

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.
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Open Institute of Technology (OPIT) masterclasses bring students face-to-face with real-world business challenges. In OPIT’s July masterclass, OPIT Professor Francesco Derchi and Ph.D. candidate Robert Mario de Stefano explained the principles of regenerative businesses and how regeneration goes hand in hand with growth.
Regenerative Business Models
Professor Derchi began by explaining what exactly is meant by regenerative business models, clearly differentiating them from sustainable or circular models.
Many companies pursue sustainable business models in which they offset their negative impact by investing elsewhere. For example, businesses that are big carbon consumers will support nature regeneration projects. Circular business models are similar but are more focused on their own product chain, aiming to minimize waste by keeping products in use as long as possible through recycling. Both models essentially aim to have a “net-zero” negative impact on the environment.
Regenerative models are different because they actively aim to have a “net-positive” impact on the environment, not just offsetting their own use but actively regenerating the planet.
Massive Transformative Purpose
While regenerative business models are often associated with philanthropic endeavors, Professor Derchi explained that they do not have to be, and that investment in regeneration can be a driver of growth.
He discussed the importance of corporate purpose in the modern business space. Having a strong and clearly stated corporate purpose is considered essential to drive business decision-making, encourage employee buy-in, and promote customer loyalty.
But today, simple corporate missions, such as “make good shoes,” don’t go far enough. People are looking for a Massive Transformational Purpose (MTP) that can take the business to the next level.
Take, for example, Ben & Jerry’s. The business’s initial corporate purpose may have been to make great ice cream and serve it up in a way that people will enjoy. But the business really began to grow when they embraced an MTP. As they announced in their mission statement, “We believe that ice cream can change the world.” Their business activities also have the aim of advancing human rights and dignity, supporting social and economic justice, and protecting and restoring the Earth’s natural systems. While these aims are philanthropic, they have also helped the business grow.
RePlanet
Professor Derchi next talked about RePlanet, a business he recently worked to develop their MTP. Founded in 2015, RePlanet designs and implements customized renewable energy solutions for businesses and projects. The company already operates in the renewable energy field and ranked as the 21st fastest-growing business in Italy in 2023. So while they were already enjoying great success, Derchi worked with them to see if actively embracing a regenerative business model could unlock additional growth.
Working together, RePlanet moved towards an MTP of building a greener future based on today’s choices, ensuring a cleaner world for generations. Meeting this goal started with the energy products that RePlanet sells, such as energy systems that recover heat from dairy farms. But as the business’s MTP, it goes beyond that. RePlanet doesn’t just engage suppliers; it chooses partners that share its specific values. It also influences the projects they choose to work on – they prioritize high-impact social projects, such as recently installing photovoltaic energy systems at a local hospital in Nigeria – and how RePlanet treats its talent, acknowledging that people are the true energy of the company.
Regenerative Business Strategies
Based on work with RePlanet and other businesses, Derchi has identified six archetypal regenerative business strategies for businesses that want to have both a regenerative impact and drive growth:
- Regenerative Leadership – Laying the foundation for regeneration in a broader sense throughout the company
- Nature Regeneration – Strategies to improve the health of the natural world
- Social Regeneration – Regenerating human ecosystems through things such as fair-trade practices
- Responsible Sourcing – Empowering and strengthening suppliers and their communities
- Health & Well-being – Creating products and services that have a positive effect on customers
- Employee Focus – Improve work conditions, lives, and well-being of employees.
Case Studies
Building on the concept of regenerative business models, Roberto Mario de Stefano shared other case studies of businesses that are having a positive impact and enjoying growth thanks to regenerative business models and strategies.
Biorfarm
Biorfarm is a digital platform that supports small-scale agriculture by creating a direct link between small farmers and consumers. Cutting out the middleman in modern supply chains means that farmers earn about 50% more for their produce. They set consumers up as “digital farmers” who actively support and learn about farming activities to promote more conscious food consumption.
Their vision is to create a food economy in which those who produce food and those who consume it are connected. This moves consumers from passive cash cows for large corporations that prioritize profits over the well-being of farmers to actively supporting natural production and a more sustainable system.
Rifo Lab
Rifo Lab is a circular clothing brand with the vision of addressing the problem of overproduction in the clothing industry. Established in Prato, Italy, a traditional textile-producing area, the company produces clothes made from textile waste and biodegradable materials. There are no physical stores, and all orders must be placed online; everything is made to order, reducing excess production.
With an eye on social regeneration, all production takes place within 30 kilometers of their offices, allowing the business to support ethical and local production. They also work with companies that actively integrate migrants into the local community, sharing their local artisan crafts with future generations.
Ogyre
Ogyre is a digital platform that allows you to pay fishermen to fish for waste. When fishermen are out conducting their livelihood, they also collect a significant amount of waste from the ocean, especially plastic waste. Ogyre arranges for fishermen to get paid for collecting that waste, which in turn supports the local fishing communities, and then transforms the waste collected into new sustainable products.
Moving Towards a Regenerative Future
The masterclass concluded with a Q&A session, where it explained that working in regenerative businesses requires the same skills as any other business. But it also requires you to embrace a mindset where value comes from giving and that growth is about working together for a better future, and not just competition.

Riccardo Ocleppo’s vision for the Open Institute of Technology (OPIT) started when he realized that his own university-level training had not properly prepared him for the modern workplace. Technological innovation is moving quickly and changing the nature of work, while university curricula evolve slowly, in part due to systems in place designed to preserve the quality of courses.
Ocleppo was determined to create a higher learning institution that filled the gap between the two realities – delivering high-quality education while preparing professionals to work in dynamic environments that keep pace with technology. Thus, OPIT opened enrolments in 2023 with a curriculum that created a unique bridge between the present and the future.
This is the story of one student, Ania Jaca, whose time at OPIT gave her the skills to connect her knowledge of product design to full system deployment.
Meet Ania
Ania is an example of an active professional who was able to identify what was missing in her own skills that would be needed if she wanted to advance her career in the direction she desired.
Ania is a highly skilled professional who was working on product and industrial design at Deloitte. She has an MA in product design, speaks five languages, studied in China, and is an avid boxer. She had the intelligence and the temperament to succeed in her career, but felt that she lacked the skills to advance and move from determining how products look to how systems really work, scale, and evolve.
Ania taught herself skills such as Python, artificial intelligence (AI), and cloud infrastructure, but soon realized that she needed a more structured education to go deeper. Thus, the search for her next steps began, and her introduction to OPIT.
OPIT appealed to Ania because it offered a fully EU-accredited MSc that she could pursue at her own pace, thanks to remote delivery and flexible hours. But more than that, it filled exactly the knowledge gap she was looking to build upon, teaching her technical foundations, but always with a focus on applications in the real world. Part of the appeal was the faculty, which includes professionals who are leaders in their field and who deal with current professional challenges on a daily basis, which they can bring into the classroom.
Ania enrolled in OPIT’s MSc in Applied Data Science & AI.
MSc in Applied Data Science and AI
This is OPIT’s first master’s program, which also launched in 2023, and is now one of four on offer. The course is designed for graduates like Ania who want a career at the intersection of management and technology. It is attractive to professionals who are already working in this area but lack the technical training to step into certain roles. OPIT requires no computer science prerequisites, so it accepted Ania with her MA in product design.
It is an intensive program that starts with foundational application courses in business, data science, machine learning, artificial intelligence, and problem-solving. The program then moves towards applying data science and AI methodologies and tools to real-life business problems.
The course combines theoretical study with a capstone project that lets students apply what they learn in the real world, either at their existing company or through internship programs. Many of the projects developed by students go on to become fundamental to the businesses they work with.
Ania’s Path Forward
Ania is working on her capstone project with Neperia Group, an Italian-based IT systems development company that works mostly with financial, insurance, and industrial companies. They specialize in developing analysis tools for existing software to enhance insight, streamline management, minimize the impact of corrective and evolutionary interventions, and boost performance.
Ania is specifically working on tools for assessing vulnerabilities in codebases as an advanced cybersecurity tool.
Ania credits her studies at OPIT for helping her build solid foundations in data science, machine learning, and cloud workflows, giving her a thorough understanding of digital products from end to end. She feels this has prepared her for roles at the intersection between infrastructure, security, and deployment, which is exactly where she wants to be. OPIT is excited to see where Ania’s career takes her in the coming years.
Preparing for the Future of Work
Overall, studying at OPIT has helped Ania and others like her prepare for the future of work. According to the Visual Capitalist, the fastest-growing jobs between 2025 and 2030 will be in big data (up by 110%), Fintech engineers (up by 95%), AI and machine learning specialists (up by 85%), software application developers (up by 60%), and security management specialists (up by 55%).
However, while these industries are growing, entry-level opportunities are declining in areas such as software development and IT. This is because AI now performs many of the tasks associated with those roles. Instead, companies are looking for experienced professionals to take on roles that involve more strategic oversight and innovative problem-solving. But how do recent graduates leapfrog past experienced professionals when there is a lack of entry-level positions to make the transition?
This is another challenge that OPIT addresses in its course design. Students don’t just learn the theory, OPIT actively encourages them to focus on applications, allowing them to build experience while studying. The capstone project consolidates this, enabling students to demonstrate to future employers their expertise at deploying technology to solve problems.
OPIT also has a dynamic Career Services department that specifically works with students to prepare them for the types of roles they want. This focus on not only learning but building a career is one of the elements that makes OPIT stand out in preparing graduates for the workplace.
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