Machine learning is a hot field, with bright, young, STEM-minded students and professionals worldwide looking to start on a career path that will let them develop technologies and data solutions that promise to power the next chapters of human innovation.
In 2019, Indeed reported that postings for machine learning engineer jobs accounted for 179 of every one million job postings in 2018, with the total number of machine learning engineer postings growing by nearly 350% between 2015 and 2018. The machine learning engineers are already making a difference through projects like the new “intelligent compaction” technology that can help civil engineers reduce eventual potholes in roads.
With innovative solutions like these, there’s no doubt that machine learning experts will be in demand for years to come, but what exactly will these professionals need to know to embark on a machine learning career? More importantly, if you’re interested in machine learning yourself, what will you need to learn? In this article, we’ll cover the most important machine learning skills so you can perform your own skill audit, measure your aptitude for machine learning, or map out the journey ahead.
Before we begin, however, it’s crucial to distinguish between machine learning and data science, two overlapping fields that are equally open to those with machine learning skills.
Machine Learning vs. Data Science
If there were a battle of the tech buzzwords, the final round would surely be fought out between ‘data science’ and ‘machine learning’. Businesses have flocked to data-driven decision-making over the last two decades. As they have increasingly deployed AI technologies to increase productivity, drive value, and improve customer satisfaction, data science and machine learning have become ubiquitous, gracing the tongues of shrewd business execs and eager undergrads alike.
Ten years ago, the Harvard Business Review ordained the data scientist the “sexiest job of the 21st century.” This now feels like an understatement. In recent years, machine learning has earned similar superlatives, with ‘machine learning engineer’ topping Indeed’s Best Jobs of 2019 list and placing fourth among LinkedIn’s 2022 Jobs on the Rise.
Despite the substantial overlap between the two — including in the machine learning skills needed to succeed — there remain important distinctions between the fields. Let’s start with machine learning.
What’s machine learning?
A subset of artificial intelligence, machine learning focuses on the development of mathematical algorithms that allow computers to progressively improve their capabilities — “learning” as they go. As Stuart Russell and Peter Norvig put it in Artificial Intelligence: A Modern Approach, the leading AI textbook, in machine learning “a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems.”
What’s data science?
According to IBM, data science is “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. [It] encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.” A data scientist follows data through the data pipeline:
Identifying research interests and questions
Engaging in data preparation to turn unstructured data (or “raw data”) into useable data
Analyzing that data, often with the help of machine learning algorithms and models
Using data visualization tools to persuasively communicate findings
IBM’s definition of data science emphasizes an important aspect of machine learning’s utility: its ability to provide “hypotheses about the world” that can drive business intelligence and decision-making.
What’s the relationship?
Once you know more about the disciplines themselves, the relationship between machine learning and data science becomes much more understandable:
Machine learning centers on algorithms that can teach themselves to perform complicated tasks much more quickly and efficiently than a human could. Machine learning has applications for medical diagnosis, image recognition, product recommendation, business analytics, and much more.
A machine learning engineer designs, develops, and ships machine learning models — and then upkeeps them once they’re shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects.
Data science extracts business intelligence from big data sets, frequently employing machine learning algorithms and models among its other analytical tools.
A data scientist ideates and executes novel approaches that turn raw data into business insights and solutions. After understanding business needs, they determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data, and then help develop machine learning models and other predictive analytics to efficiently carry out this analysis. After the analysis, a data scientist is usually responsible for communicating the results to relevant stakeholders.
A visualization of the relationship between artificial intelligence, machine learning, deep learning, and data science by Meet Patel.
With this relationship in mind, we’ll move on to the core set of machine learning skills required to greater and lesser extents in today’s hottest machine learning engineering and data scientist roles.
Machine Learning Skills
Whether you’re a machine learning engineer or a data scientist, you’ll need to have a solid foundation in applied mathematics, especially probability, statistics, and linear algebra.
Probability — in mathematics, the calculation of the likelihood that a particular event will occur — is crucial for writing machine learning algorithms because it allows machine learning engineers and data scientists to grapple with the inherent uncertainty that enters machine learning systems trained on incomplete data sets and deployed in the real world.
Statistics — in mathematics, the collection, organization, and analysis of numerical data — is crucial for writing machine learning algorithms and developing machine learning models because it allows machine learning engineers and data scientists (and AIs themselves) to learn from existing data sets and use relevant insights to predict future outcomes.
Linear Algebra — in mathematics, the use of linear equations and matrices to model natural and artificial phenomena and compute with these models — provides crucial computational and data management tools for building machine learning models. Linear algebra is particularly important when it comes to linear regression, computer vision, deep learning, and other analysis of big data sets.
Just as important as applied mathematics for the aspiring machine learning engineer or data scientist are programming skills. Most ML engineers and data scientists are fluent in at least one programming language, with R, Python, Java, and C++ being among the most popular.
In addition to being adept with programming languages, machine learning engineers and data scientists also need to be comfortable working in a variety of machine learning programming environments and libraries, such as PyTorch, TensorFlow, and Pandas.
Core Machine Learning Methods
Applied mathematics and programming skills are crucial prerequisites to designing machine learning models utilizing the three core machine learning methods: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning refers to the use of labeled data sets — where each piece of data is tagged and classified — to train a machine learning algorithm to give the correct output when fed an input. Essentially, supervised machine learning helps algorithms build an ML model by learning by example. A common use of supervised learning is in email spam filters, where an algorithm is fed many examples of confirmed spam and gradually learns to identify it without prompting.
Unsupervised learning refers to the use of unlabeled data sets to train a machine learning algorithm. Rather than learning by example, unsupervised ML algorithms are written to be able to make sense of the data themselves: discovering patterns and relationships, forming clusters, and cutting through the data to get to the most important data points. Recently, a group of researchers detailed in Nature how they used unsupervised learning to distinguish groups of Alzheimer’s and Parkinson’s Disease patients based on their molecular make-up rather than on how the diseases were presenting.
Reinforcement learning differs from supervised and unsupervised learning in that it is explicitly goal-oriented, with machine learning algorithms written to behave in ways that will maximize a numerical reward as they engage with a complex environment, usually through things called Markov decision processes. Reinforcement learning is applied throughout the industry, including in teaching cars how to drive themselves.
While not one of these key paradigms, another important method of machine learning is deep learning, where “artificial neural networks” (ANNs) of algorithms inspired by the synapses and neurons of the human brain are used for tasks in key areas of machine learning like natural language processing (NLP), computer vision, and robotic process automation (RPA). One notable application of deep learning comes in the form of the chatbots that e-commerce outfits are increasingly deploying as their first line of customer service.
A visualization of a neural network by MIT.
Key Machine Learning Areas
While more so for a machine learning engineer than a data scientist, skills in these key machine learning areas — natural language processing, computer vision, and robotic process automation — are also crucial for success in a wide variety of industries.
Natural Language Processing
Borrowing from computer science, artificial intelligence, and linguistics, researchers and engineers working in the subfield of natural language processing (NLP) are concerned with giving computers the ability to understand, and even employ, written and verbal language at a near-human level. In Artificial Intelligence: A Modern Approach, Russell and Norvig identify three goals of NLP: computers are being taught to understand and use human language 1) to communicate with humans, 2) to learn from the vast amount of information encoded in human language (like the internet), and 3) to advance the understanding of the language we’ve developed in disciplines like linguistics and cognitive science.
Current commercial applications of NLP include translation tools like Google Translate, email spam filters, personal assistants like Apple’s Siri or Amazon’s Alexa, and accessibility tools like Youtube’s automatic captioning. Another notable application is Generative Pre-Trained Transformer 3 (GPT-3), a predictive language model developed by OpenAI. The GPT-3 model can produce convincingly human-like text when fed short prompts, an ability K Allado-McDowell harnesses in the Pharmako-AI, a novel co-written with GPT-3.
Computer Vision is an AI subfield with a predictable goal: to endow computers with the ability to perceive, assess, and act on visual stimuli at a level equal to, or even exceeding, human vision. This ability is essential if computers are to become even more active in the physical world in the coming years.
That said, computer vision is already impacting our lives every day. Perhaps the most ubiquitous example is Apple’s Face ID technology, a facial recognition system that precisely maps a user’s facial biometrics. Computer vision is essential for the development of autonomous vehicles, and augmented reality video games like PokémonGo, as well as for a host of industries such as manufacturing and agriculture. Together with NLP, computer vision also plays a role in tasks such as text digitization.
Robotic Process Automation
Robotic process automation (RPA) concerns the automation of complex business processes to drive efficiency and productivity. Rather than robots active in the physical world, in RPA the robots are metaphorical “software bots” that can learn business processes (such as bank transactions) autonomously and then complete them automatically at scale.
While RPA is more difficult to discern as we go about our daily lives, it’s everywhere around us, streamlining supply chain management, executing predictive maintenance on manufacturing systems, and configuring account services for banks, telecoms, and many others.
Machine learning engineers and data scientists alike spend their lives immersed in data. To be successful, they need to be adept with all aspects of the data pipeline, from data collection and preparation to data mining, to data analysis, to data visualization — and even data architecture and cloud computing.
Data Collection and Preparation
Though machine learning is powered by data, the right data is not always easy to come by, and even if it is, often it’s not immediately usable. The early stages of the data pipeline thus entail searching for relevant databases or brainstorming ways to collect useful data, then writing algorithms to extract the data points that will allow for the best analysis.
Every day, massive data sets are collected from our mobile phones and the other sensors, cameras, and microphones that are now ubiquitous in our daily lives. Through data mining — according to SAS, “the process of finding anomalies, patterns and correlations within large data sets to predict outcomes” — machine learning engineers and data scientists probe this big data for insights.
In addition to data mining, machine learning engineers and data scientists need to be able to engage in broader data analytics using machine learning tools. At the small scale, this usually involves choosing — or writing — the appropriate algorithm to extract insights from a data set, while at the large scale ML engineers and data scientists will develop complex machine learning models to perform this analysis. In doing so, they will utilize techniques such as (the aforementioned) supervised, unsupervised, and reinforcement learning as well as other techniques such as linear regression and decision trees.
Gleaning insights from data is useless unless you are also able to communicate these insights to various stakeholders in compelling ways. For machine learning engineers and data scientists, this often means becoming adept with applications like Tableau to create beautiful, information-rich data visualizations that can support the story they want to tell.
Data architecture concerns not what is done to data at each stage of the data pipeline, but rather how this data pipeline is constructed in the first place: “the blueprint for data and the way it flows through data storage systems…foundational to data processing operations and artificial intelligence (AI) applications,” according to IBM. While at larger companies ML engineers and data scientists might not deal directly with data architecture matters — these falling instead under the purview of dedicated data architects — smaller companies tend to seek out candidates who cannot only work inside of a data pipeline but help ensure this pipeline is running smoothly.
While not a “data skill” per se, it’s worth noting that in both machine learning engineering and data science, professionals need to know their way around cloud computing services like Amazon Web Services (AWS). Especially when working with big data, cloud computing provides crucial and sustainable access to data storage and computing power — and many recruiters want to see experience with cloud computing on a candidate’s resume.
While much of the focus when applying for AI jobs is on technical skills, companies are increasingly seeking individuals with strengths in soft skills like critical thinking, leadership, and communication. Both machine learning engineers and data scientists need to be able to think and communicate effectively to collaborate with peers, get buy-in from higher-ups, and manage those reporting to them.
The importance of soft skills should come as no surprise: the World Economic Forum’s list of Top 10 Skills of 2025 is heavy with them.
Image Source: World Economic Forum
It also makes sense: more people than ever before are learning to code, and so as core computer science skills become less of a differentiator between candidates, emphasis on soft skills as differentiators will amplify.
The takeaway? Those freshman writing and humanities distribution requirements might not be a waste of time, but rather your secret weapon in the final-round interview for your dream job.
If looking through these crucial machine learning skills excites you about the possibility of a career in machine learning or data science, what are the next steps? That all depends on where you’re at right now.
If you’re coming out of high school and looking to start building machine learning skills, you should check out our picks for the best bachelor’s degree programs in computer science, machine learning, and artificial intelligence.
If you already have a bachelor’s degree in a computing-heavy STEM field, it’d be a good idea to read up on what a master’s degree in artificial intelligence or machine learning entails. You might also look into whether an online master’s in AI or ML might be right for you.
If your bachelor’s degree is in the social sciences or humanities, or even in certain STEM fields, there might still be a place for you in a machine learning master’s program. But you might need to take a bridge course or bootcamp first before starting the degree proper. While you’ll learn many of the skills above through graduate study, most programs require that you enter with the applied mathematics and programming skills already in hand.
If you already have machine learning skills and are looking to figure out how best to feature them in your job applications, check out our primer on how to craft a compelling machine learning resume that stands out from the rest.
If you’re coming out of high school and looking to become a data scientist, feel free to peruse the articles we’ve linked above — given the crossover, there is plenty you can learn from them. But you might also consider heading over to DataSciencePrograms.com for great recommendations for data science bootcamps and degree programs.
If you already have a bachelor’s degree in a STEM field, a data science bootcamp or master’s program is a great way to take the next step toward being a data scientist. AIFWD has some great ideas to get you started.
Finally, if you’re interested in learning more about how professionals are utilizing machine learning skills to drive our world forward, check out our article on the future of machine learning.