Every time you’ve chatted with a bot on a website, you’ve seen the basis of artificial intelligence (AI) and machine learning (ML) in action. Your experiences with augmented reality, any prompts you’ve ever delivered to ChatGPT, and a host of other technologies that businesses are already leveraging show us how crucial these two fields are, both today and in the future.

AI and ML are taking over the world. And with the right AI & ML courses, you put yourself in the ideal position to forge a career in an industry that’s set for a continuous annual growth rate of 36.2% between 2023 and 2030.

Factors to Consider When Choosing an AI and ML Course

AI ML courses come in all shapes and sizes, with some delivering the basics you need to build a foundation in the subjects and others moving on from those foundational concepts and into specializations. These five things are your biggest considerations when choosing a course.

1 – Course Content and Curriculum

What does the course teach? That’s not just an important question in terms of figuring out if the course helps you develop the skills you need, but it’s a crucial one for your future career prospects. The curriculum informs every step you take on your learning journey. If the content isn’t up to scratch (or takes you in a different direction than the one you intend to go in) it’s not the course for you.

2 – Course Duration and Flexibility

Combine work and family with your personal life and existing educational commitments and you have the recipe for a quagmire of time-consuming tasks that may not fit with a long-term course. The best AI and ML courses online offer flexibility, allowing you to fit your studies around other commitments and opening the door to self-paced learning.

3 – Your Instructors

Imagine you walk into a classroom and your instructor introduces themself. They tell you they have a couple of qualifications in the fields of AI and ML, but they haven’t worked in either industry and can’t expose you to professionals who have. Is that any use to you in your career? A good instructor combines technical expertise (which they’ll need a lot of) with industry experience they can draw on to lead you down the right career path.

4 – Course Reviews and Ratings

As any online marketer will tell you, user-generated content in the form of reviews, social media posts, and simple ratings tells you a ton about what a product delivers. That’s as true for AI and machine learning courses as it is for anything else. Check out what other people have to say about the course, paying special attention to former students and what’s happened to them in the wake of earning their certification.

5 – Pricing and Affordability

Money is always a challenge when it comes to education. Some universities charge tens of thousands of euros for their courses, which is fine if you can commit time and money to a full-time educational experience. It’s not so fine if you’re working on a budget. Your course’s cost plays a huge role in determining whether you take it. Just remember one thing – people tend to get what they pay for (for better or worse).

Top AI and ML Online Courses

Machine learning and artificial intelligence courses run the gamut from fast, industry-led courses designed to get you into a job to deeper degrees designed to equip you with everything you need to advance in your career. The following four are some of the best AI and ML courses online.

Course 1 – Master in Applied Data Science & AI (OPIT)

Designed for those at the postgraduate level, this Master’s degree requires you to have a background in computer science (or a relevant alternative). It’s a 100% online course that delivers an accredited degree under the European Qualification Framework (EQF), with the course also counting toward the college credits you may need to apply for future courses. Tutors are available for direct learning 24/7 and you learn via both recorded and live content delivered over the web.

Key Features and Benefits

  • Offers tons of exposure to how machine learning and artificial intelligence apply in real-world scenarios
  • You get a Master’s degree from a fully-accredited institution
  • Favors progressive assessments over high-stress exams
  • Control your own learning by arranging the course’s modules around your schedule

Enrollment Details

OPIT’s Master in Applied Data Science & AI comes in two flavors – the regular 18-month variety and a fast-tracked 12-month course. Enrollment is annual, with intake occurring every October, and the price varies depending on when you apply. Early birds get an extensive discount, paying €4,950 compared to the regular price of €6,500. You’ll need a relevant Bachelor’s degree in a subject like computer science to apply.

Course 2 – Machine Learning Introduction for Everyone (IBM via Coursera)

If OPIT’s Master’s degree is for people who are already halfway through the metaphorical marathon of machine learning and AI, IBM’s beginner’s course is for those at the starting line. It’s a seven-hour course that teaches the basics of AI and ML, in addition to helping you get to grips with the development cycle for a machine learning model. As a primer for the concepts, it’s one of the best AI ML online courses available.

Key Features and Benefits

  • Provided by a Fortune 50 company that’s one of the leaders in the AI field
  • Created by a Senior Data Scientist who currently works for IBM
  • You receive a sharable certificate that looks great on your LinkedIn profile
  • No completion of other AI machine learning courses is required to apply

Enrollment Details

“Free” is always a nice price tag to see on anything, and that’s what you get with this course, at least when trialing the course. Enrolment is semi-regular, with batches of students accepted every few months, and you get to reset deadlines based on when you can complete its modules. IBM says the course contains seven hours of content. Your experience may vary depending on how quickly (or otherwise) you adapt to the content.

Course 3 – Post Graduate Program in AI and Machine Learning (Purdue University)

Career Karma ranks this as one of the best AI ML courses online, and it’s hard to argue given that this is a near-year-long course offered with backing from industry professionals at IBM. It’s more bootcamp than formal course, though, so expect to be put through your paces with intensive hackathons and sprints that cover a huge number of AI tools. Combine that with real-world projects (using datasets from companies like Twitter and Uber) and you have a fast-paced and valuable course.

Key Features and Benefits

  • Any extremely modern curriculum that takes in real-world examples from tech industry giants
  • Backed by IBM to further the real-world experience delivered
  • You receive a postgraduate certificate from an established university
  • The online bootcamp experience is great for people who prefer fast-paced and intensive learning

Enrollment Details

Enrollment is set for May of each year, with the course lasting for 11 months thereafter. You’ll need to hit some criteria to apply. The course asks for a minimum of a Bachelor’s degree where you’ve obtained at least 50% on your modules, as well as a couple of years of work experience. That work experience requirement may be an issue for people who haven’t started their careers. Still, it’s a cost-effective program, with the course costing £2,990 (approx. €3,400).

Course 4 – Machine Learning Crash Course (Google AI)

If time is of the essence and you just want a crash course in what machine learning is and how it applies to your business, Google provides the answer with this option. At just 15 hours, it’s a course you can complete over an intensive weekend of study. It’ll introduce you to some real-world case studies, with lectures coming directly from industry heads at Google.

Key Features and Benefits

  • Contains 25 lessons (with 30 exercises) to expand and test your knowledge
  • Get industry insight from Google experts who work in the AI and ML fields
  • You don’t have to pay a euro to take part in this course
  • Includes interactive visualizations of real-world models that are great for tinkerers

Enrollment Details

Google presumes no prior knowledge of machine learning in this course, though it recommends that you’re comfortable with programming in Python and understand complex statistical concepts. Knowledge of the NumPy library is especially helpful. Assuming you build up a knowledge base (Google offers other courses to cover these foundations), you can enroll at any time and get a free course that you can fit around your schedule.

Additional Resources for AI and ML Learning

Great AI ML courses can teach you the fundamentals and offer direct experience, ideally coming from professionals in the industry. But it’s what you do outside of your formal and certified studies that can make the biggest difference to your career prospects. These additional resources both supplement what you learn from the above courses and allow you to continue developing your skills once you have your shiny new certificate:

  • Online forums and communities
  • Podcasts and YouTube channels dedicated to machine learning and AI
  • Books and eBooks
  • Conferences, workshops, and career-centric bootcamps

Use AI & ML Courses Today to Benefit Tomorrow

Consider these facts if you need any more convincing that AI and machine learning courses are right for you. The average machine learning engineer earns between €66,585 and €118,169 per year, with jobs in AI easily climbing into the six-figure range as well. Your career prospects get a boost when you study AI and ML. But remember – a certification alone is not enough.

These are fast-evolving fields, and only those who dedicate themselves to continued learning (and the adaptation that comes with market changes) excel. Start your journey with one of the four courses in this article and then continue down the educational path.

Related posts

Reuters: EFG Watch: DeepSeek poses deep questions about how AI will develop
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Feb 10, 2025 4 min read

Source:

  • Reuters, Published on February 10th, 2025.

By Mike Scott

Summary

  • DeepSeek challenges assumptions about AI market and raises new ESG and investment risks
  • Efficiency gains significant – similar results being achieved with less computing power
  • Disruption fuels doubts over Big Tech’s long-term AI leadership and market valuations
  • China’s lean AI model also casts doubt on costly U.S.-backed Stargate project
  • Analysts see DeepSeek as a counter to U.S. tariffs, intensifying geopolitical tensions

February 10 – The launch by Chinese company DeepSeek, opens new tab of its R1 reasoning model last month caused chaos in U.S. markets. At the same time, it shone a spotlight on a host of new risks and challenged market assumptions about how AI will develop.

The shock has since been overshadowed by President Trump’s tariff wars, opens new tab, but DeepSeek is set to have lasting and significant implications, observers say. It is also a timely reminder of why companies and investors need to consider ESG risks, and other factors such as geopolitics, in their investment strategies.

“The DeepSeek saga is a fascinating inflection point in AI’s trajectory, raising ESG questions that extend beyond energy and market concentration,” Peter Huang, co-founder of Openware AI, said in an emailed response to questions.

DeepSeek put the cat among the pigeons by announcing that it had developed its model for around $6 million, a thousandth of the cost of some other AI models, while also using far fewer chips and much less energy.

Camden Woollven, group head of AI product marketing at IT governance and compliance group GRC International, said in an email that “smaller companies and developers who couldn’t compete before can now get in the game …. It’s like we’re seeing a democratisation of AI development. And the efficiency gains are significant as they’re achieving similar results with much less computing power, which has huge implications for both costs and environmental impact.”

The impact on AI stocks and companies associated with the sector was severe. Chipmaker Nvidia lost almost $600 billion in market capitalisation after the DeepSeek announcement on fears that demand for its chips would be lower, but there was also a 20-30% drop in some energy stocks, said Stephen Deadman, UK associate partner at consultancy Sia.

As Reuters reported, power producers were among the biggest winners in the S&P 500 last year, buoyed by expectations of ballooning demand from data centres to scale artificial intelligence technologies, yet they saw the biggest-ever one-day drops after the DeepSeek announcement.

One reason for the massive sell-off was the timing – no-one was expecting such a breakthrough, nor for it to come from China. But DeepSeek also upended the prevailing narrative of how AI would develop, and who the winners would be.

Tom Vazdar, professor of cybersecurity and AI at Open Institute of Technology (OPIT), pointed out in an email that it called into question the premise behind the Stargate Project,, opens new tab a $500 billion joint venture by OpenAI, SoftBank and Oracle to build AI infrastructure in the U.S., which was announced with great fanfare by Donald Trump just days before DeepSeek’s announcement.

“Stargate has been premised on the notion that breakthroughs in AI require massive compute and expensive, proprietary infrastructure,” Vazdar said in an email.

There are also dangers in markets being dominated by such a small group of tech companies. As Abbie Llewellyn-Waters, Investment manager at Jupiter Asset Management, pointed out in a research note, the “Magnificent Seven” tech stocks had accounted for nearly 60% of the index’s gains over the previous two years. The group of mega-caps comprised more than a third of the S&P 500’s total value in December 2024.

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EFMD Global: What students need to know in 2025
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jan 30, 2025 3 min read

Source:


By Stephanie Mullins

Technological advances, changes around equality and the importance of sustainable initiatives may characterise 2025 for some, but what do people studying in 2025 really need to know?

We spoke to education experts from around the world to find out. From Germany’s Frankfurt School of Finance & Management and Nottingham Business School in the UK to India’s IIM Indore and Italy’s POLIMI Graduate School of Management, here’s what 21 experts actually said…

Sara Ciabattoni, Senior Program Coordinator at OPIT – Open Institute of Technology:

  1. Master Digital Skills: In today’s fast-evolving digital landscape, it’s essential to master a range of digital tools and platforms. Students should focus not only on developing technical expertise but also on leveraging technology to improve their problem-solving capabilities and drive innovation. 
  2. Focus on Lifelong Learning: The future of work is evolving, bringing challenges but even greater opportunities. The World Economic Forum’s Future of Jobs Report predicts that while some roles will be displaced by technology, even more “jobs of tomorrow” will emerge, underscoring the need to focus on growth rather than disruption. As OPIT Rector Francesco Profumo envisions, education should adopt a circular learning model, much like the circular economy, shifting from a one-time, cradle-to-grave approach to a lifelong cycle of continuous learning. This ensures we stay adaptable and ready for the opportunities of a rapidly changing world. 
  3. Develop Soft Skills: While technical expertise is crucial, employers increasingly prioritise communication, leadership, and collaboration. Cultivating these soft skills alongside academic knowledge will equip students to thrive in the complex, interconnected workplaces of the future. 
  4. Practice Critical Thinking: In an era where information is abundant but not always accurate, students must develop strong critical thinking skills. The ability to evaluate sources, question assumptions, and synthesise new ideas will be essential in making informed decisions. 

By prioritising these areas, students can better equip themselves to meet the challenges and seize the opportunities of their academic and professional futures.

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