Skills Required to Succeed in an AI and Machine Learning Career
Artificial intelligence and machine learning skills are in high demand as the technology industry continues to grow and change at lightning speed. In many ways, these specialties are the cutting edge of the cutting edge, loaded with promise for those interested in how to get a job in AI or machine learning. Once mostly relegated to science fiction, artificial intelligence (AI) applications have become a fixture in many people’s daily lives.
While AI includes several subfields, which we will discuss here, the evolution of AI from imagination to reality is partially fueled by the growth of machine learning, one of the subsets of AI. Let’s explore the distinctions between AI and machine learning and how you can acquire the artificial intelligence skills and machine learning skills necessary to succeed in these fields.
What is AI?
At its root, the focus of artificial intelligence has always been programming or teaching computers to imitate human reasoning, problem-solving, and decision-making. However, AI is a broader and continually evolving field, described by AI expert and researcher Zachary Lipton as “aspirational, a moving target based on those capabilities that humans possess but which machines do not.”
The broad field of AI includes the subfield of machine learning and has roots in a number of disciplines such as:
- Math
- Statistics
- Philosophy
- Psychology
- Cognitive Science
- Military Science
Artificial intelligence includes a vast and ever-changing list of methods and developments. For example, early AI achievements included utilizing a method known as tree search algorithms to train a computer to play chess. Once a jaw-dropping feat of technological innovation, this application is now so common that it’s carried (and often ignored) in most of our pockets on our phones.
More recently, human-AI interaction applications such as Siri, Alexa, and Google Home are more familiar examples of artificial intelligence becoming commonplace in everyday life. AI is also becoming a regular part of business operations, offering new solutions for managing sales, customer relations, electronic security, entertainment, and more. There are few areas of industry or culture that are not in some way influenced by the advancements in AI technology.
As AI experts continue to push the technology forward, it is expected to revolutionize various industries and ultimately significant parts of our day-to-day realities.
- Health care treatments will be evaluated and adjusted more quickly.
- Financial fraud will be prevented rather than simply detected after the fact.
- Smarter personal assistants will plan our days, our business strategies, and our vacations.
All of this will be possible because of the work of those with current and evolving artificial intelligence skills.
What is Machine Learning?
While the terms artificial intelligence and machine learning are often used interchangeably in non-technical spaces, they are not synonymous. A subfield within AI, machine learning utilizes mathematical data models to enable a computer to learn independently without constant human direction. Over time the computer begins to learn from data it receives without additional programming.
Consider some examples of machine learning in the real world:
- Product and Service Recommendations: Have you noticed over time that streaming services gradually become more accurate in predicting what shows and movies you might like? Services like Netflix use machine learning to analyze your viewing habits and make recommendations.
- Fraud Detection: Financial and other institutions are using machine learning algorithms to track fraud patterns and other online criminal activity over time and learn how to detect and prevent them.
- Speech Recognition: AI personal assistant software and devices such as Alexa, Siri, and Google Home are examples of machine learning applications of artificial intelligence. The software learns to recognize your voice and words and translate it into searches, text, or other uses.
- Ranking Posts on Social Media: Social media services like Twitter utilize machine learning to track your engagement and determine what kind of posts and content are likely to be most relevant to you.
- Medical Diagnosis: Machine learning is being used in a variety of medical and healthcare contexts, including image recognition to identify cancerous tissues and fluid analysis.
These are only a few ways machine learning skills are increasingly relevant. Machine learning is widely used in dozens of industries and disciplines and will continue to be a growing field in the years to come.
What are examples of subfields of Artificial Intelligence?
Those interested in a career in AI will benefit from understanding the subfields within AI, as well as the nuances that distinguish them from each other. As the analytics experts, SAS has described, artificial intelligence includes a number of subfields, including:
- Machine learning: Identifies insights within data to continue learning and problem-solving without needing ongoing human direction. Machine learning utilizes methodology from other disciplines, including statistics, physics, and neural networks.
- Neural networks: A type of machine learning based specifically on the way the human brain works. A network of interconnected components analogous to human neurons process external inputs repeatedly to identify connections and meaning in the data.
- Deep learning: A way of utilizing larger, multi-layered neural networks to find and learn complex patterns in high volumes of data. Deep learning leverages new innovations in computer science and training techniques for applications like speech and image recognition.
- Computer vision: A type of artificial intelligence that uses deep learning to recognize patterns and identify the contents of a picture or video. This enables computers to utilize video or photos to understand and respond to their surroundings in real-time.
- Natural language processing: The use of computers to recognize, analyze, and even generate human language in text or speech form.
What Skills Do You Need to be Successful with AI and Machine Learning?
With a clearer understanding of Artificial Intelligence and its subfields, you may now be wondering how to get a job in AI, or within the subfield of Machine Learning. These two disciplines require a range of artificial intelligence skills in addition to a thorough computer science background. Some of the most in-demand Artificial Intelligence and machine learning skills needed in today’s marketplace include:
- Programming languages such as C++, Python, Java, and others
- Domain knowledge
- Mathematical skills in statistics, probability, linear algebra, and graphing
- Neural network architecture
- Deep learning
- Big data and distributed computing
- Shell scripting
- Signal processing techniques
- Cluster analysis
In addition to those hard skills, specific soft skills are also crucial to success in these fields, which can be learned through advanced education in data science or computer science. Multicloud solutions experts Rackspace identify four of these important skills as:
- Ability to build a strong foundation through data preparation and architecture
- Adaptable skills to modernize data architecture
- Capacity to understand and leverage the latest innovations in AI and machine learning
- Integrated understanding to build intelligent AI and machine learning applications
Unlock AI and Machine Learning Skills with a Master of Computer Science or MS in Data Science from WPI
Worcester Polytechnic Institute’s (WPI) is well known for its experiential learning model, which provides real-world knowledge and develops leadership, collaborative, and critical-thinking skills. WPI’s Master of Computer Science and MS in Data Science take place 100% online, and balance theory, technique, and application as they prepare you with the most in-demand skills for sought-after roles in these industries..
These 30-credit hour programs include built-in bridge courses for those with an undergraduate degree outside of these two fields, and allow you to customize your education by choosing one of 4 focus areas, or creating your own from electives in these areas:
- AI & Machine Learning
- Big Data Analytics & Management
- Cybersecurity (MCS only)
- Business Intelligence
WPI’s MCS online degree will help prepare you to build, improve, and innovate technology:
- Learn tools and theories in the design of software, databases, algorithms, and computing systems
- Create and encode algorithms in software languages such as Python
- Analyze and design non-numeric algorithms and writing programs that integrate them with various data structures such as lists, stacks, queues, trees, and graphs
WPI’s MSDS online degree will help prepare you to derive insights from data and articulate these findings into innovative solutions for how we live, work and interact with the world:
- Develop fundamental skills in computing languages, programming concepts, design and analysis techniques, algorithms, statistics, and linear algebra
- Learn to create, manage, and analyze large-scale databases, use relevant statistical methods such as predictive modeling and clustering, and understand machine learning
And, of course, an AI & Machine Learning specialization will help prepare you with artificial intelligence and machine learning skills to succeed in fields that offer promising long-term career paths.
By earning your MCS or MSDS degree at WPI, you will become part of the alumni family at a prestigious, respected university that is ranked:
- #4 National Universities Where Grads Are Paid Well by U.S. News and World Report (2021)
- #5 in Best Career Services by The Princeton Review (2019)
- Among the top 25 STEM Colleges by Forbes, Top 60 Most Innovative Schools by U.S. News & World Report, and Top 30 Best Value Colleges by Payscale.com.
Develop the knowledge you need to design and deploy real-world marketplace solutions. Learn more about the Master of Computer Science and MS in Data Science online programs at WPI.