Data permeates almost every aspect of our lives. Trying to make sense of it all is a Herculean endeavor that would take humans years (if not centuries). But fear not; it’s machine learning to the rescue.

Machine learning algorithms can comb through data in a matter of days or even hours, uncovering valuable insights. Many industries have already experienced numerous benefits of these algorithms, yet the field promises to get even bigger and better.

However, we shouldn’t discard humans just yet. They still play an essential role in this process.

Machine learning algorithms couldn’t parse and interpret data correctly without human guidance. As the machine learning field grows, so will the need for skilled data scientists.

One way to acquire the skills necessary to participate in this game-changing field is by taking a machine learning course. When chosen wisely, this course will provide you with crucial theory and invaluable practice to enter the field with a bang or take your knowledge to the next level.

To ensure you choose the best machine learning course, we’ve compiled a list of our top five online picks.

Factors to Consider When Choosing a Machine Learning Course

Just like data, there are tons of courses online. Taking all of them would not be humanly possible. And frankly, not all of these courses would be worth your time. Remember these factors when browsing online learning platforms, and you’ll pick the best machine learning course each time.

Course Content and Curriculum

Shakespeare once said, “Expectation is the root of all heartache.” Believe it or not, this quote will benefit you immensely when choosing an online machine learning course.

Just because a course is named Machine Learning, it doesn’t mean it will be helpful to you. The only way to ensure the course is worth taking is to check its curriculum. Provided the description isn’t misleading, you’ll immediately know whether the course suits your educational and professional needs.

Instructor’s Expertise and Experience

Who teaches the course is as important as what is taught (if not more). Otherwise, you could just pick up a book on machine learning with the same content and try to make sense of it.

So, when a machine learning course piques your interest, check out the instructor.

Are they considered an authority in machine learning? Are they industry veterans?

A quick Google search will tell you all you need to know.

Course Duration and Flexibility

“Can I fully commit to this course?” That is the question to ask yourself before starting a machine learning course.

One look at the course’s description will tell you whether it takes an hour or months to complete. Also, you’ll immediately know if it is self-paced or fixed-timeline.

Hands-On Projects and Real-World Applications

No one can deny the value of theoretical knowledge in a machine learning course. There’s no moving on without understanding machine learning algorithms and underlying principles.

But how will you learn to use those theoretical concepts in practice? That’s right, through hands-on projects and case studies.

Ideally, your chosen course will strike the perfect balance between the two.

Course Reviews and Ratings

Sure, it’s easy to manipulate reviews and ratings. But it’s even easier to spot the fake ones. So, give the rating page a quick read-through, and you should be able to tell if the course is any good.

Certification and Accreditation

Certified and accredited courses are a must for those serious about a career in machine learning. Of course, these courses are rarely free. But if they help you land your dream job, the investment will be well worth it.

Top Picks for the Best Machine Learning Courses

We’ve also considered the above-mentioned factors when choosing our top picks for online machine learning courses. Without further ado, check out the best ones to help you learn or improve machine learning skills.

Supervised Machine Learning: Regression and Classification

This course has a lot of things going for it. It was one of the courses that popularized the entire concept of massive open online courses. And it is taught by none other than Andrew Ng, a pioneer and a visionary leader in machine learning and artificial intelligence (AI). In other words, this course is the gold standard by which every machine learning course is evaluated.

Here are all the important details at a glance:

  • The course is beginner-friendly and features flexible deadlines.
  • It lasts 11 weeks, each covering different machine learning techniques and models (six hours per week).
  • It covers the fundamentals of machine learning and teaches you how to apply them.
  • The skills you will gain include regularization to avoid overfitting, gradient descent, supervised learning, and linear regression.
  • You’ll earn a certificate after completing the course.

The only thing to note about the certificate is that you must sign up for a Coursera membership ($39/€36 a month) to receive it. Otherwise, you can audit the course for free. To apply, you only need to create a Coursera account and press the “Enroll” button.

Machine Learning With Python

Another fan-favorite on Coursera, this machine learning course uses Python (SciPy and scikit-learn libraries). It’s offered by IBM, a company at the forefront of machine learning and AI research.

Here’s what you need to know about this course:

  • The course is beginner-friendly but requires a great deal of calculus knowledge.
  • It’s divided into four weeks, each dedicated to one broad machine learning task (regression, clustering, classification, and their implementation).
  • By the end of the course, you’ll learn the theoretical fundamentals and numerous real-world applications of machine learning.
  • The emphasis is placed on hands-on learning.
  • A certificate is available, provided you apply for a Coursera membership ($39/€36 a month).

A Coursera account is all you need to apply for this course. You can start with a 7-day free trial. You’ll have to pay $39 (approximately €36) a month to continue learning.

Machine Learning Crash Course

Google’s Machine Learning Crash Course is ideal for those who want a fast-paced approach to learning machine learning. This intensive course uses TensorFlow, Google’s popular open-source machine learning framework.

Check out these facts to determine whether this is the best machine learning course for you:

  • You can take this course as a beginner if you read some additional resources before starting.
  • The course consists of 25 lessons that you can complete in 15 hours.
  • Google researchers present the lessons.
  • It perfectly combines theoretical video lectures (machine learning concepts and engineering), real-world case studies, and hands-on exercises.
  • No certificate is issued upon completion.

Enrolling in this course is pretty straightforward – just click the “Start Crash Course” button. The course is free of charge.

Machine Learning A-Z: Hands-On Python & R in Data Science

As its name implies, this Udemy course is pretty comprehensive. Two data scientists teach it, primarily focusing on practical experiences (learning to create machine learning algorithms). If you feel like you’re missing hands-on experience in machine learning, this is the course for you.

Before applying, consider the following information:

  • The course can be beginner-friendly, provided you have solid mathematics knowledge.
  • It consists of video lessons and practical exercises (around 40 hours total).
  • The introductory portion focuses on regression, classification, and clustering models.
  • You’ll receive a certificate of completion.

To gain lifetime access to this course, you’ll need to pay $89.99 (a little over €83). Applying for it is a matter of creating an Udemy account and purchasing the course.

Machine Learning Specialization

This advanced course is the course you want to take when mastering your knowledge of machine learning. Or perhaps we should say courses since this specialization consists of six separate courses. The program was created by Andrew Ng, who also serves as an instructor (one of four total).

Here’s a quick overview of the course’s key features:

  • The course isn’t beginner-friendly; it’s intermediate level and requires previous experience.
  • At a pace of three hours per week, it takes approximately seven months to complete.
  • The course focuses on numerous practical skills, including Python programming, linear regression, and decision trees.
  • Each course includes a hands-on project.
  • You’re awarded a shareable certificate upon completion of each course in the specialization.

To begin this challenging yet rewarding journey, create a Coursera account and enroll in the specialization. Then, you can choose the first course—the entire specialization costs around $350 (close to €324).

Additional Resources for Learning Machine Learning

The more you immerse yourself in machine learning, the faster you advance. So, besides attending a machine learning course, consider exploring additional learning resources, such as:

  • Books and e-books. Books on machine learning provide in-depth explanations of the topic. So, if you feel that a course’s content is insufficient, this is the path for you. Check out “Introduction to Statistical Learning” (theory-focused) and “Hands-On Machine Learning With Scikit-Learn and TensorFlow.
  • Online tutorials and blogs. Due to the complexity of the field, only a few bloggers post consistently on the topic. Still, blogs like Christopher Olah and Machine Learning Mastery are updated relatively frequently and contain plenty of fascinating information.
  • Podcasts and YouTube channels. Keep up with the latest news in machine learning with podcasts like “This Week in Machine Learning and AI.” YouTube channels like Stanford Online also offer a treasure trove of valuable information on the topic.
  • Networking and community involvement. You can learn much about machine learning by sharing insights and ideas with like-minded individuals. Connect with the machine learning community through courses or conferences (AI & Big Data Expo World Series, MLconf).

Master Machine Learning to Transform Your Future

An online machine learning course allows you to learn directly from the best of the best, whether individuals like Andrew Ng or prominent organizations like Google and IBM. Once you start this exciting journey, you probably won’t want to stop. And considering all the career prospects machine learning can bring, why would you?

If you see a future in computer science, consider pursuing a degree from the Open Institute of Technology. Besides machine learning, you’ll acquire all the necessary skills to succeed in this ever-evolving and lucrative field.

Related posts

Wired: Think Twice Before Creating That ChatGPT Action Figure
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 6 min read

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  • Wired, published on May 01st, 2025

People are using ChatGPT’s new image generator to take part in viral social media trends. But using it also puts your privacy at risk—unless you take a few simple steps to protect yourself.

By Kate O’Flaherty

At the start of April, an influx of action figures started appearing on social media sites including LinkedIn and X. Each figure depicted the person who had created it with uncanny accuracy, complete with personalized accessories such as reusable coffee cups, yoga mats, and headphones.

All this is possible because of OpenAI’s new GPT-4o-powered image generator, which supercharges ChatGPT’s ability to edit pictures, render text, and more. OpenAI’s ChatGPT image generator can also create pictures in the style of Japanese animated film company Studio Ghibli—a trend that quickly went viral, too.

The images are fun and easy to make—all you need is a free ChatGPT account and a photo. Yet to create an action figure or Studio Ghibli-style image, you also need to hand over a lot of data to OpenAI, which could be used to train its models.

Hidden Data

The data you are giving away when you use an AI image editor is often hidden. Every time you upload an image to ChatGPT, you’re potentially handing over “an entire bundle of metadata,” says Tom Vazdar, area chair for cybersecurity at Open Institute of Technology. “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.”

OpenAI also collects data about the device you’re using to access the platform. That means your device type, operating system, browser version, and unique identifiers, says Vazdar. “And because platforms like ChatGPT operate conversationally, there’s also behavioral data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

It’s not just your face. If you upload a high-resolution photo, you’re giving OpenAI whatever else is in the image, too—the background, other people, things in your room and anything readable such as documents or badges, says Camden Woollven, group head of AI product marketing at risk management firm GRC International Group.

This type of voluntarily provided, consent-backed data is “a gold mine for training generative models,” especially multimodal ones that rely on visual inputs, says Vazdar.

OpenAI denies it is orchestrating viral photo trends as a ploy to collect user data, yet the firm certainly gains an advantage from it. OpenAI doesn’t need to scrape the web for your face if you’re happily uploading it yourself, Vazdar points out. “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI says it does not actively seek out personal information to train models—and it doesn’t use public data on the internet to build profiles about people to advertise to them or sell their data, an OpenAI spokesperson tells WIRED. However, under OpenAI’s current privacy policy, images submitted through ChatGPT can be retained and used to improve its models.

Any data, prompts, or requests you share helps teach the algorithm—and personalized information helps fine tune it further, says Jake Moore, global cybersecurity adviser at security outfit ESET, who created his own action figure to demonstrate the privacy risks of the trend on LinkedIn.

Uncanny Likeness

In some markets, your photos are protected by regulation. In the UK and EU, data-protection regulation including the GDPR offer strong protections, including the right to access or delete your data. At the same time, use of biometric data requires explicit consent.

However, photographs become biometric data only when processed through a specific technical means allowing the unique identification of a specific individual, says Melissa Hall, senior associate at law firm MFMac. Processing an image to create a cartoon version of the subject in the original photograph is “unlikely to meet this definition,” she says.

Meanwhile, in the US, privacy protections vary. “California and Illinois are leading with stronger data protection laws, but there is no standard position across all US states,” says Annalisa Checchi, a partner at IP law firm Ionic Legal. And OpenAI’s privacy policy doesn’t contain an explicit carve-out for likeness or biometric data, which “creates a grey area for stylized facial uploads,” Checchi says.

The risks include your image or likeness being retained, potentially used to train future models, or combined with other data for profiling, says Checchi. “While these platforms often prioritize safety, the long-term use of your likeness is still poorly understood—and hard to retract once uploaded.”

OpenAI says its users’ privacy and security is a top priority. The firm wants its AI models to learn about the world, not private individuals, and it actively minimizes the collection of personal information, an OpenAI spokesperson tells WIRED.

Meanwhile, users have control over how their data is used, with self-service tools to access, export, or delete personal information. You can also opt out of having content used to improve models, according to OpenAI.

ChatGPT Free, Plus, and Pro users can control whether they contribute to future model improvements in their data controls settings. OpenAI does not train on ChatGPT Team, Enterprise, and Edu customer data⁠ by default, according to the company.

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LADBible and Yahoo News: Viral AI trend could present huge privacy concerns, says expert
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 4 min read

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You’ve probably seen them all over Instagram

By James Moorhouse

Experts have warned against participating in a viral social media trend which sees people use ChatGPT to create an action figure version of themselves.

If you’ve spent any time whatsoever doomscrolling on Instagram or TikTok or dare I say it, LinkedIn recently, you’ll be all too aware of the viral trend.

Obviously, there’s nothing more entertaining and frivolous than seeing AI generated versions of your co-workers and their cute little laptops and piña coladas, but it turns out that it might not be the best idea to take part.

There may well be some benefits to artificial intelligence but often it can produce some pretty disturbing results. Earlier this year, a lad from Norway sued ChatGPT after it falsely claimed he had been convicted of killing two of his kids.

Unfortunately, if you don’t like AI, then you’re going to have to accept that it’s going to become a regular part of our lives. You only need to look at WhatsApp or Facebook messenger to realise that. But it’s always worth saying please and thank you to ChatGPT just in case society does collapse and the AI robots take over, in the hope that they treat you mercifully. Although it might cost them a little more electricity.

Anyway, in case you’re thinking of getting involved in this latest AI trend and sharing your face and your favourite hobbies with a high tech robot, maybe don’t. You don’t want to end up starring in your own Netflix series, à la Black Mirror.

Tom Vazdar, area chair for cybersecurity at Open Institute of Technology, spoke with Wired about some of the dangers of sharing personal details about yourself with AI.

Every time you upload an image to ChatGPT, you’re potentially handing over ‘an entire bundle of metadata’ he revealed.

Vazdar added: “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.

“Because platforms like ChatGPT operate conversationally, there’s also behavioural data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

Essentially, if you upload a photo of your face, you’re not just giving AI access to your face, but also the whatever is in the background, such as the location or other people that might feature.

Vazdar concluded: “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

While we’re at it, maybe stop using ChatGPT for your university essays and general basic questions you can find the answer to on Google as well. The last thing you need is AI knowing you don’t know how to do something basic if it does takeover the world.

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