8 or 10 Top Skills You Need To Get a Machine Learning Engineer Job
College students about to graduate are looking forward to becoming machine learning engineers. The seats for AI and ML Courses are fast filling. Therefore, let’s quickly find out what skills one should possess to become an ML engineer.
Who Is a Machine Learning Engineer?
An ML engineer is a sophisticated computer programmer who develops machines and systems that can learn and apply knowledge without any specific direction. The aim is to ensure that the machines are able to perform certain tasks.
Requisite Skills for a Machine Learning Engineer
Here are the top 10 must-have skills for an ML engineer:
1. Data Modelling – A ML engineer’s job is to handle large volumes of data and make sense of unstructured data models to predict the outcome. The knowledge of data modeling is critical in analyzing such unstructured data. One who knows the science of data modelling knows how to find patterns, fill gaps between places with non-existent data, and identify underlying data structures. The science of data modeling is effective in the creation of algorithms as well.
2. Applied Mathematics– A major part of machine learning algorithms involves having to make trustworthy decisions in uncertainties. Since probability helps to predict future instances, knowledge of mathematical equations such as Bayes Nets and Markov decisions is useful in this regard. Other mathematical topics such as linear algebra, gradient descent, convex optimizations, partial differentiation, multivariate calculus, algorithm, and optimization also find application in machine learning.
3. Statistics– Talking about the application of applied mathematics, the roots of the algorithms that an ML engineer has to build lie in statistics. Statistics finds its applications while creating tools and tables. Therefore, if you have knowledge about statistics and its branches, such as hypothesis testing, Matrix multiplication, variance analysis, mean, Gaussian distributions, derivatives, and integrals, it will be beneficial for your ML engineering career.
4. Communication and Rapid Prototyping – In various scenarios, an ML engineer has to rapidly test numerous ideas to find out the one that works. Zeroing down is not enough. An ML engineer also has to communicate the idea within the internal and cross-teams. This involves explaining the ML concepts to many with little to no experience in the field. The better your communication, the better your hiring chances.
5. Learning Programming Languages– A major part of Machine learning involves algorithms. This makes it necessary for an ML engineer to have a good knowledge and understanding of various programming languages. Since, on a regular day, a machine learning task involves working on large sets of data, it becomes important to be strongly skilled in aspects such as data structures, algorithms, computer architecture, and complexities.
As an ML engineer, you will have to frequently use the libraries and algorithms developed by other organizations and developers. Examples of such libraries are Google TensorFlow, Microsoft CNTK, Apache Spark, and MLlib. One of the most preferred programming languages used by ML engineers and data scientists is Python because it has a number of libraries that are useful in scientific computing and data processing. By supporting ML libraries such as Theano, sci-kit-learn, and TensorFlow, Python also eases the training of algorithms.
Another language that you will find helpful for data mining and statistical computing is R. The language that will come to your rescue when programming the interface and developing embedded systems and the mechanisms of machine learning are C/C++ for the libraries can first be developed in C/C++ before making them available in the other languages through API calls. If you want to do programming for smart cards, smart homes, and sensor devices, you will find C/C++ to be quite useful in these endeavors. You can brush up on your old books on programming and even take up programming courses to enrich your skills.
6. Software design – Along with machine learning, an ML engineer must also possess a strong design interface, API (application user interface), and related skills. These technical skills are what will enrich machine learning and sustain it.
7. Reading skills – Read up on the topics to gain no knowledge about them. You can read up on research papers on Google Map-Reduce, Google file system, Google Big Table, etc. You can also read up from the numerous free books that are available on the internet.
Along with this, you must also update yourself. Attend conferences, research topics that are relevant to machine learning, update yourself on changes related to different components of machine learning, and follow machine learning-related news to stay updated on present-day trends. Business acumen and an understanding of how elements work to create a successful business model are necessary.
8. Signal Processing – It’s good to know about Time-frequency analysis, feature extraction, and advanced signal processing algorithms such as curvelets, bandlets, shearlets, and wavelets as an ML engineer because, in this role, you will come across complex situations needing a resolution.
9. Neural Network Architecture– Neural Networks are a predefined set of algorithms in machine learning literature, used to implement a task. This concept which has brought about a revolution in machine learning, helps the machines to perform like the human brain. This feature enables it to solve problems that are too complex and impractical for human beings to decode. Some instances in which neural networks find applications are speech recognition, language translation, and image classification.
10. Reinforcement Learning– It has driven some of the most exciting projects in the areas of artificial intelligence and deep learning, which are critical to Machine Learning. Reinforcement learning will be especially important if you want to delve into the artificial-intelligence related areas such as self-driven cars and robotics.
Conclusion
Besides the ones listed above, it will be great for your machine learning career if you can master a few other skills, such as Spark, Hadoop, and Distributed Computing. Since machine learning is mostly sought by individuals with a knack for artificial intelligence, a training course in the same, coupled with language, audio, and video processing, can take you miles ahead.
