Technology transforms the world in so many ways. Ford’s introduction of the assembly line was essential to the vehicle manufacturing process. The introduction of the internet changed how we communicate, do business, and interact with the world. And in machine learning, we have an emerging technology that transforms how we use computers to complete complex tasks.

Think of machine learning models as “brains” that machines use to actively learn. No longer constrained by rules laid out in their programming, machines have the ability to develop an understanding of new concepts and deliver analysis in ways they never could before. And as a prospective machine learning student, you can become the person who creates the “brains” that modern machines use now and in the future.

But you need a good starting point before you can do any of that. This article covers three of the best machine learning tutorials for beginners who want to get their feet wet while building foundational knowledge that serves them in more specialized courses.

Factors to Consider When Choosing a Machine Learning Tutorial

A machine learning beginner can’t expect to jump straight into a course that delves into neural networking and deep learning and have any idea what they’re doing. They need to learn to crawl before they can walk, making the following factors crucial to consider when choosing a machine learning tutorial for beginners.

  • Content quality. You wouldn’t use cheap plastic parts to build an airplane, just like you can’t rely on poor-quality course content to get you started with machine learning. Always look for reviews of a tutorial before engaging, in addition to checking the credentials of the provider to ensure they deliver relevant content that aligns with your career goals.
  • Instructor expertise. Sticking with our airplane analogy, imagine being taught how to pilot a plane by somebody who’s never actually flown. It simply wouldn’t work. The same goes for a machine learning tutorial, as you need to see evidence that your instructor does more than parrot information that you can find elsewhere. Look for real-world experience and accreditation from recognized authorities.
  • Course structure and pacing. As nice as it would be to have an infinite amount of free time to dedicate to learning, that isn’t a reality for anybody. You have work, life, family, and possibly other study commitments to keep on top of, and your machine learning tutorial has to fit around all of it.
  • Practical and real-world examples. Theoretical knowledge can only take you so far. You need to know how to apply what you’ve learned, which is why a good tutorial should have practical elements that test your knowledge. Think of it like driving a car. You can read pages upon pages of material on how to drive properly but you won’t be able to get on the road until you’ve spent time learning behind the wheel.
  • Community support. Machine learning is a complex subject and it’s natural to feel a little lost with the materials in many tutorials. A strong community gives you a resource base to lean into, in addition to exposing you to peers (and experienced tech-heads) who can help you along or point you in the right career direction.

Top Three Machine Learning Tutorials for Beginners

Now you know what to look for in a machine learning tutorial for beginners, you’re ready to start searching for a course. But if you want to take a shortcut and jump straight into learning, these three courses are superb starting points.

Tutorial 1 – Intro to Machine Learning (Kaggle)

Offered at no cost, Intro to Machine Learning is a three-hour self-paced course that allows you to learn as and when you feel like learning. All of which is helped by Kaggle’s clever save system. You can use it to save your progress and jump back into your learning whenever you’re ready. The course has seven lessons, the first of which offers an introduction to machine learning as a concept. Whereas the other six dig into more complex topics and come with an exercise for you to complete.

Those little exercises are the tutorial’s biggest plus point. They force you to apply what you’ve learned before you can move on to the next lesson. The course also has a dedicated community (led by tutorial creator Dan Becker) that can help you if you get stuck. You even get a certificate for completing the tutorial, though this certificate isn’t as prestigious as one that comes from an organization like Google or IBM.

On the downside, the course isn’t a complete beginner’s course. You’ll need a solid understanding of Python before you get started. Those new to coding should look for Python courses first or they’ll feel lost when the tutorial starts throwing out terminology and programming libraries that they need to use.

Ideal for students with experience in Python who want to apply the programming language to machine learning models.

Tutorial 2 – What Is Machine Learning? (Udemy)

You can’t build a house without any bricks and you can’t build a machine learning model before you understand the different types of learning that underpin that model. Those different types of learning are what the What is Machine Learning tutorial covers. You’ll get to grips with supervised, unsupervised, and reinforcement learning, which are the three core learning types a machine can use to feed its “brain.”

The course introduces you to real-world problems and helps you to see which type of machine learning is best suited to solving those problems. It’s delivered via online videos, totaling just under two hours of teaching, and includes demonstrations in Python to show you how each type of learning is applied to real-world models. All the resources used for the tutorial are available on a GitHub page (which also gives you access to a strong online community) and the tutorial is delivered by an instructor with over 27 years of experience in the field.

It’s not the perfect course, by any means, as it focuses primarily on learning types without digging much deeper. Those looking for a more in-depth understanding of the algorithms used in machine learning won’t find it here, though they will build foundational knowledge that helps them to better understand those algorithms once they encounter them. As an Udemy course, it’s free to take but requires a subscription to the service if you want a certificate and the ability to communicate directly with the course provider.

Ideal for students who want to learn about the different types of machine learning and how to use Python to apply them.

Tutorial 3 – Machine Learning Tutorial (Geeksforgeeks)

As the most in-depth machine learning tutorial for beginners, the Geeksforgeeks offering covers almost all of the theory you could ever hope to learn. It runs the gamut from a basic introduction to machine learning through to advanced concepts, such as natural language processing and neural networks. And it’s all presented via a single web page that acts like a hub that links you to many other pages, allowing you to tailor your learning experience based on what aligns best with your goals.

The sheer volume of content on offer is the tutorial’s biggest advantage, with dedicated learners able to take themselves from complete machine learning newbies to accomplished experts if they complete everything. There’s also a handy discussion board that puts you in touch with others taking the course. Plus, the “Practice” section of the tutorial includes real-world problems, including a “Problem of the Day” that you can use to test different skills.

However, some students may find the way the material is presented to be a little disorganized and it’s easy to lose track of where you are among the sea of materials. The lack of testing (barring the two or three projects in the “Practice” section) may also rankle with those who want to be able to track their progress easily.

Ideal for self-paced learners who want to be able to pick and choose what they learn and when they learn it.

Additional Resources for Learning Machine Learning

Beyond tutorials, there are tons of additional resources you can use to supplement your learning. These resources are essential for continuing your education because machine learning is an evolving concept that changes constantly.

  • Books. Machine learning books are great for digging deeper into the theory you learn via a tutorial, though they come with the downside of offering no practical examples or ways to interact with authors.
  • YouTube channels. YouTube videos are ideal for visual learners and they tend to offer a free way to build on what you learn in a tutorial. Examples of great channels to check out include Sentdex and DeepLearningAI, with both channels covering emerging trends in the field alongside lectures and tutorials.
  • Blogs and websites. Blogs come with the advantage of the communities that sprout up around them, which you can rely on to build connections and further your knowledge. Of course, there’s the information shared in the blogs, too, though you must check the writer’s credentials before digging too deep into their content.

Master a Machine Learning Tutorial for Beginners Before Moving On

A machine learning tutorial for beginners can give you a solid base in the fundamentals of an extremely complex subject. With that base established, you can build up by taking other courses and tutorials that focus on more specialized aspects of machine learning. Without the base, you’ll find the learning experience much harder. Think of it like building a house – you can’t lay any bricks until you have a foundation in place.

The three tutorials highlighted here give you the base you need (and more besides), but it’s continued study that’s the key to success for machine learning students. Once you’ve completed a tutorial, look for books, blogs, YouTube channels, and other courses that help you keep your knowledge up-to-date and relevant in an ever-evolving subject.

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Metro: Is the AI bubble about to burst after Bank of England warns of dot-com crash repeat?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Oct 15, 2025 5 min read

Source:

  • Metro, published on October 09th, 2025

The Bank of England is ringing the bell over an ‘AI bubble’ that could burst at any moment – or maybe not, some experts told Metro.

By Josh Milton

After ChatGPT came on the scene in 2022, the tech industry quickly began comparing the arrival of AI to the dawn of the internet in the 1990s.

Back then, dot-com whizzes were minting easy millions only for the bubble to burst in 2000 when interest rates were hiked. Investors sold off their holdings, companies went bust and people lost their jobs.

Now central bank officials are worried that the AI industry may see a similar boom and bust.

record of the Financial Policy Committee’s October 2 meeting shows officials saying financial market evaluations of AI ‘appear stretched’.

‘This, when combined with increasing concentration within market indices, leaves equity markets particularly exposed should expectations around the impact of AI become less optimistic,’ they added.

AI-focused stocks are mainly in US markets but as so many investors across the world have bought into it, a fallout would be felt globally.

ChatGPT creator OpenAI, chip-maker Nvidia and cloud service firm Oracle are among the AI poster companies being priced big this year.

Earnings are ‘comparable to the peak of the dot-com bubble’, committee members said.

Factors like limited resources – think power-hungry data centres, utilities and software that companies are spending billions on – and the unpredictability of the world’s politics could lead to a drop in stock prices, called a ‘correction’.

In other words, the committee said, investors may be ignoring how risky AI technology is.

Metro spoke with nearly a dozen financial analysts, AI experts and stock researchers about whether AI will suffer a similar fate. There were mixed feelings.

‘Every bubble starts with a story people want to believe,’ says Dat Ngo, of the trading guide, Vetted Prop Firms.

‘In the late 90s, it was the internet. Today, it’s artificial intelligence. The parallels are hard to ignore: skyrocketing stock prices, endless hype and companies investing billions before fully proving their business models.

‘The Bank of England’s warning isn’t alarmist – it’s realistic. When too much capital chases the same dream, expectations outpace results and corrections follow.’

Dr Alessia Paccagnini, an associate Professor from the University College Dublin’s Michael Smurfit Graduate Business School, says that companies are spending £300billion annually on AI infrastructure, while shoppers are spending $12billion. That’s a big difference.

Tech firms listed in the US now represent 30% of New York’s stock index, S&P 500 Index, the highest proportion in 50 years.

‘As a worst-case scenario, if the bubble does burst, the immediate consequences would be severe – a sharp market correction could wipe trillions from stock valuations, hitting retirement accounts and pension funds hard,’ Dr Paccagnini adds.

‘In my opinion, we should be worried, but being prepared could help us avoid the worst outcomes.’

One reason a correction would be so bad is because of how tangled-up the AI world is, says George Sweeney, an investing expert at the personal finance website site Finder.

‘If it fails to meet the lofty expectations, we could see an almighty unravelling of the AI hype that spooks markets, leading to a serious correction,’ he says.

Despite scepticism, AI feels like it’s everywhere these days, from dog bowls and fridges to toothbrushes and bird feeders.

And it might continue that way for a while, even if not as enthusiastically as before, says Professor Filip Bialy, who specialises in computer science and AI ethics at the at Open Institute of Technology.

‘TAI hype – an overly optimistic view of the technological and economic potential of the current paradigm of AI – contributes to the growth of the bubble,’ he says.

‘However, the hype may end not with the burst of the bubble but rather with a more mature understanding of the technology.’

Some stock researchers worry that the AI boom could lose steam when the companies spending billions on the tech see profits dip.

The AI analytic company Qlik found that only one in 10 business say their AI initiatives are seeing sizeable returns.

Qlik’s chief strategy officer, James Fisher, says this doesn’t show that the hype for AI is bursting, ‘but how businesses look at AI is changing’.

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Everything You Need to Know to Join OPIT
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Oct 13, 2025 6 min read

OPIT – Open Institute of Technology offers an innovative and exciting way to learn about technology. It offers a range of bachelor’s and master’s programs, plus a Foundation Year program for those taking the first steps towards higher education. Through its blend of instruction-based and independent learning, it empowers ambitious minds with the skills and knowledge needed to succeed.

This guide covers all you need to know to join OPIT and start your educational journey.

Introducing the Open Institute of Technology

Before we dig into the nitty-gritty of the OPIT application process, here’s a brief introduction to OPIT.

OPIT is a fully accredited Higher Education Institution under the European Qualification Framework (EQF) and the MFHEA Authority. It offers exclusively online education in English to an international community of students. With a winning team of top professors and a specific focus on computer science, it trains the technology leaders of tomorrow.

Some of the unique elements that characterize OPIT’s approach include:

  • No final exams. Instead, students undergo progressive assessments over time
  • A job-oriented, practical focus on the courses
  • 24/7 support, including AI assistance and student communities, so everyone feels supported
  • A strong network of company connections, unlocking doors for graduates

Reasons to Join OPIT

There are many reasons for ambitious students and aspiring tech professionals to study with OPIT.

Firstly, since all the study takes place online, it’s a very flexible and pleasant way to learn. Students don’t feel the usual pressures or suffer the same constraints they would at a physical college or university. They can attend from anywhere, including their own homes, and study at a pace that suits them.

OPIT is also a specialist in the technology field. It only offers courses focused on tech and computer science, with a team of professors and tutors who lead the way in these topics. This ensures that students get high-caliber learning opportunities in this specific sector.

Learning at OPIT is also hands-on and applicable to real-world situations, despite taking place online. Students are not just taught core skills and knowledge, but are also shown how to apply those skills and knowledge in their future careers.

In addition, OPIT strives to make technology education as accessible, inclusive, and affordable as possible. Entry requirements are relatively relaxed, fees are fair, and students from around the world are welcome here.

What You Need to Know About Joining OPIT

Now you know why it’s worth joining OPIT, let’s take a closer look at how to go about it. The following sections will cover how to apply to OPIT, entry requirements, and fees.

The OPIT Application Process

Unsurprisingly for an online-only institution, the application process for OPIT is all online, too. Users can submit the relevant documents and information on their computers from the comfort of their homes.

  1. Visit the official OPIT site and click the “Apply now” button to get started, filling out the relevant forms.
  2. Upload your supporting documents. These can include your CV, as well as certificates to prove your past educational accomplishments and level of English.
  3. Take part in an interview. This should last no more than 30 minutes. It’s a chance for you to talk about your ambitions and background, and to ask questions you might have about OPIT.

That’s it. Once you complete the above steps, you will be admitted to your chosen course and can start enjoying OPIT education once the first term begins. You’ll need to sign your admissions contract and pay the relevant fees, then begin classes.

Entry Requirements for OPIT Courses

OPIT offers a small curated collection of courses, each with its own requirements. You can consult the relevant pages on the official OPIT site to find out the exact details.

For the Foundation Program, for example, you simply need an MQF/EQF Level 3 or equivalent qualification. You also need to demonstrate a minimum B2 level of English comprehension.

For the BSc in Digital Business, applicants should have a higher secondary school leaving certificate, plus B2-level English comprehension. You can also support your application with a credit transfer from previous studies or relevant work experience.

Overall, the requirements are simple, and it’s most important for applicants to be ambitious and eager to build successful careers in the world of technology. Those who are driven and committed will get the best from OPIT’s instruction.

Fees and Flexible Payments at OPIT

As mentioned above, OPIT makes technological education accessible and affordable for all. Its tuition fees cover all relevant teaching materials, and there are no hidden costs or extras. The institute also offers flexible payment options for those with different budgets.

Again, exact fees vary depending on which course you want to take, so it’s important to consult the specific info for each one. You can pay in advance to enjoy 10% off the final cost, or refer a friend to also obtain a discount.

In addition to this, OPIT offers need-based and merit-based scholarships. Successful candidates can obtain discounts of up to 40% on bachelor’s and master’s tuition fees. This can substantially bring the term cost of each program down, making OPIT education even more accessible.

Credit Transfers and Experience

Those who are entering OPIT with pre-existing work experience or relevant academic achievements can benefit from the credit transfer program. This allows you to potentially skip certain modules or even entire semesters if you already have relevant experience in those fields.

OPIT is flexible and fair in terms of recognizing prior learning. So, as long as you can prove your credentials and experience, this could be a beneficial option for you. The easiest way to find out more and get started is to email the OPIT team directly.

Join OPIT Today

Overall, the process to join OPIT is designed to be as easy and stress-free as possible. Everything from the initial application forms to the interview and admission process is straightforward. Requirements and fees are flexible, so people in different situations and from different backgrounds can get the education they want. Reach out to OPIT today to take your first steps to tech success.

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