading, credit scoring, voice and facial recognition, motion tracking, predictive maintenance, DNA sequencing as well as brain tumour detection, among others.

Here’s What A Machine Learning Engineer Does

As a machine learning engineer, you’ll be required to assess, understand and work with the data sets that feed machine learning systems. Because these systems primarily learn from the data they are given, they have to properly select and condition that data for their learning.

Apart from that, designing and developing machine learning systems is another key responsibility of the machine learning engineer.

With a clear understanding of the available data and the intended purpose of the machine learning system, it will then be possible to choose the right architecture and technologies to learn from that data and produce the necessary deductions and behaviours.

When the best components are identified, the machine learning engineer will then build the models used by the machine learning system so that it can interpret and learn from the data effectively.

During this process, machine-learning tests and experiments will be conducted and appropriate machine-learning algorithms implemented. By testing the models with test data sets, you can then conclude if they produce the required inferences and behaviours.

Career Paths In Machine Learning

While the most common job role is that of a machine learning engineer, there are other roles within the machine learning sector such as:

  • Product Manager
  • Product Director
  • Machine Learning Consultant
  • Solutions architect.

Essential Skills You Need To Build A Machine Learning Career

In order to set yourself up for success in your career as a machine learning engineer, you need to possess skills and knowledge in several areas, including:

  • Computer science basics: Having a solid foundation in computer science is important as this includes understanding data structures and computer architecture, writing algorithms and getting acquainted with programming languages.
  • Programming fundamentals: As machine learning involves the development and improvement of software-based systems and creating automation tools, you require knowledge in programming and getting familiar with software such as Python, R, Java and SQL.
  • Artificial intelligence: Having specialised skills in areas such as neural network architecture, deep learning and dynamic programming can help you to advance in your career.
  • Data: Understanding how to extract the useful parts of data is crucial as you’ll take raw input data and convert it into accessible data sets for training the machine learning models. Besides that, you’ll be required to work with large data sets so it would be helpful to know how to manage the databases that store and present the data to the machine learning system.

Apart from these skills, you also need to have strong teamwork and communication skills as you’ll typically work in a team to support the goals of the organisation. Also, being able to organise and manage projects would help you stay on top of your projects.

To successfully acquire all the necessary skills, you’ll have to pick the course and university that best aligns with your career goals. Throughout the duration of your studies, you’ll then get to learn through lab works, lectures, computer simulation sessions, projects and internships.

Upon graduation, you’ll then be fully prepared to enter the workforce and pursue the career of your dreams in machine learning.

Take The Next Step Towards Your Career In Machine Learning With SIM

Now that you have a better understanding about Machine Learning, is it something you wish to pursue?

If so, the University of London’s Bachelor of Science (Honours) in Computer Science (Machine Learning and Artificial Intelligence) could be a great option for you to consider!

Upon successful completion of this programme, you will equip yourself with the mathematical and engineering knowledge to construct working machine learning and AI systems as well as construct an AI system and apply it to a problem in a project.

On top of that, you’ll learn and understand how machines make sense of the world through intelligent signal processing.

The course is offered by the University of London, one of Australia’s biggest and most diverse institutions. The university is a leading school in the UK and has a reputation for academic excellence with students in 190 countries.

Founded in 1836, the University of London is one of the oldest and most prestigious universities in the UK.

Learn more about the University of London’s Bachelor of Science (Honours) in Computer Science (Machine Learning and Artificial Intelligence) programme today.

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