Artificial Intelligence Engineer
- Swetha Y
- Jun 25, 2019
- 0 comment(s)
Who is an Artificial Intelligence Engineer?
An artificial intelligence engineer works with algorithms, neural networks, and other tools to advance the field of artificial intelligence in some way. These professionals may work on various types of artificial intelligence in different industries, for example.
Engineers may also choose between projects involving weak or strong artificial intelligence, where different setups focus on different capabilities. That’s a topic for another discussion. Now there are many ways one could reach there. So, let’s see how to become an Artificial Intelligence Engineer.
How to Become an Artificial Intelligence Engineer
1. Now, if we start from the Basics, one needs to earn a Bachelor’s Degree first. It can be from either of the following areas or Subjects:
- Computer Science
- Information Technology
2. The next step is to fine-tune your Technical Skills. An important thing to note here is that in order to become an AI Engineer one not only needs to be good at programming but also good at Software Development techniques and practices.
They need to be knowledgeable both theoretically and practically about the following topics:
- Software Development Life Cycle
- Modularity, OOPS, Classes
- Design Patterns
- Statistics and Mathematics
- Machine Learning
- Deep Learning & Neural Networks
- Electronics, Robotics, and Instrumentation (Not a Mandate)
3. Apart from technical Skills there are also must have Business Skills one must possess while planning on How to Become a Successful Artificial Intelligence Engineer. Some of these skills include:
- Analytic Problem-Solving
- Effective Communication
- Creative Thinking
- Industry Knowledge
4. Now, these skills can either be achieved through practice or by opting for a Master’s Degree. As AI is a newly emerging topic in today’s world, a lot of recent discoveries and research is going on which can be useful for your thesis. Going for a Master’s Degree in Data Science, Machine Learning or Computer Science is advised.
Another Option is to go for Industry Certifications for Machine Learning, Deep Learning or Data Science. This will add a lot of value to your resume and will help you get in-depth knowledge of topics both theoretically and practically. Which will, in turn, help you get an edge over other competitors.
In the roadmap of ‘How to Become an Artificial Intelligence Engineer’, we saw some Technical and Business Skills required. Let’s have a closer look at those skills, starting with Technical Skills:
- Programming Languages (R/Java/Python/C++)
One needs to be good at programming languages and not only that it’s important to have a solid understanding of classes and data structures.
Sometimes Python won’t be enough. Often you’ll encounter projects that need to leverage hardware for speed improvements. Make sure you’re familiar with basic algorithms, as well as classes, memory management, and linking.
AI & Deep Learning with TensorFlow
- Instructor-led Sessions
- Real-life Case Studies
- Lifetime Access
You’ll need to be intimately familiar with matrices, vectors, and matrix multiplication. If you have an understanding of derivatives and integrals, you should be in the clear. Statistics is going to come up a lot.
At least make sure you’re familiar with Gaussian distributions, means, and standard deviations. You need to have a firm understanding of Probability to understand models like
- Naive Bayes
- Gaussian Mixture Models and
- Hidden Markov Models
- Applied Math and Algorithms
Having a firm understanding of algorithm theory and knowing how the algorithm works are very important. You will need to understand subjects such as Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential equation, and Summations
All this math might seem intimidating at first if you’ve been away from it for a while. Yes, Machine Learning and Artificial Intelligence are much more math-intensive than something like front-end development.
- Language, Audio and Video Processing
Natural Language Processing combines two of the major areas of work i.e. Linguistics and Computer Science and chances are at some point you’re going to work with either text or audio or video.
So it’s necessary to have good control over libraries like Gensim, NLTK, and techniques like word2vec, Sentimental Analysis, and Summarization.