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Machine Learning Engineering 101

Imagine that you’re doing your school work while jamming to your favorite songs. You’ve listened to your entire playlist of favorite music, so new songs are randomly shuffled to play. The songs resonate with your musical interests, but how did the streaming service know that you would like these songs? While it may seem like psychic abilities are behind this, the prediction of songs actually comes from the work of music machine learning engineers.

Music machine learning engineers apply machine learning algorithms to music services like Spotify and Apple Music. Machine learning algorithms analyze an individual’s music preferences as well as the songs they previously listened to in order to personalize

these services. Streaming services create these personalized playlists by recommending certain genres or artists that the listener will likely enjoy. While this is specifically in the context of music streaming services, these machine learning algorithms can be implemented for almost any service that can aid people in finding useful information specific to their interests.

Generally, machine learning engineers program algorithms to analyze large amounts of data in order to better predict a user's searches and assist their needs. It is the basis of most audio and video streaming services, social media recommendation systems, and search engines. The algorithms that machine learning engineers make collect data about the users preferences, interests, and other information to make highly informed predictions as to what the user would enjoy or find useful, thereby making the person more likely to use that platform in the future.

One particularly advanced machine learning algorithm is an intricate technique known as deep learning. Deep learning, also known as deep neural network, is a technique that enhances the ability of machines to find patterns, allowing them to amplify the smallest patterns to create meaningful data. Within deep learning there is supervised learning, unsupervised learning, and reinforcement learning. In supervised learning the machine is told by the labelled data what patterns to look for, whereas in unsupervised learning the data is unlabelled and the machine looks for any patterns it can find. In reinforcement learning, the algorithm goes through trial and error in order to find which of its behaviors allow it to meet its objectives.

Unlike data analysis, where the goal is to model and transform statistics in order to turn them into actionable insights, machine learning is aimed at creating algorithms so that decisions can be made by machines. In predictive analysis, previous data is used to test and create assumptions in data in order to predict future questions. Conversely, AI machine learning systems have the ability to constantly analyze data, test predictions, and learn from these predictions autonomously.

Now that you’ve learned more about machine learning engineering, how do you get a job and what are employers looking for?

Most employers looking to hire machine learning engineers require candidates to have at least a master’s degree or PhD in computer science or math. Machine learning engineers are also required to have a variety of skills, including data modelling, finding useful patterns, and continuously evaluating how good a given model is in order to maintain its efficiency. Machine learning engineers should also be knowledgeable in probability and statistics, in addition to being fluent in computer science fundamentals like data structures, computability/complexity, and computer architecture.

Machine learning is a promising field that will only become more useful in the years to come due to its various real world applications in education, finance, healthcare, and more. Not only is machine learning becoming more advanced through technological innovations, but it is also predicted to create numerous jobs in the years to come.

Author: Prachi Fozdar

Editor: Simona Hausleitner


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