“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” –Ray Kurzweil, American Inventor and Futurist
Machine learning (ML) and artificial intelligence (AI) are ruling the digital world today. These technologies have the ability to completely transform the way a business operates. With the increasing adoption and business applications of AI and ML, many IT professionals are choosing AI and ML as their career. However, considering the competition to enter the domain, it may be difficult to stand out in a pool of applicants. This article discusses the skills and certifications you need to get hired as an AI developer, machine learning engineer, or a research scientist.
Skills Required for a Career in Machine Learning and Artificial Intelligence
A graduate, master’s, or doctorate degree in Computer Science is a must to enter the domain of AI and machine learning. Since machine learning and AI jobs entail development of algorithms, problem-solving and analytical skills come in really handy when considering a career in this field. Let’s take a look at a list of skills that will help you build your career in artificial intelligence.
1. Statistical Skills
As an AI professional, you will be expected to have an in-depth understanding of statistics and probability to understand complex algorithms. Today’s AI models, at their most basic forms, depend on finding patterns in large amounts of information. As an architect of these systems, you must be well-versed with the statistical methods used to derive insights from data.
Moreover, you must also be familiar with common AI models, such as Gaussian Mixture Models, Naïve Bayes, Hidden Markov Models, etc. These principles are based on complex statistical proofs and theorems, requiring AI developers to have an in-depth knowledge of the subject to understand their workings. Thus, detailed knowledge of statistics helps programmers become better AI professionals.
2. Mathematical Skills and Probability
Anyone wanting to pursue their career in artificial intelligence or machine learning is expected to be an expert in applied mathematics. Apart from statistics, AI as a field also features many mathematical concepts to create artificial intelligence. One of the most prominent mathematical principles in use is probability.
Probability determines a variety of outcomes in AI, with a deeper understanding of the subject being integral to creating AI models. Apart from these, AI professionals also need to have excellent analytical and problem-solving skills that come from developing one’s mathematical skills.
3. Programming Skills
Math is just one of the skillsets that aspiring AI and ML professionals are expected to have. This is only one half the requirement, the other half is one’s expertise in programming languages, such as Java, C++, Python, and R.
While C++ helps engineers increase the speed of their coding process, Python will help them understand and create complex algorithms. Python is also the go-to choice for ML developers, and also offers various libraries and frameworks to ease the process of creating an AI model. Similarly, R and Java help professionals understand stats and implement mappers, respectively. They are important considering the role of visualization in explaining AI.
4. Advanced Signal Processing Techniques
Feature extraction is an integral characteristic of machine learning. To understand the next feature and how to implement it is an important part of managing a model’s deployment. To this end, AI and ML engineers are expected to be familiar with a variety of advanced signal processing techniques.
Digital signal processing has also seen a revolution of sorts with the rise of AI, enabling techniques such as layered signal representations, nonlinear function approximation, and nonlinear signal prediction. This means that aspirants will need to have a working knowledge of algorithms like curvelets, bandlets, and shearlets to master feature extraction.
5. Distributed Computing
Almost all the AI job roles require professionals to deal with complex and large datasets, which are difficult to process using a single machine. These datasets have to be distributed equally across an entire cluster, the reason why all AI and ML professionals are expected to be experts in distributed computing.
This includes expertise in applications, such as MongoDB, along with experience in creating and operating cloud environments. With the rise of ML-focused cloud services, companies are looking for individuals who have experience working with them.
Apart from these skills, AI and ML professionals should also have a good command over Unix tools and possess data modeling and evaluation skills. This is due to the fact that Unix is one of the most common working environments for AI/ML professionals and the base for many cloud services.
Career Paths in Machine Learning and Artificial Intelligence
There are five major roles that you can choose from, on your AI and ML career path. Let’s take a look at each one in detail.
1. Machine Learning Engineer
Machine learning engineers are programmers who program the machines to perform specific tasks. They are responsible for managing as well as building machine learning platforms. This role is ideal for professionals who have an educational background in programming and engineering, as most of the learned skills will enable easier role transitions.
Machine learning engineers are also required to develop their skills in various programming languages, such as Python, Java, and Scala. ML engineers should also have a working knowledge of how to manage large amounts of data using utilities such as R and SQL. Not only should they be able to apply predictive models, but they should also be able to leverage big data best practices while dealing with enormous datasets.
Aspiring ML engineers will benefit from a few years of experience in agile development practices and leading development tools, like IntelliJ and Eclipse. A master’s degree or a doctoral degree in computer science or mathematics along with strong computer programming and analytical skills will further up your chances of getting hired.
2. Data Scientist
Data scientists are responsible for the collection, analyzing, and interpretation of large and complex datasets with the help of predictive analytics and machine learning. They may also be required to implement new machine learning models to help an organization make more timely and effective decisions.
Data scientists play a very important role in developing algorithms that allow data to be collected and cleaned for analysis. They are also tasked with creating a healthy data workflow and ensuring that predictive models get the latest data to derive insights from. To get hired, professional should be familiar with tools like Spark, MapReduce, Hive, Pig, and Hadoop. They should also develop their skills in programming languages, such as SQL, Python, Perl, and Scala.
Aspiring data scientists should ideally have a master’s or doctoral degree in computer science. Professionals with two or more years of machine learning experience are preferred by recruiters. Some machine learning experience, along with some familiarity with cloud tools like Hadoop or Amazon S3, further increases your chances of getting hired.
3. Business Intelligence (BI) Developer
Business intelligence developers are responsible for analyzing complex data sets so that market and business trends can be identified. They play an important role in turning the business into a profitable one. Business intelligence developers, model, design, as well as maintain complex data in highly-accessible data platforms in the cloud.
Business intelligence developers are expected to oversee the transition of a business into a data-driven one. They do so by communicating the benefits of data to stakeholders in the company, thus evangelizing the benefits of data. Candidates with strong analytical and technical skills are preferred for the position of Business Intelligence Developer.
They are also expected to demonstrate problem-solving skills and possess communication skills to collaborate with non-technical colleagues efficiently. Candidates are required to have a bachelor’s degree in computer science, engineering, or any other relevant field. To get hired, candidates should have some experience in SQL queries, data mining, data warehouse design, and BI technologies.
4. Research Scientist
Research scientists are masters in multiple AI disciplines, such as deep learning, machine learning, computational statistics, and applied mathematics. Candidates are expected to possess knowledge about graphic models, computer perception, natural language processing, and reinforcement learning.
Research scientists are usually tasked with pushing the limits of known AI and explaining as-of-yet unknown advancements. In addition to innovating new methods of bringing artificial intelligence to the forefront, AI research scientists also have to explain AI using visualizations,
Aspiring research scientists are expected to possess a master’s or doctoral degree in computer science. Some organizations may also recognize an advanced degree in a related field along with experience. Professionals should have a good understanding of distributed computing, parallel computing, machine learning, and artificial intelligence.
5. Big Data Engineer/Architect
Big data engineers and big data architects are responsible for designing, planning, and developing the big data environment on Spark and Hadoop systems. They are in charge of developing an ecosystem that will allow business systems to communicate with each other and collate the data.
Big data engineers handle the way data flows in an enterprise setting and ensure that it gets where it needs to be. This also includes provisioning and creating architecture appropriate to the use-case, meaning that they need expertise in configuring databases and distributed computing deployments.
Professionals with a Ph.D. in computer science or mathematics are preferred for this role. They are also expected to have prior experience of various programming languages, such as Java, Python, C++, and Scala; and in-depth knowledge about data virtualization, data mining, and data migration.
List of Artificial Intelligence Certifications
Since its advent, AI has been ever-growing and ever-progressing. Considering the various opportunities that are available in this field, a lot of professionals are choosing AI as their career. If you wish to start your AI career journey, the following courses will help you lay the foundation.
1. Learn with Google AI
This certification course familiarizes students with the fundamentals of machine learning. It also covers the basic concepts of TensorFlow and designing neural nets. This course is ideal for candidates who do not have any prior knowledge of machine learning. Candidates who are already experts in machine learning can use the course as an introduction to TensorFlow.
2. Stanford University Machine Learning
Stanford University’s machine learning course is beneficial for professionals who want to learn how to implement machine learning from enhancing web search and speech recognition. The course covers statistics topics, such as backpropagation methods and linear regression in detail. Through this course, candidates can also get familiar with MATLAB, a widely-used AI programming language.
3. Google – Machine Learning
Google’s machine learning is a more in-depth course that is ideal for candidates who are novices with a little machine learning experience. Google’s course focuses on deep learning as well as the design of self-teaching systems that can earn from large and complex datasets. This course is ideal for candidates who want to work on technologies, such as machine learning and neural network and work as data analysts, machine learning engineer, and data scientists. They can also choose to work as enterprising individuals who wish to make use of a variety of open-source materials and libraries that are available.
4. Columbia University Machine Learning
Columbia University’s Machine Learning course teaches models, applications, and methods to help students solve real-world problems with the help of supervised and unsupervised learning. The course covers topics, such as sequential models, clustering methods, matrix factorization, topic modeling, classification and regression, and model selection.
5. Nvidia – Fundamentals of Deep Learning for Computer Vision
Computer vision is a sub-field of building computers that have the ability to see like humans, that is, by processing visual information. The course covers the technical fundamentals of computer vision. It also teaches how situations or problems are identified for image classification and object recognition. The course also educates students about the important role that GPUs have played in the emergence of computer vision applications.
6. MIT – Professional Certificate Program in Machine Learning and Artificial Intelligence
The Professional Certificate Program in Machine Learning and Artificial Intelligence certificate enable students to learn the essential concepts and develop the skills required to develop effective AI systems. The course teaches students to apply industry-relevant knowledge in AI and machine learning.
Candidates applying for this course are required to have a minimum of three years of professional experience and a master’s degree in physics, statistics, computer science, or electrical engineering. The course is ideal for professionals who wish to learn the key concepts, algorithms, and practical examples of AI and machine learning.
As an aspiring AI professional, you should ensure that you are always aware of any current and upcoming advancements in artificial intelligence. You should stay abreast of the latest events and conferences happening in the space and the topics of discussion at these events. Subscribing to newsletters of AI-specific sites, such as Google AI and DeepMind is another way to stay updated about the latest happenings in the artificial intelligence world.
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Author: Prajakta Patil
Prajakta Patil has 7 years of experience in content development. As an Editor for Toolbox, she handles the Expert Insights and Quick Reads columns. Previously, she has worked as a Content Editor at Times Internet Limited and a Sub-editor at Buzzle Software Solutions. She has also worked with various newspapers in the initial years of her career. Passionate about child welfare, Prajakta works with LetsRISE to help underprivileged children of human trafficking victims. A complete wanderlust at heart, she loves spending time with her dogs.
Author: Anirudh V K
Anirudh V. K is a tech enthusiast and journalist who keeps updated with all the latest advances in computing. He is interested in computer hardware, video games, reading books, and finding beauty in nature.