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7 Skills Needed to Become a Machine Learning Engineer
  • Last Updated : 22 Dec, 2020

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. But these technologies are also thrown about like buzz words where many people don’t know what they really mean or the skills required for learning them. That’s where this article can help you by listing all the different skills you need to bag your dream job as a Machine Learning Engineer!

7-Skills-Needed-to-Become-a-Machine-Learning-Engineer

Machine Learning is a technology that is focused on machines independently learning from the data without much human intervention or explicit programming. This is a complex field that is a distillation of Artificial Intelligence and interlinked with other technologies like Data Science. But this creates the question of Who is a Machine Learning Engineer. And how is he different from a Data Scientist or a Data Analyst? Let’s understand this first.

Who is a Machine Learning Engineer?

Since Machine Learning is deeply linked with Data Science, there are some overlaps in the job description of a Data Scientist, Data Analyst, or a Machine Learning Engineer. However, the main differences are what they focus on. Data Scientists and Analysts are mainly focused on obtaining insights from the data and presenting them to the higher-ups in the organization to make important decisions. This involves having some knowledge of ML algorithms. However, Machine Learning Engineers are excluding focused on Machine Learning. Their aim is to create software components that can work with minimal human supervision and help in obtaining insights from the data they are provided with. This is why there is so much focus on Computer fundamentals and software development for a Machine Learning Engineer because that is their specialization.  

Now let’s see the skills you need to become a Machine Learning Engineer. While the first two of these are quite basic that you may have even learned in your high school or bachelor’s, they become more complex and domain-specific as you move through the list.



Skills Needed for Becoming a Machine Learning Engineer

1. Applied Mathematics

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. Some of the important topics of maths that you need to know include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc. Apart from Maths, having some knowledge of Physics concepts can also be beneficial if you want to become a Machine Learning engineer.

2. Computer Science Fundamentals and Programming

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. Python is a very popular programming language especially for Machine Learning and Data Science so it’s great if you are well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, 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. In detail, some of the common ones include Naïve Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So it’s good if you have a sound knowledge of all these algorithms before beginning your journey as an ML engineer.

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. Similarly, a clustering algorithm for categorical variables is k mode while for probability is k means. You need to know all these details about various algorithms to contribute to data modeling and evaluation effectively.

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. There are many different types of neural networks like Feedforward Neural Network, Recurrent Neural Network, Convolutional Neural Network, Modular Neural Network, Radial basis function Neural Network, etc. While it’s not necessary that you understand all these neural networks in detail to become an ML engineer, it’s important that you know the core fundamentals. And you can always learn the rest on the way!

6. Natural Language Processing

Natural Language Processing is naturally quite important and a fundamental part of Machine Learning. In essence, NLP aims to teach the human language with all its complexities to computers. This is so that machines can understand and interpret the human language to eventually understand human communication in a better way. There are many different libraries that provide the foundation of Natural Language Processing. These libraries have various functions that can be used to make computers understand natural language by breaking the text according to its syntax, extracting the important phrases, removing extraneous words, etc. You can be familiar with some or even one of these libraries like the Natural Language Toolkit which is the most popular platform for creating applications relating to NLP.

7. Communication Skills

And finally, we come to a skill that is a soft skill and may not be considered that important. However, if you are good at communication skills, it can make a world of difference in your career trajectory. That’s because while you understand the data and the insights obtained using machine learning better than anyone else, it is equally important that you can convey these insights to a non-technical team, your shareholders, or clients. This can also involve data storytelling where you should be able to present your data in a storytelling format with a beginning and ending at concrete results that you have obtained from the data using machine learning. That’s because eventually, the data analysis is less important to a company than the actionable insights that can be obtained from the data. And if you are able to convey these insights, then the sky’s your limit in your profession!

Conclusion

Machine Learning is becoming more and more widespread and is used in almost every field these days. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about ML and becoming a Machine Learning Engineer is a great idea and may even be a very wise career move! So check out all these skills and start learning them so you can improve your capabilities and bag your dream job as a Machine Learning Engineer!

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