Ten Mandatory Skills to Become an AI & ML Engineer

Ten Mandatory Skills to Become an AI & ML Engineer
Ten Mandatory Skills to Become an AI & ML Engineer

AI and ML are not emerging technologies anymore but parts of our daily lives. In the aftermath of the Covid-19 pandemic, tech has made inroads into the lives of people across urban and rural landscapes in the form of web and mobile applications.

From everyday electronics to home security, there isn’t any aspect of modern life that isn’t touched by technology that has Artificial Intelligence and Machine Learning involved in some respect. It is only natural then, that given the increase in application of AI and ML, there is a need for more qualified and skilled AI and ML Engineers who can leverage Data Science to develop solutions for modern living, and modern business alike.

As AI and ML are often used in a similar context, there is bound to be some confusion about the function of both. Let’s first dive into the purpose and nature of each of these technologies.

AI Engineer vs ML Engineer: Are they the same?

Over time, AI has become a kind of an overarching umbrella term that encompasses both artificial intelligence and machine learning. That said, the work done by AI engineers and ML engineers differs somewhat in nature, context, some of the tools they use, and the end results they are looking to achieve. To simplify, we could understand AI’s focus area to be developing solutions that require human intelligence, while ML’s focus is using data, algorithms, and insights to complete specific tasks. AI engineers chiefly use Java Programming, C ++ and similar tools, ML engineers on the other hand, use and train algorithms, and are required to work with data tools like H2O, TensorFlow, and more. The skillsets of both specializations often overlap, and globally renowned enterprises often hire professionals with skills in both areas. Let’s take a quick look at the most crucial skills in AI and ML.

Common skills for Artificial and Machine Learning Technical Skills

Programming Languages

One of the primary and crucial requirements to be able to get the best out of AI and ML engineering is the knowledge of programming languages. C++, Java, python, and R are among the most popular and commonly used languages and a level of proficiency in these languages ensures a greater scope of application than most others. Python programming remains the most preferred language for AI and ML.

Linear Algebra, Calculus, Statistics

In the time before data science was a thing, statistics was the default field that dealt with data, patterns, and insights. Its uses have transcended technology and even now, for AI and ML engineering professionals, it is recommended to have a good grasp of statistics, with a clear understanding of concepts like Mean, Standard Deviations, and Gaussian Distributions, probability theory for algorithms like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models etc.
If you were to understand even fundamental concepts like gradient descent, you would need to know about Matrices, Vectors, Matrix Multiplication, Derivatives, Integrals, and their applications.

Signal Processing Techniques
Skills in the domain of Signal Processing enable you with problem-solving capabilities that are necessary when you need to work in feature extraction, which is a very important aspect of ML. Advanced Signal Processing algorithms like Wavelets, Shearlets, Curvelets, and Bandlets and Time-frequency Analysis are additional tools that you will need to rely on when it comes to solving the more complex problems in ML.
Applied Math and Algorithms
Math has far-reaching uses in the AI and ML world. A deep knowledge of algorithms in practical application and theory will go a long way in helping you build an understanding of subjects like Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential Equations, and Summations.
Neural Network Architectures
One of the most exciting features of Artificial Intelligence and Machine Learning is their ability to write complex code that is tedious and time-consuming and sometimes beyond human capabilities. Neural networks that mimic the behavior of the human brain solve several problems like Translation, Speech Recognition, and Image Classification through training and have far-reaching real life applications as well. When you learn machine learning with python or tools like R, Tableau, SAS, etc., and work on real-world projects, you will also get to practice these skills.
Non-Technical skills Communication
As with any field of work that requires success, collaboration is achieved with seamless communication. Artificial intelligence and machine learning are no exception to this expectation. Communication in this domain is a manifestation of several teams working alongside each other. The true nature of collaboration shines through when communication happens between teams that communicate fluently and clearly with each other in technical and non-technical terms.
Domain Knowledge
It is a given that you want to excel in your chosen profession and that comes with problem solving capabilities. Those capabilities come with a strong domain knowledge of the field and related fields. Surprisingly, those fields could include genetic biology, statistics, microbiology, chemistry, and literature, other than math of course! It might surprise you what your domain knowledge can facilitate for enterprise success.
Rapid Prototyping
For all businesses, success is a coefficient of a team effort that reduces time-to-market for any product. The rapid prototyping happens when the product idea is thoroughly developed with the right models, testing, and failing fast to find the right way to scale.
Additional skills for Machine Learning
Language, Audio and Video Processing Linguistics and computer science are key to the foundation of AI in a way no other skills are. Language and linguistics form the foundation for artificial intelligence with NLP. Consider how computers, phones, and even applications are able to process voice commands. If you wonder how that comes about, there are libraries like Gensim, NLTK, and techniques like word2vec, Sentimental Analysis, and Summarization at work to facilitate it all. So, that is another area of skills you need to be at ease with to excel in a career in AI or ML. Physics, Reinforcement Learning, and Computer Vision
Physics: Theoretical or practical physics are areas that often are philosophized about, but in the case of AI and ML engineering, they are more than subjects to be thought of. ML applies several physics concepts in the technical aspects of the solutions you create in it.
Reinforcement Learning: Deep learning and reinforcement learning have had a significant contribution towards the development of Artificial learning since 2017. Having skills in the area will prove greatly beneficial if you wish to pursue a career in AI or ML.
Computer Vision: Computer science is the crux of all AI and ML development. Having skills in the area of computer vision will prove beneficial in developing solutions within the prospective areas of machine learning.
AI and ML are exciting fields of applied science and have a great scope for enthusiast learners to practice data science projects and create real world impact. KnowledgeHut’s learn machine learning with python course help you do that. And as you start working on more complex projects make sure you acquire the kind of certification that fast-tracks your career too!
The authored article is written by Mr. Subramanyam Reddy, Founder and CEO, KnowledgeHut upGrad and shared with Prittle Prattle News  exclusively.
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