Growth is inevitable in the AI sector. According to Statista, the already-booming industry looks set to go from a value of $100 billion in 2021 to $2 trillion by 2030, increasing by a multiple of 20 to become one of the world’s biggest industries. Naturally, the need for skilled AI professionals will grow alongside that enormous scaling.

That’s where you come in.

With the right applied AI course, you can develop both the knowledge of the foundational theory that sits behind AI and learn how to apply that theory in a real-world setting. Here are four of the best applied AI courses to get you started.

Factors to Consider When Choosing an Applied AI Course

Every search for a new course starts with figuring out the strengths and weaknesses of each one you consider. These factors help you do that, ensuring you don’t spend your hard-earned money on a course that fails to equip you with skills that make you desirable to employers.

Course Content and Curriculum

AI is such an expansive field that every applied AI course has the potential to cover different topics and subjects. Think about what you want to learn (and your prospective career path), then align your course selection with that intended path.

Course Duration

Applied AI courses can vary tremendously in length, from several years for degree-level courses to a few months for online courses. Ask yourself how long you wish to spend studying. Also, consider the flexibility of the course, such as whether you’ll be able to fit your studies around your existing work and family commitments.

Instructor Expertise

AI is a burgeoning industry, meaning expertise levels vary from course to course. For applied AI courses, in particular, you want professors who combine in-depth knowledge of the theory with real-world experience. What have they done in the industry? If the answer is “nothing,” they may not be able to guide you down the path to an AI-centric career.

Course Fees and Financial Aid

Course fees vary massively depending on the type of course you take. For example, those in the U.K. can easily spend between £15,000 and £25,000 on university-level courses, with Aston University’s tuition fees of £23,200 being somewhat typical. Online and self-learning courses cost considerably less, so you need to figure out how much you’re willing to spend (and if you can get any help with your fees) before moving forward.

Job Placement and Career Support

Though you need one eye pointed toward the present when choosing between applied AI courses, the other needs to be firmly pointed toward the future. What prospects will you have when you complete the course? In other words, does the course provide you with a direct path into the industry, along with support, or are you left to fend for yourself once you have your qualification?

Top Choices for Mastering Artificial Intelligence

Choices abound when you jump online to find applied AI courses. The following selection offers a nice mix, from online certifications offered by industry professionals to a couple of courses from some of the world’s most prestigious universities.

Course 1 – IBM Applied AI Professional Certification

If you’re fresh to the world of AI (though ideally not new to computer science), IBM’s industry-specific applied AI courses offer both foundational knowledge and a respected qualification. They’re flexible, too, with this course lasting for six months but only requiring three hours of work per work. Those in full-time work (or education) can fit the course around their lifestyle, while those who have time to burn can complete the entire course much quicker, earning degree credits along the way.

Key Features and Benefits

  • Certification from one of the most respected companies in the AI space
  • Direct exposure to use cases in the deep learning, machine learning, and neural network spheres
  • Learn how to build AI-powered solutions (like chatbots) using Python and IBM’s Watson AI
  • Over three-quarters (77%) of students report career improvement

Pricing and Enrollment

IBM’s course is available via Coursera and offers a seven-day trial you can use to get to grips with its structure and examine its modules. It’s fully online, which improves flexibility at the cost of not having direct access to a professor, and you’ll receive an IBM badge upon completion. You’ll pay a monthly fee of $35 (approx. €31) and can enroll at almost any time.

Course 2 – Computer Science for Artificial Intelligence (Harvard University)

Harvard University may be seen as the gold standard in the United States, but what many don’t know is that it offers a comprehensive suite of online courses that almost anybody can take. Its Computer Science for Artificial Intelligence course is a perfect example. Comprising of two courses – an introduction to computer science followed by an introduction to applying computer science principles to AI using Python – it lasts for five months. You get access to professors and can learn at your own pace, with the course recommending between seven and 22 hours of study per week.

Key Features and Benefits

  • Two modules give you a crash course in applied AI and the computer science theory that underpins it
  • Director access to Harvard professors Doug Lloyd, Brian Yu, and David J. Malan
  • Complete flexibility in how and when you learn
  • Get to grips with Python and build experience with machine learning libraries

Payment and Enrollment

As an online course, Computer Science for Artificial Intelligence is available for enrollment whenever you’re ready, with the five months starting once you’re enrolled. It costs £277 (approx. €312) and you’ll need to create an account with the EDX website (which hosts the course) to get started.

Course 3 – Artificial Intelligence Graduate Certificate (Stanford University)

Ranked as the third-best university in the United States for general computer science and AI teaching, Stanford University has opened up some of its best courses to online learners. Entirely online (and instructor-led for those who want more guidance) this is one of those applied AI courses that is equivalent to a full graduate degree. You’ll complete at least one required course – with a choice between machine learning and the principles of AI – and select up to three electives. It’s the electives that make this course stand out, as there are 18 to choose from, with the right combination giving you a chance to specialize for specific career paths.

Key Benefits and Features

  • Direct tuition from prominent Stanford faculty members, including Andrew Ng and Chelsea Finn
  • Some level of autonomy in how you study thanks to the online-centric nature of the course
  • Specialize in specific areas of AI thanks to a wide range of electives
  • You get a degree from one of the world’s foremost colleges in the AI field

Payment and Enrollment

Let’s get the bad news out of the way immediately – this isn’t a cheap course. As a full-on graduate degree, it costs between $18,200 and $22,400 to take (approx. €16,235 and €19,980), though financial aid may be available for some students. You can’t just hop onto the course, either, as a college-level understanding of calculus, linear algebra, Probability Theory, and several programming languages is required. Stanford itself calls this one of its most difficult courses and recommends that you take several foundation courses (ideally at degree level) before enrolling.

Course 4 – Master in Applied Data Science & AI (OPIT)

As a full postgraduate course that takes between 12 and 18 months to complete, OPIT’s Master in Applied Data Science & AI is an interesting case for one simple reason – there are no computer science prerequisites. The course is open to everybody and it teaches both advanced applied AI concepts and the foundational knowledge needed to understand them. You’ll complete a pair of terms containing courses, with your final term dedicated to a project or thesis that puts what you’ve learned into practice.

Key Benefits and Features

  • The course is supplied by an institution with accreditation from the European Qualification Framework
  • It’s a fully remote course that gives you control over how and when you learn
  • Discounts and payment plans are available, as well as scholarship and funding options
  • You come out of the course with a recognized postgraduate degree

Payment and Enrollment

Though the course usually costs €6,500, OPIT offers “early bird” discounts that allow you to enroll for €4,950, assuming you sign up early enough. Intakes are semi-regular, with the next one scheduled for October 2023 and international students get 90 credits under the European Credit Transfer and Accumulation System (ECTS) for successful completion.

Tips for Success in an Applied AI Course

As you can see, you have plenty of options for applied AI courses, from professional certifications designed to get you into a career quickly to full postgraduate degrees. Regardless of your choice, these tips will help you get your precious certification:

  • Dedicate time for study – Time well managed is time well spent. Understand that you’ll need to dedicate self-learning time to get to grips with concepts you’re taught during classroom hours.
  • Set clear goals – Going into an applied AI course with no sense of what you’re supposed to get out of that course leaves you directionless upon completion. Make sure you know exactly what you stand to gain before committing time (and money) to a course.
  • Network often – Even online courses give you a chance to get involved in teamwork projects and speak to experienced industry professionals. Take those chances. The more connections you build during your studies, the more opportunities you’ll see coming out of the back end.
  • Seek guidance – As attractive as the prospect of self-guided learning may be, we all need a helping hand from time to time. If a course provides direct access to tutors and professors, use it.
  • Stay up to date – AI is a fast-moving field, with every change and advancement bringing new challenges and opportunities. Stay on top of what’s happening in the industry. You may just find that one course sets you up to be ready for those changes, while another may not.

Build Your Skills With an Applied AI Course

Whether you go down the full postgraduate degree route or you choose a professional qualification, an applied AI course is a route into one of the world’s fastest-growing industries. Simply put, we’re set for an AI explosion. Over the next decade, AI will permeate everything we do, from complex computing to simple office tasks, and you can use the right course to give yourself the skills you need to take advantage of that fact. Explore the options shared in this article, ask yourself what you want to achieve in your career, and make the educational choice that’s right for you.

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CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 3 min read

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  • CCN, published on March 29th, 2025

By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

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Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 6 min read

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By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

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