Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on enabling computers to “think” for themselves. Of course, they owe this thinking to humans (data scientists and ML engineers) who continuously supervise ML algorithms and models.

So, there’s no AI takeover (for now at least), just incredible ways to propel several industries forward by automating repetitive tasks, extracting valuable insights from data, and improving decision-making processes.

But how do humans precisely communicate with computers in machine learning?

The answer is through programming languages.

One programming language stands out among the rest for its simplicity and versatility. By the title of this guide, you can already guess we’re talking about Python.

This beloved programming language is all over the machine learning field, so mastering it gives you a great head start in the industry.

With this in mind, let’s examine how you can learn Python for machine learning courses. If you already have some basic knowledge of this programming language, don’t worry. We’ll also mention a great machine learning Python course to take your knowledge to the next level.

Factors to Consider When Choosing a Python for ML Course

Do a Google search for “machine learning Python course,” and you’ll be met with dozens of web pages that promise a sound understanding of this programming language. However, you’ll find the best course for your needs if you can identify those needs first.

Course Content and Curriculum

Your chosen course’s curriculum is arguably the most important factor for selecting the perfect machine learning Python course. One look at the listed topics, and you’ll know whether the course is right for you.

Let’s take your previous experience with Python as an example. If you have none, a course that jumps straight into machine learning algorithms without covering the Python basics will obviously not work for you.

Instructor’s Expertise and Experience

What bridges the gap between struggling to comprehend a complex subject and feeling that nothing can stop you in your learning journey? The answer is simple – a good instructor.

Before committing to a course, check who teaches it. Find out the instructor’s background with Python and whether they have enough expertise to guide you through this programming language’s intricacies.

If their bio checks all the boxes, watch at least one of their lectures. It doesn’t hurt to check whether their teaching style and voice suit you, as these can also make or break your learning experience.

Course Duration and Flexibility

Most online courses are self-paced, allowing you to create your own schedule. Fixed-timing courses also have their benefits, though. They’re usually instructor-led, so you can use the opportunity to ask questions and receive clarification as you learn the material.

As for duration, the course’s description typically indicates how long the course lasts and the recommended pace. Before starting, make sure you can commit to the course from beginning to end. Otherwise, you’re just wasting time and gaining incomplete knowledge.

Hands-On Projects and Real-World Applications

Programming languages are inherently practical, so ensure that your chosen course features hands-on projects and practical examples. Sticking solely to theory will do little to prepare you for what’s waiting in the real world.

Course Reviews and Ratings

You probably check reviews before going to a new restaurant, renting an Airbnb, or purchasing clothes online. So why should shopping for online courses be any different? When a course piques your interest, check how other learners have rated it. But don’t stop at glancing at the average rating. Read through some reviews to ensure they aren’t fake and to get a better picture of the course’s quality.

Pricing and Value for Money

There are plenty of free machine learning resources online. But the more advanced courses and certificates usually come with a fee. And that’s perfectly understandable. What’s not understandable or acceptable are courses that charge ridiculously high fees yet offer little value. To avoid wasting money (and probably time), check whether the course’s price is justifiable by its duration, level, type, and provided support.

Top Python for ML Courses Reviewed

Here are our favorite Python courses primarily focused on machine learning. We’re positive you’ll find the perfect machine learning Python course, whether this is the first time you use this programming language or want to master this skill.

Python for Machine Learning

The Python for Machine Learning course on Great Learning is a great place to start your Python-learning journey. This course is beginner-friendly and relatively short, so you won’t get overwhelmed from the get-go.

This course focuses on three Python libraries: NumPy, Pandas, and Matplotlib. It guides you through the basic concepts (arrays, intersection, loading, etc.) and then moves on to more complex functions. At the end of the course, you take a quiz. Pass the quiz, and you’ll get a certificate of completion.

Applying for this course is free. Not only that, but you’ll also receive free lifetime access, so you can revisit the course whenever you’d like. Although, some learners believe that there’s little to revisit. In total, this course lasts for 90 minutes. Those who are serious about Python learning will probably need more than this.

Still, you can view this course as a beginner’s guide and move to more advanced lessons afterward. To apply, you only need to create an account on the platform and send an enrollment request.

Machine Learning A-Z: AI, Python & R

If you want to start with the basics but cover the more advanced stuff within the same course, this Udemy’s gem is for you. It covers another programming language besides Python, R. However, this won’t be an issue, as you can focus solely on Python.

The course is broken into 10 parts, with over 40 hours of on-demand videos. Each section (and even the lessons within them) is separate, so you can choose to complete the ones that will benefit you now. Start with data preprocessing, and work toward machine learning model selection.

Those seeking practical exercises in Python will love this course. However, you might need to research some notions independently, as not all lecture sections are explained in great detail.

You can purchase lifetime access to this course for $89.99 (a little over €83). The price includes a certificate of completion and several additional learning materials (articles and downloadable resources). Complete the purchase to apply for this course.

Machine Learning With Python by IBM

IBM is one of the leading companies in the machine learning field, so you should take advantage of every chance to learn from its experts. If you’re just gaining your footing in machine learning, you’ll cover all your bases with this offering.

It will take approximately 12 hours over four weeks to complete the coursework. After each lesson, you’ll get a chance to put your newly-learned knowledge to the test.

One thing to keep in mind is that this course focuses more on machine learning using Python than the programming language itself. So, if you’ve never worked with Python, an additional resource or two might come in handy.

You can use Coursera’s 7-day trial to enroll in this course. Afterward, you’ll be charged $39 (approximately €36) a month. The same fee is a must if you want to receive a certificate.

The Complete Machine Learning Course With Python

Are you a data scientist in the making looking to build a solid portfolio with Python? If yes, you’ll love this course. You can find it on Udemy, just like millions of learners before you. This number might surprise you at first. But once you see that one of the founders of this course is Andrew Ng, a thought leader in machine learning, it will make much more sense.

In 18 hours, this course covers all the basics of machine learning with Python. But there’s a catch. You’ll need at least basic Python programming knowledge to keep up.

If this isn’t an issue, create an Udemy account and pay the $59.99 (around €55.50) fee to apply. Lifetime access and a certificate of completion are included.

Programming for Everybody (Getting Started With Python)

While not focused on machine learning per se, this course is necessary for anyone who has yet to work with Python. Pair it with one of the other courses on our list, and your success is guaranteed.

As the name implies, this course covers all the basics. It is designed to allow virtually anyone to follow, regardless of their skills. The simplest math is all you need.

You’ll also need 19 hours to complete this course offered by the University of Michigan. However, the instructor snuck a couple of non-Python-related stories into those 19 hours, which some learners didn’t like.

If you don’t mind a break here and there, join this course on Coursera for free or $49 (a little over €45) if you want a certificate.

Additional Resources for Learning Python for Machine Learning

Perhaps you can’t get enough of learning about Python. Or you find Python for machine learning courses lacking information. Whatever the case, you can find additional resources (both online and offline) to help you master this programming language. Check out some of our favorites:

  • Books and e-books: “Python for Data Science, for Dummies,” “Introduction to Machine Learning with Python: A Guide for Data Scientists,” “Python Data Science Handbook: Essential Tools for Working with Data”
  • Blogs: Planet Python, Real Python
  • YouTube channels: IBM Technology, Google Career Certificates, techTFQ
  • Community forums and discussion groups: Kaggle Discussions, Reddit (r/learnpython)

The Path to Python

As you can see, there’s no shortage of Python for machine learning courses, even hosted by some of the biggest names in the industry. Take one of the listed courses or combine them; the choice is all yours. All that matters is that you ultimately master this programming language and crush any data science career you choose.

If these courses aren’t enough to quench your thirst for knowledge, a Bachelor’s in Modern Computer Science will definitely do the trick. With it, you can learn all the ins and outs of Python and machine learning in general.

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Master the AI Era: Key Skills for Success
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 24, 2025 6 min read

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.

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Lessons From History: How Fraud Tactics From the 18th Century Still Impact Us Today
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2025 6 min read

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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