

Artificial intelligence (AI) is a modern-day monolith that is likely going to be as important to the world as the introduction of the internet. We already see it creeping into every aspect of industry, from the basic chatbots you find on many websites to the self-driving cars under production at companies like Tesla.
As an industry, AI looks set to zoom past its current global valuation of $100 billion, becoming worth a staggering $2 trillion by 2030. To ensure you enjoy a prosperous career in an increasingly computer-powered world, you need to learn about AI. That’s where each artificial intelligence tutorial in this list can help you.
Top AI Tutorials for Beginners
If you know nothing about AI beyond the name, these are the three tutorials to get you started with the subject.
Tutorial 1 – Artificial Intelligence Tutorial for Beginners: Learn the Basics of AI (Guru99)
You need to get to the grips with AI theory before you can start with more practical work. Guru99’s tutorial helps you there, with a set of 11 lessons that take you from the most basic of concepts (what is AI?) to digging into the various types of machine learning. It’s like a crib notes version of an AI book, as it takes you on a speedy flight through AI fundamentals before capping its offer with a look at some practical applications.
Key Topics
- The basic theory of AI and machine learning
- Different types of machine learning algorithms
- An introduction to neural networking
Why Take This Artificial Intelligence Tutorial?
The tutorial is completely free, with every lesson being accessible via the Guru99 website with the click of a mouse. It’s also a great choice for complete AI newbies. You’ll cover the basics first, getting a grounding in AI in the process, before moving on to more complicated aspects of machine learning.
Tutorial 2 – Artificial Intelligence Tutorial for Beginners (Simplilearn)
This 14-lesson tutorial may seem intimidating at first. However, those 14 lessons only take an hour to complete, and the course has no prerequisites. This combination of brevity and a lack of tutorial requirements make it ideal for beginners who want to get to grips with the theory of AI. It’ll also help you develop some programming skills useful in more advanced courses.
Key Topics
- Basic programming skills you can use to develop AI models
- An introduction to Big Data and Spark
- Basic AI concepts, including machine learning, linear algebra, and algorithms
Why Take This Artificial Intelligence Tutorial?
Many of the tutorials you come across online will ask you to have a basic understanding of probability theory and linear algebra. This course equips you with those skills, in addition to giving you a solid grounding in many of the AI concepts (and machine learning models) you’ll encounter when you reach the intermediate level. Think of it as a crash course in the basics of AI.
Top AI Tutorials for Intermediate Learners
If you have a grasp of the basics, meaning you can separate your supervised learning algorithms from your unsupervised ones, you’re ready for these intermediate-level tutorials.
Tutorial 1 – Intro to Artificial Intelligence (Udacity)
Don’t let the use of the word “intro” in this tutorial’s name fool you because this is more than a mere explanation of AI concepts. As a four-month course, it requires you to have a good understanding of concepts like linear algebra and probability theory. Assuming you have that understanding, you’ll embark on a four-month self-paced learning journey (that’s completely free) that delves deep into the applications of AI.
Key Topics
- The theoretical and practical applications of natural language processing
- How AI has uses in every aspect of modern life, from advanced research to gaming
- The fundamentals of AI that underpin the practical applications you learn about
Why Take This Artificial Intelligence Tutorial?
The price tag is right, as this is one of the few Udacity courses you can take without spending any money. It’s also created by two of the best minds in AI – Peter Norvig and Sebastian Thrun – who deliver a nice mix of content, including instructor-led videos, quizzes, and experiential learning. Granted, there’s a large time commitment. But that commitment pays off as the course delivers a solid understanding of AI’s fundamentals and practical applications.
Tutorial 2 – Natural Language Processing Specialization (Coursera)
Anybody who’s used ChatGPT or “spoken” to a chatbot knows that a lot of companies are interested in what AI can do to deliver written content. That’s where Natural Language Processing (NLP) comes in, and this course is ideal for understanding the techniques that allow you to build chatbots and similar technologies.
Key Topics
- How to use logistic regression (and other techniques) to conduct sentiment analysis
- Build autocomplete and autocorrect models
- Discover how to develop AI algorithms that both detect and use human language
Why Take This Artificial Intelligence Tutorial?
Specialization is the key as you get deeper into the AI field. With this course, you focus your learning on language models and NLP, allowing you to dig deeper into an in-demand field that offers plenty of career opportunities. It’s somewhat intensive, requiring four months of study at about 10 hours per week to complete. But you get a shareable certificate at the end and develop a foundation in NLP that can apply in many business areas.
Top AI Tutorials for Advanced Learners
By the time you reach the advanced stage, you’re ready for your AI tutorials to teach you how to build and operate your own AI.
Tutorial 1 – Artificial Intelligence A-Z 2023: Build an AI With ChatGPT4 (Udemy)
With backing from a successful Kickstarter campaign, the Artificial Intelligence A-Z tutorial covers some of the fundamentals but focuses mostly on practical applications. You’ll create several types of AI, including a snazzy virtual self-driving car and an AI designed to beat simple games, helping you get to grips with how to put the theory you’ve learned into practice. The tutorial comes with 17 videos, a trio of downloadable resources, and 20 articles. All of which you can access whenever you need them.
Key Topics
- How to build practical AIs that actually do things
- The fundamentals of complex topics, such as Q-Learning
- How Asynchronous Advantage Actor Critic (AC3) applies to modern AI
Why Take This Artificial Intelligence Tutorial?
The two main reasons to take this tutorial are that it gives you hands-on experience with some exciting AI concepts, and you get a certificate you can put on your CV when you’ve finished. It’s well-structured and popular, with almost 204,000 students having already taken it from all over the world. And at just £59.99 (approx. €69), you get a lot of bang for your buck with videos, articles, and downloadable resources.
Tutorial 2 – A* Pathfinding Tutorial – Unity (YouTube)
Many prospective game developers will get their start with Unity, which is a free development tool that you can use to create surprisingly complex games. This YouTube tutorial series includes 10 videos, which walk you through how to use the A* algorithm to program AIs to determine the paths characters follow in a video game. It requires some programming knowledge, specifically C#, but it’s ideal for those who want to use their AI skills to transition into the world of gaming.
Key Topics
- Using the A* algorithm to create paths for AI-driven characters in video games
- Movement smoothing and terrain-related penalties
- Using multi-threading to improve pathfinding performance
Why Take This Artificial Intelligence Tutorial?
The price is certainly right for this tutorial, as the course creator (Sebastian Lague) makes all of his videos free to view on YouTube. But the biggest benefit of this tutorial is that it introduces complicated concepts that game developers use to determine character movement. If you’re interested in what makes video game characters “work” in terms of their actions in a game, this tutorial shows you the algorithm that underpins it all.
Additional AI Resources
The six tutorials in this list run the gamut from introducing you to the basics of AI to demonstrating specialized applications of the technology. Building on that knowledge requires you to go further, with the following books, podcasts, and websites all being great resources.
Great AI-Related Books
- Artificial Intelligence: A Modern Approach (Peter Norvig and Stuart Russell)
- Python: Advanced Guide to Artificial Intelligence (Giuseppe Bonaccorso)
- Neural Networks and Deep Learning (Charu C Aggarwal)
Great AI-Related Podcasts
- The AI Podcast (Noah Kravitz)
- Artificial Intelligence: AI Podcast (Lex Fridman)
- Eye on AI (Craig Smith)
Great AI-Related Websites and Blogs
- MIT News
- Analytics Vidhya
- KDnuggets
Understand Complex Concepts With an Artificial Intelligence Tutorial
AI is one of the world’s fastest-growing industries, with the previously-mentioned $2 trillion 2030 valuation representing a 20-fold growth from today. The point? Getting in close to the ground floor now by developing your understanding of AI concepts will set you up for a future in which many of the best jobs are in the AI field.
Each artificial intelligence tutorial in this list offers something different to students, from beginners who want to get to grips with AI to those who have a decent understanding and are ready to specialize. Regardless of the course you choose, the most important thing is that you keep learning. AI won’t stay static. It’s like a runaway locomotive that’s going to keep plowing forward, with nothing to stop it, to its next evolution. Use these tutorials to learn both basic and advanced concepts, then build on that learning with continued education.
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During the Open Institute of Technology’s (OPIT’s) 2025 Graduation Day, we conducted interviews with many recent graduates to understand why they chose OPIT, how they felt about the course, and what advice they might give to others considering studying at OPIT.
Karina is an experienced FinTech professional who is an experienced integration manager, ERP specialist, and business analyst. She was interested in learning AI applications to expand her career possibilities, and she chose OPIT’s MSc in Applied Data Science & AI.
In the interview, Karina discussed why she chose OPIT over other courses of study, the main challenges she faced when completing the course while working full-time, and the kind of support she received from OPIT and other students.
Why Study at OPIT?
Karina explained that she was interested in enhancing her AI skills to take advantage of a major emerging technology in the FinTech field. She said that she was looking for a course that was affordable and that she could manage alongside her current demanding job. Karina noted that she did not have the luxury to take time off to become a full-time student.
She was principally looking at courses in the United States and the United Kingdom. She found that comprehensive courses were expensive, costing upwards of $50,000, and did not always offer flexible study options. Meanwhile, flexible courses that she could complete while working offered excellent individual modules, but didn’t always add up to a coherent whole. This was something that set OPIT apart.
Karina admits that she was initially skeptical when she encountered OPIT because, at the time, it was still very new. OPIT only started offering courses in September 2023, so 2025 was the first cohort of graduates.
Nevertheless, Karina was interested in OPIT’s affordable study options and the flexibility of fully remote learning and part-time options. She said that when she looked into the course, she realized that it aligned very closely with what she was looking for.
In particular, Karina noted that she was always wary of further study because of the level of mathematics required in most computer science courses. She appreciated that OPIT’s course focused on understanding the underlying core principles and the potential applications, rather than the fine programming and mathematical details. This made the course more applicable to her professional life.
OPIT’s MSc in Applied Data Science & AI
The course Karina took was OPIT’s MSc in Applied Data Science & AI. It is a three- to four-term course (13 weeks), which can take between one and two years to complete, depending on the pace you choose and whether you choose the 90 or 120 ECTS option. As well as part-time, there are also regular and fast-track options.
The course is fully online and completed in English, with an accessible tuition fee of €2,250 per term, which is €6,750 for the 90 ECTS course and €9,000 for the 120 ECTS course. Payment plans are available as are scholarships, and discounts are available if you pay the full amount upfront.
It matches foundational tech modules with business application modules to build a strong foundation. It then ends with a term-long research project culminating in a thesis. Internships with industry partners are encouraged and facilitated by OPIT, or professionals can work on projects within their own companies.
Entry requirements include a bachelor’s degree or equivalency in any field, including non-tech fields, and English proficiency to a B2 level.
Faculty members include Pierluigi Casale, a former Data Science and AI Innovation Officer for the European Parliament and Principal Data Scientist at TomTom; Paco Awissi, former VP at PSL Group and an instructor at McGill University; and Marzi Bakhshandeh, a Senior Product Manager at ING.
Challenges and Support
Karina shared that her biggest challenge while studying at OPIT was time management and juggling the heavy learning schedule with her hectic job. She admitted that when balancing the two, there were times when her social life suffered, but it was doable. The key to her success was organization, time management, and the support of the rest of the cohort.
According to Karina, the cohort WhatsApp group was often a lifeline that helped keep her focused and optimistic during challenging times. Sharing challenges with others in the same boat and seeing the example of her peers often helped.
The OPIT Cohort
OPIT has a wide and varied cohort with over 300 students studying remotely from 78 countries around the world. Around 80% of OPIT’s students are already working professionals who are currently employed at top companies in a variety of industries. This includes global tech firms such as Accenture, Cisco, and Broadcom, FinTech companies like UBS, PwC, Deloitte, and the First Bank of Nigeria, and innovative startups and enterprises like Dynatrace, Leonardo, and the Pharo Foundation.
Study Methods
This cohort meets in OPIT’s online classrooms, powered by the Canvas Learning Management System (LMS). One of the world’s leading teaching and learning software, it acts as a virtual hub for all of OPIT’s academic activities, including live lectures and discussion boards. OPIT also uses the same portal to conduct continuous assessments and prepare students before final exams.
If you want to collaborate with other students, there is a collaboration tab where you can set up workrooms, and also an official Slack platform. Students tend to use WhatsApp for other informal communications.
If students need additional support, they can book an appointment with the course coordinator through Canvas to get advice on managing their workload and balancing their commitments. Students also get access to experienced career advisor Mike McCulloch, who can provide expert guidance.
A Supportive Environment
These services and resources create a supportive environment for OPIT students, which Karina says helped her throughout her course of study. Karina suggests organization and leaning into help from the community are the best ways to succeed when studying with OPIT.

In April 2025, Professor Francesco Derchi from the Open Institute of Technology (OPIT) and Chair of OPIT’s Digital Business programs entered the online classroom to talk about the current state of the Metaverse and what companies can do to engage with this technological shift. As an expert in digital marketing, he is well-placed to talk about how brands can leverage the Metaverse to further company goals.
Current State of the Metaverse
Francesco started by exploring what the Metaverse is and the rocky history of its development. Although many associate the term Metaverse with Mark Zuckerberg’s 2021 announcement of Meta’s pivot toward a virtual immersive experience co-created by users, the concept actually existed long before. In his 1992 novel Snow Crash, author Neal Stephenson described a very similar concept, with people using avatars to seamlessly step out of the real world and into a highly connected virtual world.
Zuckerberg’s announcement was not even the start of real Metaverse-like experiences. Released in 2003, Second Life is a virtual world in which multiple users come together and engage through avatars. Participation in Second Life peaked at about one million active users in 2007. Similarly, Minecraft, released in 2011, is a virtual world where users can explore and build, and it offers multiplayer options.
What set Zuckerberg’s vision apart from these earlier iterations is that he imagined a much broader virtual world, with almost limitless creation and interaction possibilities. However, this proved much more difficult in practice.
Both Meta and Microsoft started investing significantly in the Metaverse at around the same time, with Microsoft completing its acquisition of Activision Blizzard – a gaming company that creates virtual world games such as World of Warcraft – in 2023 and working with Epic Games to bring Fortnite to their Xbox cloud gaming platform.
But limited adoption of new Metaverse technology saw both Meta and Microsoft announce major layoffs and cutbacks on their Metaverse investments.
Open Garden Metaverse
One of the major issues for the big Metaverse vision is that it requires an open-garden Metaverse. Matthew Ball defined this kind of Metaverse in his 2022 book:
“A massively scaled and interoperable network of real-time rendered 3D virtual worlds that can be experienced synchronously and persistently by an effectively unlimited number of users with an individual sense of presence, and with continuity of data, such as identity, history, entitlements, objects, communication, and payments.”
This vision requires an open Metaverse, a virtual world beyond any single company’s walled garden that allows interaction across platforms. With the current technology and state of the market, this is believed to be at least 10 years away.
With that in mind, Zuckerberg and Meta have pivoted away from expanding their Metaverse towards delivering devices such as AI glasses with augmented reality capabilities and virtual reality headsets.
Nevertheless, the Metaverse is still expanding today, but within walled garden contexts. Francesco pointed to Pokémon Go and Roblox as examples of Metaverse-esque words with enormous engagement and popularity.
Brands Engaging with the Metaverse: Nike Case Study
What does that mean for brands? Should they ignore the Metaverse until it becomes a more realistic proposition, or should they be establishing their Meta presence now?
Francesco used Nike’s successful approach to Meta engagement to show how brands can leverage the Metaverse today.
He pointed out that this was a strategic move from Nike to protect their brand. As a cultural phenomenon, people will naturally bring their affinity with Nike into the virtual space with them. If Nike doesn’t constantly monitor that presence, they can lose control of it. Rather than see this as a threat, Nike identified it as an opportunity. As people engage more online, their virtual appearance can become even more important than their physical appearance. Therefore, there is a space for Nike to occupy in this virtual world as a cultural icon.
Nike chose an ad hoc approach, going to users where they are and providing experiences within popular existing platforms.
As more than 1.5 million people play Fortnite every day, Nike started there, first selling a variety of virtual shoes that users can buy to kit out their avatars.
Roblox similarly has around 380 million monthly active users, so Nike entered the space and created NIKELAND, a purpose-built virtual area that offers a unique brand experience in the virtual world. For example, during NBA All-Star Week, LeBron James visited NIKELAND, where he coached and engaged with players. During the FIFA World Cup, NIKELAND let users claim two free soccer jerseys to show support for their favorite teams. According to statistics published at the end of 2023, in less than two years, NIKELAND had more than 34.9 million visitors, with over 13.4 billion hours of engagement and $185 million in NFT (non-fungible tokens or unique digital assets) sales.
Final Thoughts
Francesco concluded by discussing that while Nike has been successful in the Metaverse, this is not necessarily a success that will be simple for smaller brands to replicate. Nike was successful in the virtual world because they are a cultural phenomenon, and the Metaverse is a combination of technology and culture.
Therefore, brands today must decide how to engage with the current state of the Metaverse and prepare for its potential future expansion. Because existing Metaverses are walled gardens, brands also need to decide which Metaverses warrant investment or whether it is worth creating their own dedicated platforms. This all comes down to an appetite for risk.
Facing these types of challenges comes down to understanding the business potential of new technologies and making decisions based on risk and opportunity. OPIT’s BSc in Digital Business and MSc in Digital Business and Innovation help develop these skills, with Francesco also serving as program chair.
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