{"id":26037,"date":"2021-01-21T11:16:05","date_gmt":"2021-01-21T11:16:05","guid":{"rendered":"https:\/\/stonebridge-blog.positive-dedicated.net\/?p=26037"},"modified":"2021-01-21T11:16:06","modified_gmt":"2021-01-21T11:16:06","slug":"how-to-become-a-machine-learning-engineer","status":"publish","type":"post","link":"https:\/\/www.stonebridge.uk.com\/blog\/access-to-higher-education-diplomas\/how-to-become-a-machine-learning-engineer\/","title":{"rendered":"How to become a Machine Learning Engineer"},"content":{"rendered":"\n

Machine learning is a hot topic in society today. It\u2019s 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

So much is the need that by 2022, machine learning is expected to be an $8.81 billion<\/strong><\/a> industry. Taking it from a seemingly niche area of computer science to a key component of the technology industry.<\/p>\n\n\n\n

By gaining machine learning capabilities, you will become desirable for exciting and innovative careers across a growing number of industries.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Read on for more on how you can become a Machine Learning Engineer.<\/p>\n\n\n\n

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Work on Developing Essential Skills<\/h2>\n\n\n\n

Anyone pursuing a career in machine learning will need certain knowledge and skills before they\u2019ll be considered for roles. Below are some of the most important, but by no means all of them. By mastering these, you\u2019ll be in a greater position for entry into the field with continued learning and development.  <\/p>\n\n\n\n

Python Programming<\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Python\u2019s 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.<\/p>\n\n\n\n

Advanced Mathematics<\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

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Data Analysis, Modelling and Visualisation<\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Once you\u2019ve 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\u2019s ready to be transformed or handled sophisticatedly by different algorithms.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Machine Learning Theory<\/h3>\n\n\n\n

It\u2019s 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\u2019d ideally have experience with are:<\/p>\n\n\n\n