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Top 9 Skills required for Machine Learning | How to become a Machine Learning Engineer

Top 9 Skills required for Machine Learning

We live in the world of machines and Humans. Man is constantly evolving and learning from his previous experience for many decades. Alternatively the era of machines and robots has already arrived where machine are eventually replacing humans. 

The future of machine could be very massive and it’s completely beyond our imagination. It’s a massive responsibility to regulate them. It might be performed solely by an individual or just say it’s a machine learning expert, who can control their activities and getting this title is a big achievement in itself. 

ML Engineers are subtle programmers who develop machines and programs that may be taught and apply information without particular direction.

Artificial intelligence is a main objective of a ML Engineers. They’re qualified programmers, however their focus goes beyond particularly programming machines to carry out particular duties. They create applications that can allow machines to take actions with out being particularly directed to carry out these duties. So, let us explore, what are the essential expertise  or skills required to become a Machine Learning Engineer. 

If you are looking to pursue your Machine Learning Certification Course, it’s our recommendation to choose Edureka’s Certification with the experts having great exposure in their domain. Now let’s move to explore the Top 9 Skills required for Machine Learning.  

Programming Languages

At the beginning a Machine Leaning Expert must have an excellent command on a programming language, ideally python as it’s straightforward to study and its purposes are wider than many other programming languages.

For Machine Learning Engineer it is always important to have good basics on topics including Memory management, Data Structure(DS), Object Oriented Programming. Though Python is an excellent Language, it alone can’t benefit you. You’ll need to understand all these languages including C++, R, Java and likewise work on MapReduce at some point.

Statistics for Machine Learning

To get expertise in machine learning, a ML expert must have a good command of Matrices, Vectors & Matrix Multiplication. A superb understanding of Derivatives, Integrals and Gradient would be beneficial for him/her. Statistical concepts consisting Mean, Gaussian Distributions & Standard Deviations are needed along with probability theory for algorithms including Gaussian Mixture Models, Naive Bayes, Hidden Markov Models etc.

Applied Mathematics

To become a ML Engineer, an expert must have a great command on Algorithm theory & better understanding with the subjects including Convex Optimizations, Gradient Descent, Quadratic Programming, Partial Differentiation etc.

Signal Processing Techniques

Signal Processing also plays an important role in machine learning technology. So a ML Engineer must have the ability to solve the problems utilizing Signal Processing techniques. He or she should be familier with the signals Knowledge of Time-frequency Analysis & Advanced Signal Processing Algorithms like Shearlets, Wavelets, Curvelets Bandlets.

Neural Network Concept

Neural Networks have a tremendous and unique approach in the field of AI and Machine Learning. They are the class of models and particular set of algorithms that have revolutionized the field of ML.

Neural Networks are used in the field of ML for performing complex and impractical operations. They are applied in machine learning problems for complex mapping between input and output. They possess a far beyond approach to perform complex operations accurately for many problems including speech recognition, translation, image classification etc.

Audio & Video Processing

A skilled Machine Learning Engineer must has a good set of knowledge in audio and video processing techniques because he or she may be going to work with either text or video or audio. To work on it an engineer must be good in libraries including NLTK, Gensim, word2vec, sentimental analysis & summarization. Voice & Audio analysis involves extracting useful data from the audio signals. Being well versed in mathematics & concepts of Fourier Transformation will be plus for any ML Engineer.

Rapid Prototyping

Creating prototype for your model is a great practice for any Machine Learning Engineer.  If he or she comes out with any idea then he or she must do a proper A/B Testing on ideas. The idea must be passed through a multiple algorithmic calculation which decides to select the exact model for further process. 

Industry Knowledge

It’s not being enough if you are a skilled ML Engineer but at the mean time you must have a good industry knowledge for whom you are working for. In many cases the most machine learning projects assigned to those that address or analyze the real pain point. Anywhere you are working, you should understand the industry’s work & its beneficial work.

Techno Savvy and Updated

To remain exist in the field of technology you must be techno-savvy and up to date with changes because most of the time new models come into the market that outperform the previous architecture. So any ML Expert must be aware about the information regarding the tools, algorithm through blogs, research papers, conference videos etc.



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