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    The Best Artificial Intelligence and Machine Learning Courses

    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Jul 1, 2023 • 9 min read
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    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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    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

    Source:

    • 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.

    Read the full article below:

    Read the article
    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

    Source:

    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.

    Read the full article below (in Italian):

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