Machine learning is one of the hottest new trends in the IT industry right now, and it is all set to gain prominence in the future tech industry. With various companies like Microsoft, Apple, and Google rolling out their own developer tools, developer interest and engagement is at an all-time high. According to 96 percent of professionals, machine learning projects will rise dramatically in the near future, leading to job growth. This means you can apply for a lucrative machine learning job as long as you have the necessary skills. The question is, what qualifies as “necessary” when it comes to cementing a promising career and actually getting a machine learning job? While the scope of machine learning is vast, you can find success by honing a few specific skills — some basic and some not-so-basic. Let’s see what they are.
Machine learning is considerably different from other avenues of technologies. How? Well, the growth rate of this technology is impressive. New methodologies, libraries, algorithms, techniques, and paradigms are frequently exploding onto the scene. For this reason, you must learn to stay current all the time. A good way to do so involves subscribing to the best tech blogs, reading research papers, and following technical conferences.
At the same time, keep in mind that the growth trajectory of machine learning is being influenced by industrial applications. So, depending on the type of projects you select and the effort you put into learning new tricks and tips of the trade, you can manage the demands and requirements of this industry. This will help develop a well-paid, fulfilling, and growth-focused career in the field of machine learning.
You will not be able to hold onto an ML job if you lack knowledge in these languages. Each of them serves a specific purpose. C++ is useful for speeding up any coding work you might have on your plate, while R works wonders when it comes to plots and statistics. Hadoop is a Java-based programming language, which means you’ll have to implement reducers and mappers in Java.
The more theories you learn, the easier it gets to understand algorithms. But understanding Hidden Markov models, Naive Bayes, and Gaussian Mixture Models is difficult without a thorough understanding of statistics and probability. You can use statistics in the form of a model evaluation metric for p-values, receiver-operator curves, and confusion matrices.
To be in demand for a hot machine learning job, you must learn everything you can from the world of programming. Brush up on your programming and computer science knowledge because getting a machine learning job requires you to be totally comfortable with concepts like algorithms, data structures, and computer architecture. Bear in mind that ML algorithms do not function in isolation and are mostly a part of bigger systems. So, ML programmers must get comfortable working with APIs to create future-ready interfaces. You will also find it useful for learning about the fundamentals of the software development life cycle.
You must continually evaluate the efficiency of a specific model. Based on the tasks you have at hand, you must select suitable error measure and accuracy, and implement a suitable evaluation strategy.
In more cases than one, a machine learning job involves working day in and out with huge volumes of data. Right now, it is impossible to process this data with a single machine; the right way would be to distribute it throughout a whole cluster. Projects like Apache Hadoop and cloud services such as EC2 from Amazon simplify the process and even make it a lot more cost-effective.
When you are part of an enterprise, you may get the chance to work with signal processing professionals who belong to the ML and data science teams. However, it’s a good idea to learn some of the fundamental concepts and ideas from this body of knowledge yourself. Focus on features extraction to further your machine learning career, and the best way to do that is signal processing.
Related algorithms allow you to resolve problems in various innovative methods using algorithms like curvelets, wavelet, bandlets, and contourlets. Another significant signal processing technique is Fourier analysis and convolution that provides great benefits to machine learning professionals. However, keep in mind that signal processing is not always easy to learn. But once you do achieve this milestone, you will become an unstoppable force in the field of machine learning.
You should brush up your skills in UNIX tools like grep, awk, cat, cut, sort, sed, tr, find, and others. Because all the processing is performed on a Linux-based machine, access to such tools is a must. Become familiar with their functions and learn to utilize them carefully. UNIX tools can make your life easier and raise the odds you’ll be considered for a machine learning job.
Machine learning is a booming industry right now, and a lot of people are trying to get in on the ground floor of this rising trend. But how can you develop your skills? Well, you need to start with a strong quantitative background. Without that, charting a career path in ML can be difficult.
But if this is something you are passionate about and want to make this your lifelong goal, you should not allow your background to discourage you from pursuing your career ambitions. There are lots of courses available that have been endorsed by the industry and provide you the opportunity to learn all that you need to succeed in machine learning.
Featured image: Shutterstock
In one of the biggest announcements at this month's Ignite 2019, Microsoft gave us details Azure Arc, a new set…
If you logged into Azure Portal over the past few days, you may have suffered a little disorientation. Some new…
Cloud computing offers many benefits to small businesses, but it also brings certain risks, including the risk of bankrupting your…
Configuring your IaaS Azure virtual machines to take advantage of accelerated networking can vastly improve network performance. Here’s how to…
Active Directory and Exchange work very closely with each other, which is why you must make sure you are using…
What could cause an Azure Application Gateway to all of a sudden generate 502 error pages? A quick investigation and…