How to become an AI Engineer?

 Nowadays, The Demand for Artificial Intelligence is increasing day by day and most of the engineering students want to become an AI Engineer. In the coming days, AI will revolutionize all industries and will rise in demand for AI in every sector. Therefore the demand for the job for AI is increasing. As per the report, AI will create a Business value worth around US$4 trillion by 2022. There are several artificial intelligence experts coming forward and distributing their projects. The reality is that it is not so easy to become an Artificial Engineer, but the positive thing is if the candidates are already aware of different programming languages, basic concepts in computer science, and technology resources, then it's good to go on learning and expecting an Artificial Intelligence Engineer.

 

What Does AI Engineer do?

 

AI Engineers build AI models and work on a variety of projects and by depending on the role, the main responsibility of the job could be:- 

• computer programming languages such as Java, Python or C, 
• working with data, algorithms, and good knowledge in statistics, 
• Using data modeling and evaluation strategies
• Work with Robotics team
• Perform AI/ML Testing
• Applying Machine Learning algorithms and libraries
• Research and Design Deep Learning algorithms
• AI Engineers have a good understanding of Data Science
• Convert machine learning models into application program interfaces ( APIs), so that it can be used by other applications

But there are many AI engineers who may work on different projects for example if they are working on self-driving car projects there could be elements of computer vision, creating deep learning models, sensor fusion, and programming languages.

 

Step To Becoming and AI Engineer

 

Earn an Educational Qualification 

 

To become an AI Engineer first you have to take the right qualification and you need to earn a bachelor’s degree because it is the most important part of the journey to becoming an AI engineer. 

Education required:

• Bachelor’s degree in IT, Computer Science, Statistics, Data Science, Finance, Economics, etc.
• Master’s degree in Computer Science, Mathematics, Cognitive Science, Data Science, etc.
• Certifications in Artificial Intelligence, Data Science, Machine Learning, etc.

Programming Skill

 

Programming is the first skill needed to become an AI Engineer and you have to become proficient in programming languages. You need to choose one or more languages that will help you discover the AI capabilities and apply them. Some of the many languages that work well with AI such as Java, Python, C++, Lisp, R, Prolog, etc. to build and implement models.

You can also start with Java or C++, but you'll face multitasking and portability issues. But Also, Python will not be so enough, so we suggest, better start with Python or R and finally move to learn algorithms.

Statistical knowledge/calculus/Algebra 

 

Coming to statistical knowledge, you must be intimately familiar with the vectors, and matrixes. If you understand derivatives and integrals, you have to be in the clear. Statistics is going to come up a lot. You also need to have a firm understanding of the probability of understanding models such as Naive Bayes, Gaussian Mixture Models, Hidden Markov models. you will need detailed knowledge of statistics, calculus, or algebra, and probability to improve your AI program.

 

Algorithms and Applied Mathematics

 

To become a successful AI Engineer, You must have in-depth knowledge of algorithm theory and how algorithms work. Artificial Intelligence would need you to know subjects like Gradient Descent, Lagrange, Quadratic Programming, Partial Differential Equation, etc. At first, all these maths can seem intimidating if you've been away from it for a while. Be prepared, Machine Learning and Artificial Intelligence are much more math-intensive than advances at the top.

 

Natural Language processing

 

Natural Language Processing combines two main fields of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. You have a very high chance to work with either text, audio, or video. So it is important to have good control over libraries such as Gensim, TextBlob, Core NLP, NLTK, and techniques such as word2vec, Sentimental Analysis, and Summarization.

 

Neural Networks

 

At times, we may need Machine Learning for tasks that are too complicated for the developer to code directly.  It is here where the neural networks come in. Neural networks are modeled after the human brain, capable of identifying numerical patterns based on sensory input.

Naturally, the Artificial Intelligence field has evolved single-layer neural networks into Deep Learning neural networks, in which data is passed through several layers for more complex pattern recognition. Deep neural networks have by far been the most effective way to tackle complex problems, such as Translation, Speech Recognition, and Image Classification, which play a very important role in AI. 

 

Communication and problem-solving skills

 

AI developers need to interact effectively with stakeholders in order to sell their products and ideas. They should also have outstanding problem-solving skills to overcome barriers for decision-making and draw valuable insights for business.

 

Analytical Skills

 

To become a successful AI Engineer, you have to quickly check the numbers and data. This requires analytical thinking. You must have to ask questions to the data analytics team to ascertain the feasibility of data and brainstorm with the key stakeholders.

 

 Spark and Big Data Technologies

 

AI engineers work with large volumes of data in terabytes or petabytes which could be streaming or real-time production-level data. To make sense of such data, those engineers need to know about Spark and other big data technologies. Other Big Data technologies, such as Hadoop, Cassandra, and MongoDB, can also be used along with Apache Spark.

 

Business Acumen

 

The most successful, also common, Artificial Intelligence projects require working on the front and solving weak pain points. Whichever sector you are in, you need to be aware of the market, the consumers, and the way the core business works. If you do not have the business acumen to convert your technological ideas into a successful business model then it would be less beneficial to have such technical skills.

 

Certification course

 

To be an AI engineer, it is highly recommended that you complete a certification course in data science, machine learning or artificial intelligence. These certifications will add value to your CV and help you gain in-depth knowledge of AI topics, along with hiking up your pay to match the salary of an AI Engineer.

 

Also Read: How Artificial Intelligence can Impact Cyber Security

 

So here you can see the process to become an AI Engineer. If you want to start your career in Artificial Intelligence and want to do a certification course to add value to your CV then join “Nearlearn”. Nearlearn is the Foremost Artificial Intelligence Training Institute in Bangalore and also the best Machine Learning Training Institute. They provide highly skilled trainers having 10+ years of industry experience in these filed. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies and also provide internship facilities to their students. 

 

For more information contact us:

Visit: www.nearlearn.com