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7 top skills you need to become an ML engineer

Machine learning is a technology involving machines learning independently from available data without any human interference or explicit programming. The field of machine learning is a complex field, often considered an extension of artificial intelligence and related to other technologies such as Data Science. Because of overlapping roles and responsibilities, it becomes necessary to know the difference between machine learning engineers, data scientists, or data analysts. Let’s first learn who an ML engineer is and what makes it different from other similar job roles to figure this out.

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Who is an ML engineer?

Getting into the details of similar job roles related to machine learning, data scientists and analysts primarily aim to obtain insights from data and share with the organization to make informed decisions. This at times includes some knowledge of machine learning. However, ML engineers aim to develop software components that can work on themselves with minimal human intervention and help in deriving insights from the available data. Because of this, a lot more attention is paid to computer fundamentals and software development for an ML engineer because this is what their specialization is. To seek a detailed understanding of machine learning, you can look for the best machine learning course that will match your learning objectives and help you seek job opportunities easily.

Top 7 skills required to become an ML engineer

Let’s learn in detail about the top 7 skills to become a successful ML engineer.

  1. Applied Mathematics

Mathematica has got a pivotal role to play in machine learning. To become a proficient machine learning engineer, you need to gain expertise in mathematical concepts related to linear algebra, probability theory and statistics, multivariate calculus, algorithms and optimization and other concepts like real and complex analysis (sets and sequences, topology, metric spaces, single-valued and continuous functions limits, cauchy kernel, fourier transforms), information theory (entropy, information gain), and function spaces and manifolds.

  1. Neural Network Architectures

Neural networks are mainly a predefined set of algorithms used for implementing ML tasks. They majorly offer a class of models and perform a major role in machine learning. ML engineers need to be proficient in neural networks because it helps them understand the human brain’s functioning and help to imitate and model an artificial one. Furthermore, neural networks offer a better understanding of parallel and sequential computations. Some of the major concepts that ML engineers need to focus upon while learning about neural networks include Perceptrons, Convolutional Neural, Networks, Recurrent Neural Network, Long/Short Term Memory Network (LSTM), Hopfield Networks, Boltzmann Machine Network, Deep Belief Network, and Deep Auto-encoders.

  1. Data Modelling and Evaluation

Being a machine learning engineer, one needs to have proficiency in data modelling and evaluation because the entire work of an ML engineer is based on data. Data modelling includes knowledge of underlying data structures and figuring out patterns that are invisible to normal people. Furthermore, ML engineers need to evaluate data using algorithms best suited for data. Knowledge of all these concepts will help you contribute to data modelling and evaluation adequately.

  1. Advanced Signal Processing Techniques

The primary use of signal processing is to decrease noise and pull out the best features of a signal. To attain proficiency in advanced signal processing techniques, one needs to seek knowledge of convex or greedy optimization theory and algorithms, spectral time-frequency analysis of signals, and algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets and so on.

  1. Audio and Video Processing

It is a bit different from natural language processing as one can apply audio and video processing on audio signals only. To be good at audio and video processing, one needs to understand Fourier transforms, Music theory, and TensorFlow.

  1. Reinforcement Learning

Although gaining knowledge of reinforcement learning is important for learning artificial intelligence and deep learning, it can be useful for you during your initial days as a machine learning engineer.

  1. Excellent Communication Skills

Possessing excellent communication skills is one of the most important skills for an ML engineer because his job is to derive insights from data and convey it in an understandable manner that any non-technical person and the stakeholders can understand. This will help the stakeholders to understand the facts better and make improved decisions in the future. ML engineers need to be good storytellers and should be able to translate insights into actionable insights.

Machine learning has become quite prevalent in today’s world and can be found even in the simplest of applications. Be it cybersecurity, healthcare, or automobile, and machine learning has found its applications almost everywhere. Given this, becoming a machine learning engineer can present you with a rewarding career ahead and take more significant leaps in your career ladder. So to have a great career as a machine learning engineer, pull up your socks and work hard to get proficient at these skills!

https://www.knowledgehut.com/blog/data-science/machine-learning-skills

https://www.udacity.com/blog/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html

https://www.geeksforgeeks.org/7-skills-needed-to-become-a-machine-learning-engineer/

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