Machine Learning, the booming career option shaping the job market

You'd be hard-pressed to find a field generating more buzz than Machine Learning these days. Jobs related to Machine Learning are growing rapidly as companies try to get the most out of emerging technologies. The most compelling thing about Machine Learning is its seemingly limitless applicability. There are hardly any fields (virtually) where machine learning does not apply. The fields that are immensely impacted by machine learning include education, finance, computer science and more, healthcare etc. So, one can easily guess that several machine learning skills are in high demand in the global marketplace today.

The Indian IT industry is expected to add around 1.80 lakh to 2 lakh new jobs in 2018. This year will see a sharp increase in demand for professionals with skills in emerging technologies such as Artificial Intelligence (AI) and machine learning, even as people with capabilities in Big Data and Analytics will continue to be the most sought after by companies across sectors, say sources in the recruitment industry.

Now, you might be wondering what it takes to get a machine learning job. One must have a deep understanding of a broad set of algorithms and applied math, problem-solving and analytical skills, probability and statistics and programming languages such as Python, C++, Java etc. Above all, to explore this subject need innate curiosity.

In addition to that, there are few skills required to learn the subject and get a job in this field:

1. With programming languages like Python and R and their packages making it easy to work with data and models, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design. So, one must be able to write high-quality software.
2. It is necessary to work with large data sets. As the cost of data storage is on a dramatic downward trend, there are more and more data sets from different domains to work with and apply models to.
3. Try to expand your expertise in Unix Tools. Since all of the processing will most likely be on the Linux-based machine, you need to learn their functions to utilize them well.
4. Problem-solving skills are required to learn and understand the signal processing techniques. Feature extraction is one of the most important parts of machine learning. Depending on the type of problem you are trying to solve, you may be able to utilize really cool advance signal processing algorithms like wavelets, shearlets, curvelets, contourlets, bandlets. Learning about time-frequency analysis, and applying it to your problems will also help.
5. To perform quantitative analysis on the system, product intuition is a necessary skill. This includes understanding the complex system that generates all of the data that data scientists analyze. A data scientist who understands the product well can generate hypotheses about ways the system can behave if changed in a particular manner. The traditional analytics skill set includes defining key primary and secondary metrics that the company can use to keep track of success at particular objectives.

Staying curious about your work surroundings and industry remains a critical quality in an employee or a learner and is always in demand. This shows that you are investing time and energy to update yourself on new concepts, ideas. Machine learning is already an incredibly powerful tool that can do a surprisingly good job of solving really difficult classification problems. The next tier of demand is for people who can build practical implementations, especially in collaboration with a cutting-edge research team. Simply put, the popularity of machine learning has grown with the expansion and accessibility of data analytics in various industries and it's a major advancement in the way we dwell in our local and global economy.

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