

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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The world is rapidly changing. New technologies such as artificial intelligence (AI) are transforming our lives and work, redefining the definition of “essential office skills.”
So what essential skills do today’s workers need to thrive in a business world undergoing a major digital transformation? It’s a question that Alan Lerner, director at Toptal and lecturer at the Open Institute of Technology (OPIT), addressed in his recent online masterclass.
In a broad overview of the new office landscape, Lerner shares the essential skills leaders need to manage – including artificial intelligence – to keep abreast of trends.
Here are eight essential capabilities business leaders in the AI era need, according to Lerner, which he also detailed in OPIT’s recent Master’s in Digital Business and Innovation webinar.
An Adapting Professional Environment
Lerner started his discussion by quoting naturalist Charles Darwin.
“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.”
The quote serves to highlight the level of change that we are currently seeing in the professional world, said Lerner.
According to the World Economic Forum’s The Future of Jobs Report 2025, over the next five years 22% of the labor market will be affected by structural change – including job creation and destruction – and much of that change will be enabled by new technologies such as AI and robotics. They expect the displacement of 92 million existing jobs and the creation of 170 million new jobs by 2030.
While there will be significant growth in frontline jobs – such as delivery drivers, construction workers, and care workers – the fastest-growing jobs will be tech-related roles, including big data specialists, FinTech engineers, and AI and machine learning specialists, while the greatest decline will be in clerical and secretarial roles. The report also predicts that most workers can anticipate that 39% of their existing skill set will be transformed or outdated in five years.
Lerner also highlighted key findings in the Accenture Life Trends 2025 Report, which explores behaviors and attitudes related to business, technology, and social shifts. The report noted five key trends:
- Cost of Hesitation – People are becoming more wary of the information they receive online.
- The Parent Trap – Parents and governments are increasingly concerned with helping the younger generation shape a safe relationship with digital technology.
- Impatience Economy – People are looking for quick solutions over traditional methods to achieve their health and financial goals.
- The Dignity of Work – Employees desire to feel inspired, to be entrusted with agency, and to achieve a work-life balance.
- Social Rewilding – People seek to disconnect and focus on satisfying activities and meaningful interactions.
These are consumer and employee demands representing opportunities for change in the modern business landscape.
Key Capabilities for the AI Era
Businesses are using a variety of strategies to adapt, though not always strategically. According to McClean & Company’s HR Trends Report 2025, 42% of respondents said they are currently implementing AI solutions, but only 7% have a documented AI implementation strategy.
This approach reflects the newness of the technology, with many still unsure of the best way to leverage AI, but also feeling the pressure to adopt and adapt, experiment, and fail forward.
So, what skills do leaders need to lead in an environment with both transformation and uncertainty? Lerner highlighted eight essential capabilities, independent of technology.
Capability 1: Manage Complexity
Leaders need to be able to solve problems and make decisions under fast-changing conditions. This requires:
- Being able to look at and understand organizations as complex social-technical systems
- Keeping a continuous eye on change and adopting an “outside-in” vision of their organization
- Moving fast and fixing things faster
- Embracing digital literacy and technological capabilities
Capability 2: Leverage Networks
Leaders need to develop networks systematically to achieve organizational goals because it is no longer possible to work within silos. Leaders should:
- Use networks to gain insights into complex problems
- Create networks to enhance influence
- Treat networks as mutually rewarding relationships
- Develop a robust profile that can be adapted for different networks
Capability 3: Think and Act “Global”
Leaders should benchmark using global best practices but adapt them to local challenges and the needs of their organization. This requires:
- Identifying what great companies are achieving and seeking data to understand underlying patterns
- Developing perspectives to craft global strategies that incorporate regional and local tactics
- Learning how to navigate culturally complex and nuanced business solutions
Capability 4: Inspire Engagement
Leaders must foster a culture that creates meaningful connections between employees and organizational values. This means:
- Understanding individual values and needs
- Shaping projects and assignments to meet different values and needs
- Fostering an inclusive work environment with plenty of psychological safety
- Developing meaningful conversations and both providing and receiving feedback
- Sharing advice and asking for help when needed
Capability 5: Communicate Strategically
Leaders should develop crisp, clear messaging adaptable to various audiences and focus on active listening. Achieving this involves:
- Creating their communication style and finding their unique voice
- Developing storytelling skills
- Utilizing a data-centric and fact-based approach to communication
- Continual practice and asking for feedback
Capability 6: Foster Innovation
Leaders should collaborate with experts to build a reliable innovation process and a creative environment where new ideas thrive. Essential steps include:
- Developing or enhancing structures that best support innovation
- Documenting and refreshing innovation systems, processes, and practices
- Encouraging people to discover new ways of working
- Aiming to think outside the box and develop a growth mindset
- Trying to be as “tech-savvy” as possible
Capability 7: Cultivate Learning Agility
Leaders should always seek out and learn new things and not be afraid to ask questions. This involves:
- Adopting a lifelong learning mindset
- Seeking opportunities to discover new approaches and skills
- Enhancing problem-solving skills
- Reviewing both successful and unsuccessful case studies
Capability 8: Develop Personal Adaptability
Leaders should be focused on being effective when facing uncertainty and adapting to change with vigor. Therefore, leaders should:
- Be flexible about their approach to facing challenging situations
- Build resilience by effectively managing stress, time, and energy
- Recognize when past approaches do not work in current situations
- Learn from and capitalize on mistakes
Curiosity and Adaptability
With the eight key capabilities in mind, Lerner suggests that curiosity and adaptability are the key skills that everyone needs to thrive in the current environment.
He also advocates for lifelong learning and teaches several key courses at OPIT which can lead to a Bachelor’s Degree in Digital Business.

Many people treat cyber threats and digital fraud as a new phenomenon that only appeared with the development of the internet. But fraud – intentional deceit to manipulate a victim – has always existed; it is just the tools that have changed.
In a recent online course for the Open Institute of Technology (OPIT), AI & Cybersecurity Strategist Tom Vazdar, chair of OPIT’s Master’s Degree in Enterprise Cybersecurity, demonstrated the striking parallels between some of the famous fraud cases of the 18th century and modern cyber fraud.
Why does the history of fraud matter?
Primarily because the psychology and fraud tactics have remained consistent over the centuries. While cybersecurity is a tool that can combat modern digital fraud threats, no defense strategy will be successful without addressing the underlying psychology and tactics.
These historical fraud cases Vazdar addresses offer valuable lessons for current and future cybersecurity approaches.
The South Sea Bubble (1720)
The South Sea Bubble was one of the first stock market crashes in history. While it may not have had the same far-reaching consequences as the Black Thursday crash of 1929 or the 2008 crash, it shows how fraud can lead to stock market bubbles and advantages for insider traders.
The South Sea Company was a British company that emerged to monopolize trade with the Spanish colonies in South America. The company promised investors significant returns but provided no evidence of its activities. This saw the stock prices grow from £100 to £1,000 in a matter of months, then crash when the company’s weakness was revealed.
Many people lost a significant amount of money, including Sir Isaac Newton, prompting the statement, “I can calculate the movement of the stars, but not the madness of men.“
Investors often have no way to verify a company’s claim, making stock markets a fertile ground for manipulation and fraud since their inception. When one party has more information than another, it creates the opportunity for fraud. This can be seen today in Ponzi schemes, tech stock bubbles driven by manipulative media coverage, and initial cryptocurrency offerings.
The Diamond Necklace Affair (1784-1785)
The Diamond Necklace Affair is an infamous incident of fraud linked to the French Revolution. An early example of identity theft, it also demonstrates that the harm caused by such a crime can go far beyond financial.
A French aristocrat named Jeanne de la Mont convinced Cardinal Louis-René-Édouard, Prince de Rohan into thinking that he was buying a valuable diamond necklace on behalf of Queen Marie Antoinette. De la Mont forged letters from the queen and even had someone impersonate her for a meeting, all while convincing the cardinal of the need for secrecy. The cardinal overlooked several questionable issues because he believed he would gain political benefit from the transaction.
When the scheme finally exposed, it damaged Marie Antoinette’s reputation, despite her lack of involvement in the deception. The story reinforced the public perception of her as a frivolous aristocrat living off the labor of the people. This contributed to the overall resentment of the aristocracy that erupted in the French Revolution and likely played a role in Marie Antoinette’s death. Had she not been seen as frivolous, she might have been allowed to live after her husband’s death.
Today, impersonation scams work in similar ways. For example, a fraudster might forge communication from a CEO to convince employees to release funds or take some other action. The risk of this is only increasing with improved technology such as deepfakes.
Spanish Prisoner Scam (Late 1700s)
The Spanish Prisoner Scam will probably sound very familiar to anyone who received a “Nigerian prince” email in the early 2000s.
Victims received letters from a “wealthy Spanish prisoner” who needed their help to access his fortune. If they sent money to facilitate his escape and travel, he would reward them with greater riches when he regained his fortune. This was only one of many similar scams in the 1700s, often involving follow-up requests for additional payments before the scammer disappeared.
While the “Nigerian prince” scam received enough publicity that it became almost unbelievable that people could fall for it, if done well, these can be psychologically sophisticated scams. The stories play on people’s emotions, get them invested in the person, and enamor them with the idea of being someone helpful and important. A compelling narrative can diminish someone’s critical thinking and cause them to ignore red flags.
Today, these scams are more likely to take the form of inheritance fraud or a lottery scam, where, again, a person has to pay an advance fee to unlock a much bigger reward, playing on the common desire for easy money.
Evolution of Fraud
These examples make it clear that fraud is nothing new and that effective tactics have thrived over the centuries. Technology simply opens up new opportunities for fraud.
While 18th-century scammers had to rely on face-to-face contact and fraudulent letters, in the 19th century they could leverage the telegraph for “urgent” communication and newspaper ads to reach broader audiences. In the 20th century, there were telephones and television ads. Today, there are email, social media, and deepfakes, with new technologies emerging daily.
Rather than quack doctors offering miracle cures, we see online health scams selling diet pills and antiaging products. Rather than impersonating real people, we see fake social media accounts and catfishing. Fraudulent sites convince people to enter their bank details rather than asking them to send money. The anonymity of the digital world protects perpetrators.
But despite the technology changing, the underlying psychology that makes scams successful remains the same:
- Greed and the desire for easy money
- Fear of missing out and the belief that a response is urgent
- Social pressure to “keep up with the Joneses” and the “Bandwagon Effect”
- Trust in authority without verification
Therefore, the best protection against scams remains the same: critical thinking and skepticism, not technology.
Responding to Fraud
In conclusion, Vazdar shared a series of steps that people should take to protect themselves against fraud:
- Think before you click.
- Beware of secrecy and urgency.
- Verify identities.
- If it seems too good to be true, be skeptical.
- Use available security tools.
Those security tools have changed over time and will continue to change, but the underlying steps for identifying and preventing fraud remain the same.
For more insights from Vazdar and other experts in the field, consider enrolling in highly specialized and comprehensive programs like OPIT’s Enterprise Security Master’s program.
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