Basic Skills Required to Become a Machine Learning Engineer


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Do you want to transition to becoming a Machine Learning Engineer? If so, then you are not alone! Technologies like Artificial Intelligence, Machine Learning, Data Science, etc. are becoming more and more popular these days. This article can help you by listing all the different skills you need to bag your dream job as a Machine Learning Engineer!

1. Applied Mathematics

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Maths is quite an important skill in the arsenal of a Machine Learning engineer. It is also one of the basic subjects that are taught right from school and that’s why it is the first skill on our list. But are you wondering why you need maths at all? (Especially if you don’t like it?!!) Well, maths can have many uses in ML. You can apply various mathematical formulas in selecting the correct ML algorithm for your data, you can use maths to set parameters, approximate confidence levels, Many of the ML algorithms are applications derived from statistical modeling procedures and so it’s very easy to understand them if you have a strong foundation in Maths. 

2. Computer Science Fundamentals and Programming

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This is another basic requirement for becoming a good machine learning engineer. You need to be familiar with different CS concepts like data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, etc. The good thing is you probably know all of this if you have done your bachelor’s in computer science! You should be well versed in different programming languages like Python and R for ML and statistics, Spark and Hadoop for distributed computing, SQL for database management, Apache Kafka for data pre-processing, etc. 

3. Machine Learning Algorithms

What is a very important skill in becoming a Machine Learning Engineer? Obviously, it’s very important to know all the common machine learning algorithms so that you know where to apply what algorithms. Mostly ML algorithms are divided into 3 common types namely, Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms. 

4. Data Modeling and Evaluation

As a machine learning engineer, you should be skilled in data modeling and evaluation. After all, data is your bread and butter! Data modeling involves understanding the underlying structure of the data and then finding patterns that are not obvious to the naked eye. You also need to evaluate the data using an algorithm that is suitable for the data. For example, the type of machine learning algorithms to use such as regression, classification, clustering, dimension reduction, etc. depends on the data. A classification algorithm well suited to large data and speed may be naive beyes, or a regression algorithm for accuracy might be a random forest 

5. Neural Networks

Nobody can forget the importance of Neural Networks in the life of an ML engineer! These Neural Networks are modeled after the neurons in the human brain. They have multiple layers that include an input layer that receives data from the outside world which then passes through multiple hidden layers that transform the input into data that is valuable for the output layer. These demonstrate a deep insight into parallel and sequential computations that are used to analyze or learn from the data. 

You will need all these skills and more to become a machine learning engineer.

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