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.

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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

Read the full article below (in Italian):

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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