Things You Should Know If You Want to Become a Machine Learning Engineer


Machine learning is the next big thing in the market. Have you seen machines that do activities without any human involvement? This is what machine learning engineers do. They develop machines and systems which can learn and apply knowledge.

How is artificial intelligence changing the job scenario for machine learning engineers?

Artificial intelligence and machine learning have been successful in touching almost every aspect of our daily life. It may be the voice-activated virtual assistants like Siri and Alexa, or Predictive technologies used by companies like Netflix and Amazon for a better understanding of the customers.

Artificial intelligence makes the computer do the tasks which earlier needed human intelligence and machine learning is about building the algorithm for the machines which helps them to identify patterns and thus give a better insight into the data. Countries around the world are continuously working on strategies and initiatives to guide the development of artificial intelligence.

Lately, organizations from almost every sector are investing in AI tools and techniques, thus boosting their companies. Currently, AI investments are being dominated by large tech companies like Baidu, Microsoft, Google, Apple, Facebook, and so on. And almost 10%-30% of non-tech companies are adopting artificial intelligence, depending upon their industry.

There has been considerable advancement in the automobile industry with the implementation of Artificial intelligence with vehicles. Self-driving cars were something impossible without IoT working closely with AI. Then there is the Facial recognition feature by Google, which helps to identify a person using digital images or patterns. These technologies are changing the way people have expected their life to be.

As per a recent study, Artificial intelligence will be creating almost 58 million new jobs by 2022, giving a major shift in quality, location, and permanency the new jobs. BY 2025 machines will be taking over the work tasks that are being performed by humans by almost 71%, with the human workforce focusing more on productive tasks. This creates the need for reskilling and upskilling of the current workforce.

In a current report, the top decision-makers of IT/ITES observed that Machine learning and other AI-powered solutions would play a major role in shaping future workplaces. With the latest technological advancements, the tech companies are on the lookout for talents equipped with a better understanding of these technologies.

Here are some of the skills needed for becoming a machine learning engineer.

Programming skills:

Machine learning calls for a stronghold over programming and software development skills. It’s all about creating dynamic algorithms. Being clear with the fundamentals of analysis and design can be an added advantage for you. Here are the skills that you should be acquainted with:

  • Fundamentals of Programming and CS: 

    Machine learning involves computation of huge sets of data which requires knowledge on the fundamentals concepts such as computer architecture, data structures, algorithms, etc. The basics of stacks, b-trees, sort logos, or the parallel programming problems come in handy when we talk about the fundamentals.

  • Software design: 

    Being a machine learning engineer, you will be creating algorithms and systems to integrate with existing ecosystems other software components. And for this, a stronghold over in Application Programming Interfaces (APIs) like web API’s, static and dynamic libraries, etc. are essential for sustenance in the future.

Programming languages: 

Machine learning is known for its versatility and is not bound to any specific language. All it needs is the required components and features, and you can virtually use any language if it satisfies the above condition. ML libraries have got different programming languages, and each language can be used for a different task.

  1. Python:  

    One of the popular languages used among machine learning engineers is Python. It has got many useful libraries like NumPy, SciPy, and Pandals which help in the efficient processing of data and better scientific computing. It has got some specialized libraries like Scikit-learn, Theano, and TensorFlow, which allow learning algorithms using different computing platforms.

  2. R Language: 

    Developed by Ross Ihaka and Robert Gentleman, this one of the best languages used for machine learning tasks. Coming with a large number of algorithms and statistical models, it is specially tailored for data mining and statistical computing.

  3. C/C++:

    C/C++ use is pretty much lower when we talk about the programming languages needed for machine learning. But it cannot be as it is used to program the infrastructure and mechanics of machine learning. In fact, a number of ML libraries are actually developed in C/C++ and wrapped around with API calls to make it available for other languages.

Although the language is a bit different from traditional languages, it is not difficult to learn.

Basic skills needed:

Machine learning is a combination of math, data science, and software engineering. And no matter how many certifications you have got, but you should be well acquainted with these basic skills to be master in your domain:

  • Data modeling:

    Data modeling is a process used to estimate the structure of a dataset, for finding patterns and at times when data is nonexistent. In machine learning, we have to analyze unstructured data, which relies wholly on data modeling. Data modeling and evaluation concepts are needed for creating sound algorithms.

  • Statistics:

    Statistics is mainly the creation of models from data. Most of the machine learning algorithms are building upon statistical models. It has got various other branches which are also used in the process like analysis of variance and hypothesis testing.

Along with these two, there is one more basic skill, which is of utmost importance – Probability. The principles of probability and its derivative techniques like Markov Decision Processes and Bayes Nets help in dealing with the uncertainties and make reliable predictions.

These are many skills which are needed to become a machine learning engineer, and many institutes provide professional Machine learning certification course. They are playing a major role in the rise of AI and efficient machine learning engineers by guiding the participants through the latest advancements and technical approaches in artificial intelligence technologies.


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