Artificial intelligence is no longer science fiction — it is the engine driving medicine, finance, language, and creativity. This guide shows you exactly how to learn to program AI, where in the world to study it, and who can help you get there.
Why learn to program AI now?
We are experiencing a major technological evolution. Artificial Intelligence (AI) software has now taken on many job responsibilities previously associated with programming: writing programs, diagnosing illnesses, making music, and driving cars. The engineers and scientists developing these technologies are some of the most highly sought after workers in the world — the rate at which new graduates from universities are being produced has not kept pace with this increasing demand.
Learning how to program AI systems is not simply about making a living; it is about being part of creating what comes next in our society. No matter what your ultimate goal may be, building AI systems will take the same fundamental skill sets and these skill sets are more accessible than you may think. Whether your goal involves working at the cutting edge of research, starting your own business or using intelligent technology in a field you love (medicine, the arts, climate science, education) these are the foundations required to build AI systems.
“The best time to learn AI was five years ago. The second best time is right now.”
What does AI programming actually involve?
The fundamental concept behind AI programming is that AI software learns from data instead of using rule-based programming to generate output. Traditional programming tells the computer what to do and how to do it. On the other hand, an AI system is able to detect patterns in the data it is fed, predict outcomes based on those patterns, and make improvements by learning from its mistakes. As a result of this unique view of computation, there are many subdisciplines within the field of AI.
The field of AI encompasses many areas such as Machine Learning, Neural Networks (Deep Learning), Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning. Each of these areas has specific techniques, types of software, and unresolved problems that are still being studied. Additionally, each area is currently hiring rapidly.
Your step-by-step learning path
There is no single route into AI, but the following sequence works reliably — whether you are a complete beginner or an experienced programmer pivoting into the field.
- Achieve fluency in Python programming. This language serves as a cross-platform means of communication between the AI research community and companies making use of AI. You’ll begin by learning syntax, data structures, functions, and how to use Object-Oriented Programming (OOP).
- Develop a strong mathematical background. This can include understanding linear algebra (vectors, matrices, transformations), calculus (derivatives, gradients), probability, and statistics. You don’t require a PhD; instead, you should feel confident using those concepts.
- Study the fundamentals of Machine Learning, which includes supervised learning (using regression or classification to develop algorithms), unsupervised learning (using clustering to segment groups of objects), and model evaluation (using metrics such as precision/recall or accuracy). The essential libraries needed for ML include scikit-learn, NumPy, Pandas, and Matplotlib.
- Get into Deep Learning! You will want to know how a Neural Network learns (using backpropagation), as well as study different types of Deep Learning networks for various intended purposes: Convolutional Neural Networks (CNNs) for Vision related AI; Recurrent Neural Networks (RNNs) for sequence-related AI; and Transformers (aka Attention Networks) for Language-related AI. The two primary frameworks for building AI with Deep Learning include Pytorch and Tensorflow.
- Continue to build with practice to solidify the theoretical knowledge you’ve acquired. Participate in competitions on Kaggle, contribute code to open source projects, replicate various published Machine Learning papers, and create a portfolio of projects on Github.
- Select an area of specialisation within AI or Machine Learning. Areas of specialisation include NLP/large language models, Computer Vision, Robotics, Generative AI, AI Safety, or Healthcare AI; think about an area of interest to you that has strong growth potential and where you can go deep into a subject. You are generally going to find many generalists; however, very few specialists, therefore being a specialist is an advantage.
Courses to consider taking that are free to audit include: Andrew Ng’s Machine Learning Specialisation on Coursera; fast.ai’s Practical Deep Learning; MIT OpenCourseWare courses: 18.06 Linear Algebra and 6.S191 Deep Learning, Stanford University’s CS229 Machine Learning, and Andrej Karpathy’s Neural Networks: Zero to Hero video series on YouTube.
Where to study AI around the world
While self-directed learning is a great method to learn, attaining a degree from a top-tier academic institution can yield additional opportunities beyond those offered through online courses alone. A bachelor’s or master’s degree from a prestigious academic institution provides access to world-class research laboratories, high-profile faculty, cooperative peer communities, and internship placements with some of the best companies in the world. Having a degree from an accredited university also demonstrates extensive knowledge of the field to prospective employers.
The growth of artificial intelligence as an area of active research and education has spread worldwide. Although the USA continues to produce the most research output, leading programmes, in this field, have developed all around the world in Europe, Asia, Canada, and Australia. Listed below are some of the institutions that have been consistently rated among the top institutions for AI/computer science research globally:
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MIT & Stanford
United States — global research leaders, home to seminal AI breakthroughs
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Oxford & Imperial College London
United Kingdom — strong in AI ethics, NLP, and applied machine learning
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University of Toronto & Mila (Montreal)
Canada — birthplace of deep learning; home of Hinton, Bengio, LeCun
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TU Munich & ETH Zürich
Germany / Switzerland — Europe’s top technical universities for AI
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NUS & NTU
Singapore — Asia’s gateway; world-class research and industry links
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University of Melbourne & ANU
Australia — growing AI research profile; excellent quality of life
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École Polytechnique & INRIA
France — strong mathematical tradition; EU AI research hub
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Tsinghua & Peking University
China — massive investment in AI; world-leading research output
Programmes range from three- and four-year Bachelor’s degrees in Computer Science or Data Science, to one- and two-year Master’s degrees, to fully-funded PhD research positions. Many institutions now offer hybrid and online tracks, making world-class education accessible regardless of where you live today — though nothing quite replaces the experience of being physically present in a thriving research environment.
Smapse Education — The World’s Leading Admissions Consultancy
Ambition is something all of us have, regardless of where we grow up. However, when it comes to getting accepted into a university in a foreign country, nothing could be more complicated than the many different processes that exist in each country for applying, getting a visa, providing proof of language proficiency, when scholarships are due, and the expectation of the culture, etc. If you do anything wrong during this process, you may lose an entire year by not being able to start your education at a foreign university.
As a result, Smapse Education has built up its reputation over the years as “the” company to support students with guidance throughout the process of being accepted into international schools and universities. From the first time you speak with the Smapse team until the time you arrive at your chosen school, experienced education consultants from Smapse will be by your side helping you with every step of your admission process. Those steps include identifying the right institution for you, writing an effective application, navigating through any bureaucratic red tape, obtaining funding for your education, and assisting you with settling down correctly when you arrive in your new home.
Smapse has assisted thousands of students to gain admission into some of the most prestigious AI (artificial intelligence), computer science, and technology programs at universities and colleges throughout the United States, the United Kingdom, Canada, Germany, Singapore, Australia, and more. In addition to having an expansive understanding of international admissions requirements, Smapse’s education consultants will provide guidance based on the factors you should consider about the programs you are applying to at each university — thus giving you a solid competitive advantage.
- University selection specific to your needs
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Where does an AI career take you?
AI practitioners are actually employed in a wide range of roles in AI; due to the diversity of AI jobs, they aren’t limited to one job title. Practitioners can be part of an AI ecosystem of different specialists that offer varied intellectual incentives and pay structures.
- Types of AI practitioner roles include:
- Machine Learning Engineer
- Research Scientist
- NLP Engineer
- Computer Vision Engineer
- Data Scientist
- AI Product Manager
- Robotics Engineer
- AI Safety Researcher
The demand for talent in AI is outpacing supply globally, and this gap is widening. According to a recent report, after only a few months of working as an AI practitioner, you will most likely earn a six-figure salary with leading tech companies. Although you would earn an excellent salary even as an entry-level employee, top researchers can earn salaries comparable to those of investment bankers and attorneys. More importantly, you will be involved in intellectually stimulating work; you will be solving challenging problems that no one has solved before.
You can get started by taking an online course this evening or by enrolling in a degree program at your local university (or a Master’s program at a top tier university such as MIT, Carnegie Mellon, or Oxford) to begin your journey toward becoming an AI practitioner. You need to work hard and build something, and strive for a quality of education that is within your means. Partners like Smapse Education can assist you in accomplishing this goal.
