Technologies You Must Know To Build a Career in AI and ML
Apart from being two of the hottest job designations and industries right now, Artificial Intelligence (AI) and Machine Learning (ML) are incredibly interesting. It is unbelievable to realize the potential of these technologies and how much they can shape our future. AI and ML have left most of the IT professionals aspiring for a career in this sector and people have all been upskilling and making their way towards becoming AI experts. If you are someone who aspires to land a career in this industry but unsure of how to go about it, we have a resourceful write up for you.
We have compiled a list of technologies you must know to build a career in AI and ML. Check it out.
It goes without saying the having hands on experience with the most commonly used programming languages is mandatory. It is not enough if you know to work on one programming language when it comes to AI and ML. You need to be multi and interdisciplinary as each programming language serves a distinct purpose. If C++ has the ability to let you swiftly code, Hadoop lets you implement reducers and mappers (because its Java-based) and R works best with plots and statistics. You need to be versatile when it comes to programming languages and have a sound practical experience.
Today, the amount of touch points that work on generating and extracting data have increased substantially and because of this, experts mostly work on large chunks of data sets that span across millions of rows. In such cases, it becomes inevitable to work on not just one system but over a cluster of systems. This distribution of data sets across the systems in a cluster is called distributed computing and you need to have exposure to the tools and techniques of it. For distributed computing, we recommend working on EC2 by Amazon, Apache Hadoop and more.
If you intend to make your life easier amidst the sweet challenges you face while working on a project in this industry, you have to learn some of the most essential Unix tools like grep, ut, tr, sort, sed, awk , cat and more. You need to know the function of each tool and have the sense of application at the right times to simplify your workflow better.
Algorithms are one of the most integral parts of artificial intelligence and machine learning. You need to be strong both theoretically and practically for a seamless career shift into the industry. You need to understand the logic behind each algorithm, its functionality and application. You need to possess a strong understanding of partial and differential equations, convex optimization, gradient decent, lagrange and more. Besides, signal-processing algorithms like shearlets, contourlets, wavelets, and more will help you work on feature extraction.
ML algorithms can be accessed using APIs, packages or libraries. However, the ability to sense the most effective algorithm requires the right skill. From choosing an appropriate model such as neural net or decision tree and deciding on a learning progression (such as gradient descent, bagging, or linear regression) to having an idea of how hyperparameters influence learning, you need to have the ideal knowledge and exposure.
Artificial Intelligence and Machine Learning are very interesting once you get started with them. If you have a value addition to our suggestions, drop them on your comments.
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