Machine learning is a hot topic in society today. It’s hard to imagine the future of any industry without the use of technology to manage and streamline its processes. As this becomes more commonplace, the demand for Machine Learning Engineers increases.
Companies are looking to these skilled individuals to take their systems to the next level. Leading them into the future with slick algorithms that learn from historical data to perform tasks better, with little input.
So much is the need that by 2022, machine learning is expected to be an $8.81 billion industry. Taking it from a seemingly niche area of computer science to a key component of the technology industry.
By gaining machine learning capabilities, you will become desirable for exciting and innovative careers across a growing number of industries.
Though, with it still being a fairly young discipline, the route into the profession is relatively elusive. As machine learning draws on knowledge from many other fields, it can be hard to know what to focus on.
Read on for more on how you can become a Machine Learning Engineer.
Work on Developing Essential Skills
Anyone pursuing a career in machine learning will need certain knowledge and skills before they’ll be considered for roles. Below are some of the most important, but by no means all of them. By mastering these, you’ll be in a greater position for entry into the field with continued learning and development.
Python is the chosen programming language for most Machine Learning Engineers. Most of the tools used for data are either built in Python or have API access allowing Python access.
Python’s syntax is relatively easy to learn and there are plenty of training resources available. Aside from Python, other beneficial programming languages are C++ for speeding up code and R for statistics and plots.
To become a Machine Learning Engineer, you need to understand the mathematics behind machine learning algorithms. Machine learning is generally built on the foundation of Linear Algebra, Calculus, Statistics and Probability. Linear Algebra makes it possible for algorithms to run on huge datasets. Calculus helps finetune the result by optimising the performance of the algorithm. Statistics are used to draw logical conclusions from data and Probability helps to predict the likelihood of future events.
Data Analysis, Modelling and Visualisation
Most of machine learning is data science, which essentially involves a lot of data preparation. When building machine learning models, your time is mostly spent gathering data, exploring and cleaning it before analysing the results.
Once you’ve cleaned up data sets you process them through machine learning models. This takes out any mistaken values and validates the data before manipulating it to a desired state. So that it’s ready to be transformed or handled sophisticatedly by different algorithms.
You then need to represent the data through appropriate visualisation methods. Whether this be in single charts or comprehensive dashboards. Through effective data visualisation you can significantly reduce the time it takes to process information and see valuable insights.
Machine Learning Theory
It’s essential you understand how machine learning algorithms work. Such as their specific goals and how to use them on data at scale. The base set of algorithms you’d ideally have experience with are:
- Linear Regression
- Logistic Regression
- k-Means Clustering
- k-Nearest Neighbours
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- Naive Bayes
Foundation in Computer Science Theory
You’ll also need to know the time and space algorithms take to process different amounts of data. Recognising how to reduce space and time considerations allows for machine learning pipelines that can handle petabytes of data. Helping you to build data pipelines that operate with maximum performance.
Most employers only consider candidates with at least a master’s degree in machine learning, computer science or a related discipline. If not a PhD.
It is possible to get into a machine learning role without a degree. But you’d need some serious skills and ample evidence of them to get a look-in.
Gaining advanced recognised qualifications through a master’s degree or PhD programme is the best way to evidence your learning. It also allows you to gain the programming knowledge, understanding of machine learning frameworks and advanced mathematics skills you’ll need.
You could also gain experience while you learn by starting work in the industry after gaining a relevant undergraduate degree. Getting your foot on the ladder of a career path ultimately leading to a Machine Learning Engineer role. Working as a Software Engineer, Programmer, Developer, or Computer Engineer while studying your advanced degree.
Once you obtain your advanced qualifications, you’ll need to stay on top of changes and developments in the field. You can do this through various continuing education courses.
Get Started with an Access to HE Diploma
Machine learning studies are a gateway to numerous dynamic and fascinating careers with cutting edge technologies. By becoming qualified, you’ll have ample opportunities available to you with high earning potential.
You can get started on the path to becoming a Machine Learning Engineer by studying computer science. Machine learning is a specialised field within computer science. Therefore, there are many elements of computer science qualifications that are both important and relevant for machine learning professionals. Such as the different data structures, algorithms, computability and complexity as well as computer architecture.
Studying level 3 and undergraduate qualifications in computer science will give you essential knowledge of machine learning and artificial intelligence.
What’s more, an Access to Higher Education Diploma (Computer Science) can stand in place of A Levels. So, if you don’t have the qualifications to apply for your undergraduate degree, you can once you complete this course.
The course teaches you many essential elements of machine learning. From advanced mathematics and data analytics to programming constructs and coding standards. Helping you get both university and industry ready, so you can hit the ground running once you qualify.
It is also studied completely online, with no structured lessons or timetable. Giving you the freedom to move through the materials at your pace and allowing for a comfortable learning experience.
This course is provided by Stonebridge Associated Colleges, who are a leading UK distance learning provider. As a trusted and regulated online college, you can be assured of the quality of your education.
Find out more by clicking the link to view the course below.