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Know These Things to Succeed in Machine Learning   Featured

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As data science keeps becoming a go-to profession for many, you might be among those who want to become part of the new professions like Machine Learning Engineer. If so, you are joining a list of many people who want to know more about technologies like Artificial Intelligence, Machine Learning, the Internet of Things and how Data Science can help an organization. Although many people talk about these professions and buzzwords, most of them do not know what it takes to succeed in the field of data science. As an aspiring enthusiast who wants to get the best out of the new professions, know these things to succeed as a machine learning expert.

What is Machine learning?

Machine learning is a technology that is focused on helping machines learn independently from data without human input. The machine learning field is connected with artificial intelligence and is a crucial area in data science.

Who is a Machine Learning engineer?

With the crucial link between Data Science and Machine Learning, Machine Learning Engineers, Data Scientists, and Data Analysts often have overlapping job descriptions. However, the differences are in what each one of them focuses on. For instance, Data Scientists and analysts mainly focus on gathering insights from data and presenting them to the organization's leadership, who use the data to make critical decisions. Data Scientists and Analysts have some knowledge about machine learning algorithms. On the other hand, Machine Learning engineers are focused on Machine learning. Their goal is to create software components that can work with limited human supervision to gather insights from the data provided. Here are some skills you need to have as an ML engineer.  

  1. Applied Mathematics

Mathematics is an important skill for an ML engineer. It helps ML users to select the correct ML algorithms for data. Maths can also help in setting parameters, approximating levels and performing statistics. Some topics that one has to be conversant with include statistics, calculus, linear algebra, multivariate distributions and probability. On top of maths, physics concepts can also be beneficial if you want to become a successful data engineer.

  1. Computer science fundamentals and programming

This is another key requirement for a machine learning engineer. You must be familiar with computer science concepts like data structures, algorithms, and space and time complexity. If you have studied a bachelor's in computer science, this should not be new. You should also be well versed in different programming languages like Python, R and ML Statistics. Others like Spark and Hadoop, and SQL for database management, are also crucial for an ML Engineer.

  1. Machine Learning Algorithms

 As an aspiring ML engineer, the most important skill you can possess is ML algorithms. You should understand all common ML algorithms so that you know when and where to apply. The classes of algorithms include Supervised, Unsupervised and Reinforcement Machine Learning Algorithms. Some common algorithms include Naïve Bayes Classified, K Means Clustering, Apriori Algorithm, and Random Forests. You should have sound knowledge of these algorithms and others before starting the journey.

  1. Communication skills

While hard skills are crucial for an ML engineer, soft skills are equally important. If you want to change the direction of your career for the better, you should have good communication skills. These skills will make a world of difference in your Machine Learning Engineering job. This is because, in addition to understanding the data and insights obtained using machine learning, you can convey the insights to both technical and non-technical teams with ease. Furthermore, you will be able to communicate with the shareholders and clients. This is known as data storytelling, where you can present data in a storytelling fashion, which is easy to understand.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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