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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Cyber Threat Landscape 2024: Human-Centric Cyber Threats
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

Human-centric cyber threats have long posed a serious issue for organizations. After all, humans are often the weakest link in the cybersecurity chain. Unfortunately, when artificial intelligence came into the mix, it only made these threats even more dangerous.

So, what can be done about these cyber threats now?

That’s precisely what we asked Tom Vazdar, the chair of the Enterprise Cybersecurity Master’s program at the Open Institute of Technology (OPIT), and Venicia Solomons, aka the “Cyber Queen.”

They dedicated a significant portion of their “Cyber Threat Landscape 2024: Navigating New Risks” master class to AI-powered human-centric cyber threats. So, let’s see what these two experts have to say on the topic.

Human-Centric Cyber Threats 101

Before exploring how AI impacted human-centric cyber threats, let’s go back to the basics. What are human-centric cyber threats?

As you might conclude from the name, human-centric cyber threats are cybersecurity risks that exploit human behavior or vulnerabilities (e.g., fear). Even if you haven’t heard of the term “human-centric cyber threats,” you’ve probably heard of (or even experienced) the threats themselves.

The most common of these threats are phishing attacks, which rely on deceptive emails to trick users into revealing confidential information (or clicking on malicious links). The result? Stolen credentials, ransomware infections, and general IT chaos.

How Has AI Impacted Human-Centric Cyber Threats?

AI has infiltrated virtually every cybersecurity sector. Social engineering is no different.

As mentioned, AI has made human-centric cyber threats substantially more dangerous. How? By making them difficult to spot.

In Venicia’s words, AI has allowed “a more personalized and convincing social engineering attack.”

In terms of email phishing, malicious actors use AI to write “beautifully crafted emails,” as Tom puts it. These emails contain no grammatical errors and can mimic the sender’s writing style, making them appear more legitimate and harder to identify as fraudulent.

These highly targeted AI-powered phishing emails are no longer considered “regular” phishing attacks but spear phishing emails, which are significantly more likely to fool their targets.

Unfortunately, it doesn’t stop there.

As AI technology advances, its capabilities go far beyond crafting a simple email. Venicia warns that AI-powered voice technology can even create convincing voice messages or phone calls that sound exactly like a trusted individual, such as a colleague, supervisor, or even the CEO of the company. Obey the instructions from these phone calls, and you’ll likely put your organization in harm’s way.

How to Counter AI-Powered Human-Centric Cyber Threats

Given how advanced human-centric cyber threats have gotten, one logical question arises – how can organizations counter them? Luckily, there are several ways to do this. Some rely on technology to detect and mitigate threats. However, most of them strive to correct what caused the issue in the first place – human behavior.

Enhancing Email Security Measures

The first step in countering the most common human-centric cyber threats is a given for everyone, from individuals to organizations. You must enhance your email security measures.

Tom provides a brief overview of how you can do this.

No. 1 – you need a reliable filtering solution. For Gmail users, there’s already one such solution in place.

No. 2 – organizations should take full advantage of phishing filters. Before, only spam filters existed, so this is a major upgrade in email security.

And No. 3 – you should consider implementing DMARC (Domain-based Message Authentication, Reporting, and Conformance) to prevent email spoofing and phishing attacks.

Keeping Up With System Updates

Another “technical” move you can make to counter AI-powered human-centric cyber threats is to ensure all your systems are regularly updated. Fail to keep up with software updates and patches, and you’re looking at a strong possibility of facing zero-day attacks. Zero-day attacks are particularly dangerous because they exploit vulnerabilities that are unknown to the software vendor, making them difficult to defend against.

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Nurturing a Culture of Skepticism

The key component of the human-centric cyber threats is, in fact, humans. That’s why they should also be the key component in countering these threats.

At an organizational level, numerous steps are needed to minimize the risks of employees falling for these threats. But it all starts with what Tom refers to as a “culture of skepticism.”

Employees should constantly be suspicious of any unsolicited emails, messages, or requests for sensitive information.

They should always ask themselves – who is sending this, and why are they doing so?

This is especially important if the correspondence comes from a seemingly trusted source. As Tom puts it, “Don’t click immediately on a link that somebody sent you because you are familiar with the name.” He labels this as the “Rule No. 1” of cybersecurity awareness.

Growing the Cybersecurity Culture

The ultra-specific culture of skepticism will help create a more security-conscious workforce. But it’s far from enough to make a fundamental change in how employees perceive (and respond to) threats. For that, you need a strong cybersecurity culture.

Tom links this culture to the corporate culture. The organization’s mission, vision, statement of purpose, and values that shape the corporate culture should also be applicable to cybersecurity. Of course, this isn’t something companies can do overnight. They must grow and nurture this culture if they are to see any meaningful results.

According to Tom, it will probably take at least 18 months before these results start to show.

During this time, organizations must work on strengthening the relationships between every department, focusing on the human resources and security sectors. These two sectors should be the ones to primarily grow the cybersecurity culture within the company, as they’re well versed in the two pillars of this culture – human behavior and cybersecurity.

However, this strong interdepartmental relationship is important for another reason.

As Tom puts it, “[As humans], we cannot do anything by ourselves. But as a collective, with the help within the organization, we can.”

Staying Educated

The world of AI and cybersecurity have one thing in common – they never sleep. The only way to keep up with these ever-evolving worlds is to stay educated.

The best practice would be to gain a solid base by completing a comprehensive program, such as OPIT’s Enterprise Cybersecurity Master’s program. Then, it’s all about continuously learning about new developments, trends, and threats in AI and cybersecurity.

Conducting Regular Training

For most people, it’s not enough to just explain how human-centric cyber threats work. They must see them in action. Especially since many people believe that phishing attacks won’t happen to them or, if they do, they simply won’t fall for them. Unfortunately, neither of these are true.

Approximately 3.4 billion phishing emails are sent each day, and millions of them successfully bypass all email authentication methods. With such high figures, developing critical thinking among the employees is the No. 1 priority. After all, humans are the first line of defense against cyber threats.

But humans must be properly trained to counter these cyber threats. This training includes the organization’s security department sending fake phishing emails to employees to test their vigilance. Venicia calls employees who fall for these emails “clickers” and adds that no one wants to be a clicker. So, they do everything in their power to avoid falling for similar attacks in the future.

However, the key to successful employee training in this area also involves avoiding sending similar fake emails. If the company keeps trying to trick the employees in the same way, they’ll likely become desensitized and less likely to take real threats seriously.

So, Tom proposes including gamification in the training. This way, the training can be more engaging and interactive, encouraging employees to actively participate and learn. Interestingly, AI can be a powerful ally here, helping create realistic scenarios and personalized learning experiences based on employee responses.

Following in the Competitors’ Footsteps

When it comes to cybersecurity, it’s crucial to be proactive rather than reactive. Even if an organization hasn’t had issues with cyberattacks, it doesn’t mean it will stay this way. So, the best course of action is to monitor what competitors are doing in this field.

However, organizations shouldn’t stop with their competitors. They should also study other real-world social engineering incidents that might give them valuable insights into the tactics used by the malicious actors.

Tom advises visiting the many open-source databases reporting on these incidents and using the data to build an internal educational program. This gives organizations a chance to learn from other people’s mistakes and potentially prevent those mistakes from happening within their ecosystem.

Stay Vigilant

It’s perfectly natural for humans to feel curiosity when it comes to new information, anxiety regarding urgent-looking emails, and trust when seeing a familiar name pop up on the screen. But in the world of cybersecurity, these basic human emotions can cause a lot of trouble. That is, at least, when humans act on them.

So, organizations must work on correcting human behaviors, not suppressing basic human emotions. By doing so, they can help employees develop a more critical mindset when interacting with digital communications. The result? A cyber-aware workforce that’s well-equipped to recognize and respond to phishing attacks and other cyber threats appropriately.

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Cyber Threat Landscape 2024: The AI Revolution in Cybersecurity
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

There’s no doubt about it – artificial intelligence has revolutionized almost every aspect of modern life. Healthcare, finance, and manufacturing are just some of the sectors that have been virtually turned upside down by this powerful new force. Cybersecurity also ranks high on this list.

But as much as AI can benefit cybersecurity, it also presents new challenges. Or – to be more direct –new threats.

To understand just how serious these threats are, we’ve enlisted the help of two prominent figures in the cybersecurity world – Tom Vazdar and Venicia Solomons. Tom is the chair of the Master’s Degree in Enterprise Cybersecurity program at the Open Institute of Technology (OPIT). Venicia, better known as the “Cyber Queen,” runs a widely successful cybersecurity community looking to empower women to succeed in the industry.

Together, they held a master class titled “Cyber Threat Landscape 2024: Navigating New Risks.” In this article, you get the chance to hear all about the double-edged sword that is AI in cybersecurity.

How Can Organizations Benefit From Using AI in Cybersecurity?

As with any new invention, AI has primarily been developed to benefit people. In the case of AI, this mainly refers to enhancing efficiency, accuracy, and automation in tasks that would be challenging or impossible for people to perform alone.

However, as AI technology evolves, its potential for both positive and negative impacts becomes more apparent.

But just because the ugly side of AI has started to rear its head more dramatically, it doesn’t mean we should abandon the technology altogether. The key, according to Venicia, is in finding a balance. And according to Tom, this balance lies in treating AI the same way you would cybersecurity in general.

Keep reading to learn what this means.

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Implement a Governance Framework

In cybersecurity, there is a governance framework called ISO/IEC 27000, whose goal is to provide a systematic approach to managing sensitive company information, ensuring it remains secure. A similar framework has recently been created for AI— ISO/IEC 42001.

Now, the trouble lies in the fact that many organizations “don’t even have cybersecurity, not to speak artificial intelligence,” as Tom puts it. But the truth is that they need both if they want to have a chance at managing the risks and complexities associated with AI technology, thus only reaping its benefits.

Implement an Oversight Mechanism

Fearing the risks of AI in cybersecurity, many organizations chose to forbid the usage of this technology outright within their operations. But by doing so, they also miss out on the significant benefits AI can offer in enhancing cybersecurity defenses.

So, an all-out ban on AI isn’t a solution. A well-thought-out oversight mechanism is.

According to Tom, this control framework should dictate how and when an organization uses cybersecurity and AI and when these two fields are to come in contact. It should also answer the questions of how an organization governs AI and ensures transparency.

With both of these frameworks (governance and oversight), it’s not enough to simply implement new mechanisms. Employees should also be educated and regularly trained to uphold the principles outlined in these frameworks.

Control the AI (Not the Other Way Around!)

When it comes to relying on AI, one principle should be every organization’s guiding light. Control the AI; don’t let the AI control you.

Of course, this includes controlling how the company’s employees use AI when interacting with client data, business secrets, and other sensitive information.

Now, the thing is – people don’t like to be controlled.

But without control, things can go off the rails pretty quickly.

Tom gives just one example of this. In 2022, an improperly trained (and controlled) chatbot gave an Air Canada customer inaccurate information and a non-existing discount. As a result, the customer bought a full-price ticket. A lawsuit ensued, and in 2024, the court ruled in the customer’s favor, ordering Air Canada to pay compensation.

This case alone illustrates one thing perfectly – you must have your AI systems under control. Tom hypothesizes that the system was probably affordable and easy to implement, but it eventually cost Air Canada dearly in terms of financial and reputational damage.

How Can Organizations Protect Themselves Against AI-Driven Cyberthreats?

With well-thought-out measures in place, organizations can reap the full benefits of AI in cybersecurity without worrying about the threats. But this doesn’t make the threats disappear. Even worse, these threats are only going to get better at outsmarting the organization’s defenses.

So, what can the organizations do about these threats?

Here’s what Tom and Venicia suggest.

Fight Fire With Fire

So, AI is potentially attacking your organization’s security systems? If so, use AI to defend them. Implement your own AI-enhanced threat detection systems.

But beware – this isn’t a one-and-done solution. Tom emphasizes the importance of staying current with the latest cybersecurity threats. More importantly – make sure your systems are up to date with them.

Also, never rely on a single control system. According to our experts, “layered security measures” are the way to go.

Never Stop Learning (and Training)

When it comes to AI in cybersecurity, continuous learning and training are of utmost importance – learning for your employees and training for the AI models. It’s the only way to ensure all system aspects function properly and your employees know how to use each and every one of them.

This approach should also alleviate one of the biggest concerns regarding an increasing AI implementation. Namely, employees fear that they will lose their jobs due to AI. But the truth is, the AI systems need them just as much as they need those systems.

As Tom puts it, “You need to train the AI system so it can protect you.”

That’s why studying to be a cybersecurity professional is a smart career move.

However, you’ll want to find a program that understands the importance of AI in cybersecurity and equips you to handle it properly. Get a master’s degree in Enterprise Security from OPIT, and that’s exactly what you’ll get.

Join the Bigger Fight

When it comes to cybersecurity, transparency is key. If organizations fail to report cybersecurity incidents promptly and accurately, they not only jeopardize their own security but also that of other organizations and individuals. Transparency builds trust and allows for collaboration in addressing cybersecurity threats collectively.

So, our experts urge you to engage in information sharing and collaborative efforts with other organizations, industry groups, and governmental bodies to stay ahead of threats.

How Has AI Impacted Data Protection and Privacy?

Among the challenges presented by AI, one stands out the most – the potential impact on data privacy and protection. Why? Because there’s a growing fear that personal data might be used to train large AI models.

That’s why European policymakers sprang into action and introduced the Artificial Intelligence Act in March 2024.

This regulation, implemented by the European Parliament, aims to protect fundamental rights, democracy, the rule of law, and environmental sustainability from high-risk AI. The act is akin to the well-known General Data Protection Regulation (GDPR) passed in 2016 but exclusively targets the use of AI. The good news for those fearful of AI’s potential negative impact is that every requirement imposed by this act is backed up with heavy penalties.

But how can organizations ensure customers, clients, and partners that their data is fully protected?

According to our experts, the answer is simple – transparency, transparency, and some more transparency!

Any employed AI system must be designed in a way that doesn’t jeopardize anyone’s privacy and freedom. However, it’s not enough to just design the system in such a way. You must also ensure all the stakeholders understand this design and the system’s operation. This includes providing clear information about the data being collected, how it’s being used, and the measures in place to protect it.

Beyond their immediate group of stakeholders, organizations also must ensure that their data isn’t manipulated or used against people. Tom gives an example of what must be avoided at all costs. Let’s say a client applies for a loan in a financial institution. Under no circumstances should that institution use AI to track the client’s personal data and use it against them, resulting in a loan ban. This hypothetical scenario is a clear violation of privacy and trust.

And according to Tom, “privacy is more important than ever.” The same goes for internal ethical standards organizations must develop.

Keeping Up With Cybersecurity

Like most revolutions, AI has come in fast and left many people (and organizations) scrambling to keep up. However, those who recognize that AI isn’t going anywhere have taken steps to embrace it and fully benefit from it. They see AI for what it truly is – a fundamental shift in how we approach technology and cybersecurity.

Those individuals have also chosen to advance their knowledge in the field by completing highly specialized and comprehensive programs like OPIT’s Enterprise Cybersecurity Master’s program. Coincidentally, this is also the program where you get to hear more valuable insights from Tom Vazdar, as he has essentially developed this course.

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