For decades, we have used computers to make important decisions in every arena, from business down to our personal lives. Artificial intelligence is the next evolution in computer-based decision-making. Combined with data science, which is the art of processing, extracting, and analyzing data, AI stands to hold a huge influence over our future.

You stand at the cusp of that technological wave. By completing an artificial intelligence and data science course, you develop dual capabilities that put you in the perfect position to enjoy a superb career.

Factors to Consider When Choosing an AI and Data Science Course

You need to know what you’re letting yourself in for before choosing a data science and artificial intelligence course. After all, the course you choose (and its quality) will impact your career prospects. Consider these six factors when making your choice.

1 – Course Content

Both data science and AI are expansive fields that contain a lot of categories and specializations. So, the question you need to ask is does the course cover what I need to know to get the job I want? If it doesn’t, you end up dedicating months (or even years) of your life to a course that brings you no closer to your goals.

2 – Course Duration and Flexibility

Not every student has the luxury of being able to commit full-time to an AI and data science course. Some have work, families, and other commitments to maintain. Ideally, your course should be of an appropriate length for your needs, in addition to offering the flexibility you need to fit your studies around the rest of your life.

3 – Instructor Expertise and Experience

Though data science has been around for decades, AI is still a somewhat nascent field, at least in terms of its modern form. You want to see that your course is created and overseen by people who know what they’re talking about. Do they have direct industry experience? Are their qualifications up to standard? What does your instructor have that makes taking their AI and data science course worthwhile?

4 – Course Fees and Return on Investment

A career in data science is usually strong enough to offer a good return on investment, with European data scientists pulling in an average of €60,815 per year. Throw AI into the mix and you have extra skills that could easily lead you toward six figures. Still, the cost of the course plays a role in your decision, with some courses costing five figures themselves.

5 – Online vs. Offline Courses

Picking between online and offline courses is like playing an arcade game with a guaranteed prize – there’s no way to lose. Your only consideration is what works best for you. Offline courses are great for self-motivated learners who need flexibility. Online courses put you in a classroom environment so you have direct contact with instructors and peers.

6 – Certification and Accreditation

When you finally start applying for jobs, the first thing your potential employer will ask is “Where did this person study their artificial intelligence and data science course?” The answer to that question will impact their decision, meaning your course provider needs to have a solid enough reputation to make their certifications and accreditations worth having.

Top AI and Data Science Courses

There is a metaphorical river of courses, both online and off, that can teach you about artificial intelligence and data science. Here are four of the best.

Course 1 – AI For Business Specialization (University of Pennsylvania via Coursera)

AI, Big Data, and the core concepts behind machine learning combine to create this AI and data science course. Beyond teaching you how to apply these computing concepts in a business setting, AI For Business Specialization digs into the ethics of applying AI fairly inside a business and how these evolving technologies will affect the people you work with, for, and manage.

Key Features

• Direct exposure to industry-hardened professionals who apply the skills you’re learning
• Includes peer-reviewed assessments designed to test your knowledge
• A 100% online course that offers complete flexibility in how you schedule your learning
• No experience in data science or AI required to get started

Pros and Cons

For somebody new to the concepts of AI and data science, this is the perfect course because it starts you out at the beginner level and builds you up from there. It’s flexible, too, with the course providers recommending two hours of learning per week to complete the four-month course. However, the course carries no university credit, so those using it to supplement their existing studies have to make do with the certificate and nothing more.

Course 2 – Machine Learning (Udacity)

Those looking for a budget-conscious artificial intelligence and data science course can rely on Udacity to provide its Machine Learning course at no charge. You’ll need a solid understanding of concepts like linear algebra and probability theory, making this course unsuitable for beginners. But assuming you come prepared, you’ll learn about the main approaches in machine learning (supervised, unsupervised, and reinforcement learning) in a self-paced online environment.

Key Features

• Takes approximately four months to complete, though you can finish at your own pace
• Created and taught by industry experts
• Ideal for building foundational knowledge for future courses related to data science and AI
• Teaches multiple approaches to machine learning

Pros and Cons

The price is certainly right with this course, as you’re getting something very useful at no cost. It’s also an online version of class CS7641, which is taught at Georgia Tech, so the course has real-world credentials behind it. Sadly, its college-based origins don’t mean that you’ll get college credit with the course. It’s also pretty limited to specific forms of machine learning, making it great as an introduction to basic concepts but perhaps not as useful to people who already have some understanding of data science and AI.

Course 3 – Introduction to Artificial Intelligence (AI) (IBM via Coursera)

Quick, intense, and practical are just some of the words we can use to describe this data science and artificial intelligence course. IBM’s experts are clearly masters in the field (they wouldn’t be working for IBM if they weren’t) and they’ve distilled some of the best of their knowledge into this nine-hour completely online course. You’ll learn about the applications of AI in real-world scenarios, start getting to grips with concepts like machine learning and neural networks, and receive direct career advice from your instructors.

Key Features

• Offered by a Fortune 50 company that specializes in AI and data science
• Free enrollment for a self-paced course
• You get direct career advice from people who work in the field
• The course offers a shareable online certificate that looks great on your LinkedIn profile

Pros and Cons

Let’s get the obvious out of the way first – this is an AI and data science course for those who want to learn the fundamentals before building their knowledge in other ways. But it’s the connections that come with the course that make this such a strong contender. Having people from IBM, who already work in the field that interests you, to advise you is great for people who need a route into AI and data science.

Course 4 – Master in Applied Data Science & AI (OPIT)

A Master’s degree allows you to dig deeper into the concepts of AI and data science, with OPIT’s degree being perfect for those in the postgraduate phase who’ve balked at the cost of similar programs. This AI and data science course requires an extensive time investment of between 12 and 18 months, though it’s fully online so you can learn at your own pace. It also counts toward college credits, offering 90 ECTS upon completion.

Key Features

• Completely online so it offers flexibility in terms of how and where you learn
• Provided by an EU-accredited institution to ensure the certification you receive is actually useful
• You get 24/7 access to tutors who can advise you when you’re stuck
• Progressive assessments are favored over “final exams” and other high-pressure tests

Pros and Cons

This artificial intelligence and data science course is the most expensive on the list, clocking in at €6,500 (or €4,950 for early birds). It also requires a BSc in an appropriate field, such as computer science, to start studying. But that investment in both time and money leads you to a course that has full accreditation under the European Qualification Framework and gives you a well-rounded set of skills that set you up for C-Suite positions in your future career.

Tips for Success in AI and Data Science Courses

An AI and data science course could offer the best tutelage in the world but it won’t mean a thing if you’re not applying yourself as a student. These quick tips help you take what you learn further:

• Set clear goals for what you hope to achieve, both within the course and after completion, so you always have a path to follow.
• Don’t take “this course requires x number of hours per week” as given. Practice and set time to study whenever you can to build on your knowledge.
• As valuable as your peers and instructors may be, they’re not the only resources available to you. Engage with online communities and forums to stay up to date on trends in AI and data science.
• Some courses offer direct examples of how what you learn applies to the real world. Others don’t, so you have to seek out (and apply) your learning to real projects yourself.
• Think about what AI looked like five years ago compared to today. This is a continuously evolving field (the same goes for data science), so continued learning is a must once you’ve completed your course.

Combine AI and Data Sciences for Career Advancement

Earlier, we stated that data scientists earn an average of €60,815 per year in Europe. That’s a starting point. Mastery in the fields of AI and data science (which starts with an artificial intelligence and data science course) puts you in a position to work at the C-Suite level in many of today’s businesses. Investing in yourself now, when these fields are still in their growth phase, puts you in the perfect position to take advantage as we see both fields enjoy explosive growth in the future.

AI is everywhere today.

The algorithms that drive your Netflix and Spotify recommendations use AI to figure out what you’ll like based on what you’ve already consumed. Every chatbot you’ve ever spoken to, targeted ad you’ve seen, and even the more fanciful ideas floating around (self-driving cars, anyone?) use AI to some degree.

Given that so many businesses use AI already, it stands to reason that taking online courses on the subject will help you get ahead. But for the budget-conscious among you, a course that costs thousands of euros isn’t the route you want to go down. You want a free AI course.

That’s where this article comes in. But let’s get something clear immediately, a free AI course won’t go into as much depth as a paid one. Nor will it give you a qualification that’s as prestigious as one from a formal educational institution. But what it will give you is foundational knowledge, often backed by a certification, which is why we’re looking at four of the best AI courses you can study for free in this article.

Top Artificial Intelligence Course Online Free With Certificate – Four Great Options

Is it really possible to find an artificial intelligence free course with certificate that shows you have actually learned something useful? It is, and these four courses are great examples.

Course 1 – Elements of AI (University of Helsinki)

With over 950,000 students already to its name, the Elements of AI course is all about lifting the veil on the mysterious concept of AI. It includes two modules, the first giving you an introduction to the “whats” and “wherefores” of AI, with the second digging into building your own AI models. It’s set up to run in 170 countries and is ideal for those who want a basic grasp on AI that they can build on with other courses.

Key Topics Covered

• The theory of AI, including what is and isn’t possible with the tech
• Development of basic AI algorithms
• An introduction (and exploration) of using Python to create AI models
• Practical exercises that you can take at your own pace to see how AI applies in real-world scenarios

Certificate Details

The certification you get from this free AI course comes directly from the University of Helsinki, which is a recognized and authoritative European institution. But it’s important to note that the certificate is not a degree. Instead, it’s both a demonstration of your grasp of basic AI concepts and a statement of your intent to dig deeper into the topic.

Course 2 – Machine Learning With Python: A Practical Introduction (IBM)

There are three things you want from your AI course – online, free, and practical. IBM’s offering delivers all three, with the focus being on how you can apply machine learning (with Python programs underpinning your models) to the real world. The content is created and delivered by Saeed Aghabozorgi, who’s a senior data scientist at IBM, meaning it comes direct from somebody who understands precisely how machine learning is applied in practical terms.

Key Topics Covered

• Python programming in the context of creating machine learning models
• The theory and application of both supervised and unsupervised learning
• An introduction to the most common machine learning algorithms
• Real-world examples of how machine learning is already impacting society

Certificate Details

In return for five weeks of your time (estimated study – four to five hours per week) you’ll earn an IBM “skill badge.” This online credential verifies that you’ve completed the course and can be shared on social media profiles. The course is also part of IBM’s Data Science Professional Certificate Program, making it a piece of a larger jigsaw puzzle of free AI courses that you can complete over the course of a year to get an IBM certificate.

Course 3 – Supervised Machine Learning: Regression and Classification (DeepLearning.AI via Coursera)

You’re getting into specialization territory with this course, which serves as the first of several that make up DeepLearning.AI’s Machine Learning Specialization certificate. It’s a completely online course that allows you to reset deadlines to suit your schedule and takes about 33 hours of studying to complete. Sadly, it’s only available in English (at the time of writing), which may make it less accessible to non-English speakers.

Key Topics Covered

• A wide-spanning introduction to the various types of machine learning
• Explanations of the best practices for AI implementation currently used in major Silicon Valley companies
• Several mathematical and statistical concepts, such as linear regression
• Practical examples and project work for building predictive machine learning models

Certificate Details

Coursera provides its own shareable certificates to anybody who completes this course, with those certificates being shareable on social media and printable for your CV. It’s also worth noting that this course is part of a wider three-course program. Combine it with DeepLearning.AI’s Advanced Learning Algorithms and Unsupervised Learning and Recommender Systems to get two more course-specific certificates and a certificate for completing all three courses.

Course 4 – Learn With Google AI (Google)

Learn with Google AI is less a dedicated course and more a collection of different modules (and even competitions) designed to help you get to grips with AI. Think of it like a resource bank, only it incorporates practical exercises as well as theoretical information. Beyond the courses themselves, you’ll find a useful glossary and some guides for how AI can apply to environmental and social courses.

Key Topics Covered

• Theoretical modules covering machine learning, neural networks, and the ethics behind AI
• Hands-on tutorials that give you practical experience with the course content
• Real-world examples of how Google incorporates AI into what it does
• Competitions that allow you to test your skills against other participants

Certificate Details

Learn with Google AI isn’t a traditionally structured course, and that’s reflected in the lack of certification for completing the courses in this resource bank. It’s better to think of these courses as free primers that equip you with the knowledge you need to ace other free (or paid) AI courses.

Factors to Consider When Choosing an AI Course

The price is certainly right with a free AI course, but you’re still investing valuable time into whichever program you choose. Think about the following to ensure you spend that time wisely:

• Course content – Though many artificial intelligence free course will cover the basic concepts underpinning AI, you want to know that you’re going somewhere with what you learn. Think about why you’re studying AI and whether the course will move you closer to your goals.
• Course duration and flexibility – Online courses come with a key advantage over traditional programs – you control your studying. That flexibility allows you to fit your studies around your life, though you still have deliverables (and sometimes tests) you need to complete.
• Instructor credentials – With free courses, the certification you get isn’t as immediately prestigious as one you’d receive from a paid course. A respected instructor can add that prestige. Research the background of whoever creates and delivers the course, specifically checking their reputation as a teacher and experiences in the AI industry.
• Community support and resources – Given that most free AI courses focus on self-learning, you need to know that there are people (or resources) around to help when you get stuck. No learner is an island. If there are other students and instructors around to offer guidance, you have a course that you’re more likely to pass.
• Certificate value – As touched upon earlier, the value of your certificate plays a role in your decision, with specific attention being paid to how employers will see that certificate on your CV. A respected instructor or a course delivered by a major brand (think Google or IBM) adds credibility compared to courses delivered by nameless and faceless individuals.

Tips for Successfully Completing an AI Course Online

No athlete gets a gold medal for running half a race, and the same applies to students who don’t complete the courses they start. Use these tips to see you through when the going gets tough:

• Set clear goals for yourself, which inform the course you choose and help to motivate you if you start feeling discouraged when struggling with the material.
• Dedicate time to learning both in the context of your course and by parsing out personal time for practice.
• Engage with the community that’s evolved around the course to learn directly from peers and qualified professionals.
• Never be afraid of seeking help when needed, as you’re learning some complex concepts that are all too easy to misinterpret.
• Take every opportunity you can find to apply the theoretical concepts you learn in real-world scenarios.

Study AI Courses Free Online

A free AI course is never going to be a direct substitute for a paid course delivered by a recognized institution. But it doesn’t have to be. Free courses can set you up with general skills that you can apply in your existing workplace, in addition to helping you lay a foundation for future study. And in some cases (such as with courses offered directly by major AI players) you’ll get a certification that actually means something to employers.

AI is going to be so much more than a part of future technology. It’ll be the bedrock on which everything to come is built. Your efforts to expand your knowledge in the field will help you become one of the people who lay that bedrock. The sooner you start learning (and applying) AI, the better your position will be when the AI revolution truly takes hold.

Artificial intelligence (AI) is a modern-day monolith that is likely going to be as important to the world as the introduction of the internet. We already see it creeping into every aspect of industry, from the basic chatbots you find on many websites to the self-driving cars under production at companies like Tesla.

As an industry, AI looks set to zoom past its current global valuation of $100 billion, becoming worth a staggering $2 trillion by 2030. To ensure you enjoy a prosperous career in an increasingly computer-powered world, you need to learn about AI. That’s where each artificial intelligence tutorial in this list can help you.

Top AI Tutorials for Beginners

If you know nothing about AI beyond the name, these are the three tutorials to get you started with the subject.

Tutorial 1 – Artificial Intelligence Tutorial for Beginners: Learn the Basics of AI (Guru99)

You need to get to the grips with AI theory before you can start with more practical work. Guru99’s tutorial helps you there, with a set of 11 lessons that take you from the most basic of concepts (what is AI?) to digging into the various types of machine learning. It’s like a crib notes version of an AI book, as it takes you on a speedy flight through AI fundamentals before capping its offer with a look at some practical applications.

Key Topics

• The basic theory of AI and machine learning
• Different types of machine learning algorithms
• An introduction to neural networking

Why Take This Artificial Intelligence Tutorial?

The tutorial is completely free, with every lesson being accessible via the Guru99 website with the click of a mouse. It’s also a great choice for complete AI newbies. You’ll cover the basics first, getting a grounding in AI in the process, before moving on to more complicated aspects of machine learning.

Tutorial 2 – Artificial Intelligence Tutorial for Beginners (Simplilearn)

This 14-lesson tutorial may seem intimidating at first. However, those 14 lessons only take an hour to complete, and the course has no prerequisites. This combination of brevity and a lack of tutorial requirements make it ideal for beginners who want to get to grips with the theory of AI. It’ll also help you develop some programming skills useful in more advanced courses.

Key Topics

• Basic programming skills you can use to develop AI models
• An introduction to Big Data and Spark
• Basic AI concepts, including machine learning, linear algebra, and algorithms

Why Take This Artificial Intelligence Tutorial?

Many of the tutorials you come across online will ask you to have a basic understanding of probability theory and linear algebra. This course equips you with those skills, in addition to giving you a solid grounding in many of the AI concepts (and machine learning models) you’ll encounter when you reach the intermediate level. Think of it as a crash course in the basics of AI.

Top AI Tutorials for Intermediate Learners

If you have a grasp of the basics, meaning you can separate your supervised learning algorithms from your unsupervised ones, you’re ready for these intermediate-level tutorials.

Tutorial 1 – Intro to Artificial Intelligence (Udacity)

Don’t let the use of the word “intro” in this tutorial’s name fool you because this is more than a mere explanation of AI concepts. As a four-month course, it requires you to have a good understanding of concepts like linear algebra and probability theory. Assuming you have that understanding, you’ll embark on a four-month self-paced learning journey (that’s completely free) that delves deep into the applications of AI.

Key Topics

• The theoretical and practical applications of natural language processing
• How AI has uses in every aspect of modern life, from advanced research to gaming
• The fundamentals of AI that underpin the practical applications you learn about

Why Take This Artificial Intelligence Tutorial?

The price tag is right, as this is one of the few Udacity courses you can take without spending any money. It’s also created by two of the best minds in AI – Peter Norvig and Sebastian Thrun – who deliver a nice mix of content, including instructor-led videos, quizzes, and experiential learning. Granted, there’s a large time commitment. But that commitment pays off as the course delivers a solid understanding of AI’s fundamentals and practical applications.

Tutorial 2 – Natural Language Processing Specialization (Coursera)

Anybody who’s used ChatGPT or “spoken” to a chatbot knows that a lot of companies are interested in what AI can do to deliver written content. That’s where Natural Language Processing (NLP) comes in, and this course is ideal for understanding the techniques that allow you to build chatbots and similar technologies.

Key Topics

• How to use logistic regression (and other techniques) to conduct sentiment analysis
• Build autocomplete and autocorrect models
• Discover how to develop AI algorithms that both detect and use human language

Why Take This Artificial Intelligence Tutorial?

Specialization is the key as you get deeper into the AI field. With this course, you focus your learning on language models and NLP, allowing you to dig deeper into an in-demand field that offers plenty of career opportunities. It’s somewhat intensive, requiring four months of study at about 10 hours per week to complete. But you get a shareable certificate at the end and develop a foundation in NLP that can apply in many business areas.

Top AI Tutorials for Advanced Learners

By the time you reach the advanced stage, you’re ready for your AI tutorials to teach you how to build and operate your own AI.

Tutorial 1 – Artificial Intelligence A-Z 2023: Build an AI With ChatGPT4 (Udemy)

With backing from a successful Kickstarter campaign, the Artificial Intelligence A-Z tutorial covers some of the fundamentals but focuses mostly on practical applications. You’ll create several types of AI, including a snazzy virtual self-driving car and an AI designed to beat simple games, helping you get to grips with how to put the theory you’ve learned into practice. The tutorial comes with 17 videos, a trio of downloadable resources, and 20 articles. All of which you can access whenever you need them.

Key Topics

• How to build practical AIs that actually do things
• The fundamentals of complex topics, such as Q-Learning
• How Asynchronous Advantage Actor Critic (AC3) applies to modern AI

Why Take This Artificial Intelligence Tutorial?

The two main reasons to take this tutorial are that it gives you hands-on experience with some exciting AI concepts, and you get a certificate you can put on your CV when you’ve finished. It’s well-structured and popular, with almost 204,000 students having already taken it from all over the world. And at just £59.99 (approx. €69), you get a lot of bang for your buck with videos, articles, and downloadable resources.

Tutorial 2 – A* Pathfinding Tutorial – Unity (YouTube)

Many prospective game developers will get their start with Unity, which is a free development tool that you can use to create surprisingly complex games. This YouTube tutorial series includes 10 videos, which walk you through how to use the A* algorithm to program AIs to determine the paths characters follow in a video game. It requires some programming knowledge, specifically C#, but it’s ideal for those who want to use their AI skills to transition into the world of gaming.

Key Topics

• Using the A* algorithm to create paths for AI-driven characters in video games
• Movement smoothing and terrain-related penalties
• Using multi-threading to improve pathfinding performance

Why Take This Artificial Intelligence Tutorial?

The price is certainly right for this tutorial, as the course creator (Sebastian Lague) makes all of his videos free to view on YouTube. But the biggest benefit of this tutorial is that it introduces complicated concepts that game developers use to determine character movement. If you’re interested in what makes video game characters “work” in terms of their actions in a game, this tutorial shows you the algorithm that underpins it all.

Additional AI Resources

The six tutorials in this list run the gamut from introducing you to the basics of AI to demonstrating specialized applications of the technology. Building on that knowledge requires you to go further, with the following books, podcasts, and websites all being great resources.

Great AI-Related Books

• Artificial Intelligence: A Modern Approach (Peter Norvig and Stuart Russell)
• Python: Advanced Guide to Artificial Intelligence (Giuseppe Bonaccorso)
• Neural Networks and Deep Learning (Charu C Aggarwal)

Great AI-Related Podcasts

• The AI Podcast (Noah Kravitz)
• Artificial Intelligence: AI Podcast (Lex Fridman)
• Eye on AI (Craig Smith)

Great AI-Related Websites and Blogs

• MIT News
• Analytics Vidhya
• KDnuggets

Understand Complex Concepts With an Artificial Intelligence Tutorial

AI is one of the world’s fastest-growing industries, with the previously-mentioned $2 trillion 2030 valuation representing a 20-fold growth from today. The point? Getting in close to the ground floor now by developing your understanding of AI concepts will set you up for a future in which many of the best jobs are in the AI field.

Each artificial intelligence tutorial in this list offers something different to students, from beginners who want to get to grips with AI to those who have a decent understanding and are ready to specialize. Regardless of the course you choose, the most important thing is that you keep learning. AI won’t stay static. It’s like a runaway locomotive that’s going to keep plowing forward, with nothing to stop it, to its next evolution. Use these tutorials to learn both basic and advanced concepts, then build on that learning with continued education.

Growth is inevitable in the AI sector. According to Statista, the already-booming industry looks set to go from a value of $100 billion in 2021 to $2 trillion by 2030, increasing by a multiple of 20 to become one of the world’s biggest industries. Naturally, the need for skilled AI professionals will grow alongside that enormous scaling.

That’s where you come in.

With the right applied AI course, you can develop both the knowledge of the foundational theory that sits behind AI and learn how to apply that theory in a real-world setting. Here are four of the best applied AI courses to get you started.

Factors to Consider When Choosing an Applied AI Course

Every search for a new course starts with figuring out the strengths and weaknesses of each one you consider. These factors help you do that, ensuring you don’t spend your hard-earned money on a course that fails to equip you with skills that make you desirable to employers.

Course Content and Curriculum

AI is such an expansive field that every applied AI course has the potential to cover different topics and subjects. Think about what you want to learn (and your prospective career path), then align your course selection with that intended path.

Course Duration

Applied AI courses can vary tremendously in length, from several years for degree-level courses to a few months for online courses. Ask yourself how long you wish to spend studying. Also, consider the flexibility of the course, such as whether you’ll be able to fit your studies around your existing work and family commitments.

Instructor Expertise

AI is a burgeoning industry, meaning expertise levels vary from course to course. For applied AI courses, in particular, you want professors who combine in-depth knowledge of the theory with real-world experience. What have they done in the industry? If the answer is “nothing,” they may not be able to guide you down the path to an AI-centric career.

Course Fees and Financial Aid

Course fees vary massively depending on the type of course you take. For example, those in the U.K. can easily spend between £15,000 and £25,000 on university-level courses, with Aston University’s tuition fees of £23,200 being somewhat typical. Online and self-learning courses cost considerably less, so you need to figure out how much you’re willing to spend (and if you can get any help with your fees) before moving forward.

Job Placement and Career Support

Though you need one eye pointed toward the present when choosing between applied AI courses, the other needs to be firmly pointed toward the future. What prospects will you have when you complete the course? In other words, does the course provide you with a direct path into the industry, along with support, or are you left to fend for yourself once you have your qualification?

Top Choices for Mastering Artificial Intelligence

Choices abound when you jump online to find applied AI courses. The following selection offers a nice mix, from online certifications offered by industry professionals to a couple of courses from some of the world’s most prestigious universities.

Course 1 – IBM Applied AI Professional Certification

If you’re fresh to the world of AI (though ideally not new to computer science), IBM’s industry-specific applied AI courses offer both foundational knowledge and a respected qualification. They’re flexible, too, with this course lasting for six months but only requiring three hours of work per work. Those in full-time work (or education) can fit the course around their lifestyle, while those who have time to burn can complete the entire course much quicker, earning degree credits along the way.

Key Features and Benefits

• Certification from one of the most respected companies in the AI space
• Direct exposure to use cases in the deep learning, machine learning, and neural network spheres
• Learn how to build AI-powered solutions (like chatbots) using Python and IBM’s Watson AI
• Over three-quarters (77%) of students report career improvement

Pricing and Enrollment

IBM’s course is available via Coursera and offers a seven-day trial you can use to get to grips with its structure and examine its modules. It’s fully online, which improves flexibility at the cost of not having direct access to a professor, and you’ll receive an IBM badge upon completion. You’ll pay a monthly fee of $35 (approx. €31) and can enroll at almost any time.

Course 2 – Computer Science for Artificial Intelligence (Harvard University)

Harvard University may be seen as the gold standard in the United States, but what many don’t know is that it offers a comprehensive suite of online courses that almost anybody can take. Its Computer Science for Artificial Intelligence course is a perfect example. Comprising of two courses – an introduction to computer science followed by an introduction to applying computer science principles to AI using Python – it lasts for five months. You get access to professors and can learn at your own pace, with the course recommending between seven and 22 hours of study per week.

Key Features and Benefits

• Two modules give you a crash course in applied AI and the computer science theory that underpins it
• Director access to Harvard professors Doug Lloyd, Brian Yu, and David J. Malan
• Complete flexibility in how and when you learn
• Get to grips with Python and build experience with machine learning libraries

Payment and Enrollment

As an online course, Computer Science for Artificial Intelligence is available for enrollment whenever you’re ready, with the five months starting once you’re enrolled. It costs £277 (approx. €312) and you’ll need to create an account with the EDX website (which hosts the course) to get started.

Course 3 – Artificial Intelligence Graduate Certificate (Stanford University)

Ranked as the third-best university in the United States for general computer science and AI teaching, Stanford University has opened up some of its best courses to online learners. Entirely online (and instructor-led for those who want more guidance) this is one of those applied AI courses that is equivalent to a full graduate degree. You’ll complete at least one required course – with a choice between machine learning and the principles of AI – and select up to three electives. It’s the electives that make this course stand out, as there are 18 to choose from, with the right combination giving you a chance to specialize for specific career paths.

Key Benefits and Features

• Direct tuition from prominent Stanford faculty members, including Andrew Ng and Chelsea Finn
• Some level of autonomy in how you study thanks to the online-centric nature of the course
• Specialize in specific areas of AI thanks to a wide range of electives
• You get a degree from one of the world’s foremost colleges in the AI field

Payment and Enrollment

Let’s get the bad news out of the way immediately – this isn’t a cheap course. As a full-on graduate degree, it costs between $18,200 and $22,400 to take (approx. €16,235 and €19,980), though financial aid may be available for some students. You can’t just hop onto the course, either, as a college-level understanding of calculus, linear algebra, Probability Theory, and several programming languages is required. Stanford itself calls this one of its most difficult courses and recommends that you take several foundation courses (ideally at degree level) before enrolling.

Course 4 – Master in Applied Data Science & AI (OPIT)

As a full postgraduate course that takes between 12 and 18 months to complete, OPIT’s Master in Applied Data Science & AI is an interesting case for one simple reason – there are no computer science prerequisites. The course is open to everybody and it teaches both advanced applied AI concepts and the foundational knowledge needed to understand them. You’ll complete a pair of terms containing courses, with your final term dedicated to a project or thesis that puts what you’ve learned into practice.

Key Benefits and Features

• The course is supplied by an institution with accreditation from the European Qualification Framework
• It’s a fully remote course that gives you control over how and when you learn
• Discounts and payment plans are available, as well as scholarship and funding options
• You come out of the course with a recognized postgraduate degree

Payment and Enrollment

Though the course usually costs €6,500, OPIT offers “early bird” discounts that allow you to enroll for €4,950, assuming you sign up early enough. Intakes are semi-regular, with the next one scheduled for October 2023 and international students get 90 credits under the European Credit Transfer and Accumulation System (ECTS) for successful completion.

Tips for Success in an Applied AI Course

As you can see, you have plenty of options for applied AI courses, from professional certifications designed to get you into a career quickly to full postgraduate degrees. Regardless of your choice, these tips will help you get your precious certification:

• Dedicate time for study – Time well managed is time well spent. Understand that you’ll need to dedicate self-learning time to get to grips with concepts you’re taught during classroom hours.
• Set clear goals – Going into an applied AI course with no sense of what you’re supposed to get out of that course leaves you directionless upon completion. Make sure you know exactly what you stand to gain before committing time (and money) to a course.
• Network often – Even online courses give you a chance to get involved in teamwork projects and speak to experienced industry professionals. Take those chances. The more connections you build during your studies, the more opportunities you’ll see coming out of the back end.
• Seek guidance – As attractive as the prospect of self-guided learning may be, we all need a helping hand from time to time. If a course provides direct access to tutors and professors, use it.
• Stay up to date – AI is a fast-moving field, with every change and advancement bringing new challenges and opportunities. Stay on top of what’s happening in the industry. You may just find that one course sets you up to be ready for those changes, while another may not.

Build Your Skills With an Applied AI Course

Whether you go down the full postgraduate degree route or you choose a professional qualification, an applied AI course is a route into one of the world’s fastest-growing industries. Simply put, we’re set for an AI explosion. Over the next decade, AI will permeate everything we do, from complex computing to simple office tasks, and you can use the right course to give yourself the skills you need to take advantage of that fact. Explore the options shared in this article, ask yourself what you want to achieve in your career, and make the educational choice that’s right for you.

Combine mathematics with analytics, mix in programming skills, and add a dash of artificial intelligence, and you have the recipe for creating a data scientist. These professionals use complex technical skills to parse, analyze, and draw insights from complex datasets, enabling more accurate decision-making in the process.

As companies gather more data than ever before (both about their customers and themselves), these skills are in increasingly high demand. That’s demonstrated by data from the U.S. Bureau of Labor Statistics, which says that the number of data science jobs in the U.S. alone looks set to increase by 36% between 2021 and 2031.

That higher-than-average growth rate creates an opportunity for students, though grasping that opportunity requires a dedication to learning. This article explores the question of what is data science course material and highlights a selection of courses that set you on a data-propelled career path.

What to Expect From a Data Science Course

Answering the question of “what is data science course?” starts with examining the components of the typical course. Bear in mind that these components vary in nature and complexity depending on the specific course you take, though all are usually present.

Overview of Course Content

The content of a data science course is usually split into four core categories:

• Statistics and Probability – Math underpins everything a data scientist does, as they use numbers to spot patterns and determine the likelihood of various potential outcomes. Most data science courses delve into statistics and probability for this reason, with more advanced courses often requiring a degree in a field related to these areas.
• Programming – Whether it’s Python (the most popular data science programming language), R, or SQL, your course will teach you how to write in a language that machines understand.
• Data Visualization and Analysis – Anybody can collect reams of data. It’s the ability to visualize that data (and draw insights from it) that sets data scientists apart from other professionals. A good course equips you with the ability to use visualization tools to shine a spotlight on what a dataset actually tells you.
• Machine Learning and AI – The rise of machine learning transformed data science. Using algorithms created by data scientists, machines can analyze datasets presented to them and learn from the patterns to predict probabilities for different outcomes and even predict market trends. Your course will teach you how to create the algorithms that serve as a machine learning model’s “brain.”

Hands-On Projects and Real-World Applications

If you had the desire, you could read pages and pages on how to tune a car’s engine. But without practical and real-world wrench-in-hand experience working on an engine, you’ll never figure out how what you learn from books applies in the field.

The same line of thinking applies to data science, which is often so technically complex that it’s difficult to see how what you learn applies in the real world. A good data science course incorporates a real-world component through projects and exposure to faculty members who have direct experience in using the skills they teach.

Peer Collaboration and Networking

What is data science course for if not to learn how to become a data scientist? While learning the technical side is crucial, of course, a good course also puts you in contact with like-minded individuals who have the same (or similar) goals as you.

That contact helps you to build the collaborative skills you’ll need when you enter the workforce. But perhaps more importantly, it aids you in creating a network of peers who could lead you to job opportunities or work with you on entrepreneurial ventures.

Top Data Science Courses Available

With the components of a data science course established, you have a vital question to answer – what data science course should you take? The following are four suggestions (two online courses and two university courses) that give you a solid grounding in the subject.

Online Courses

Taking a data science course online gives you flexibility, though you may miss out on some of the collaborative and networking aspects that university-led courses provide.

Course 1 – What Is Data Science? (IBM via Coursera)

Coming with the stamp of approval from IBM, a leading name in the computer science field, this nine-hour course is suitable for beginners who want a self-paced learning approach. It’s part of a multi-part program (the IBM Data Science Professional Certificate) that’s designed to give you an industry-recognized qualification that could fast-track your entry into the field.

As for the course itself, it’s split into three parts, each containing multiple instructor-led videos and quizzes to test what you’ve learned. By the end, you’ll understand what data scientists do, build a basic understanding of various data science-related topics, and see how the profession relates to the modern business world. Granted, the course offers a surface-level understanding of the subject, with more complex topics examined in other classes. But it’s a superb tool for developing the foundation on which you can build with other courses.

Course 2 – Introduction to Data Science With Python (Harvard via edX)

Where IBM’s course equips you with general knowledge, Harvard’s online offering digs into the practical side of data science. Specifically, it focuses on using Python (and its many libraries) to solve data science problems drawn from real-world examples.

The course takes eight weeks, with study time between three and four hours per week. Ultimately, this class helps you build on your established programming skills and shows you how to apply them in a data science context.

As you may have guessed, that mention of building on existing skills means you’ll need a solid understanding of Python to participate in this free course. But assuming you have that, Harvard’s class is ideal for showing you just how flexible the language can be, especially when developing machine learning algorithms. Furthermore, simply having the word “Harvard” on your online certification adds credibility to your CV when you start applying for jobs.

University Programs

University programs demand a larger time (and monetary) commitment than purely online programs, though the upside is that you get a more prestigious qualification at the end. These two courses are ideal, with one even being a hybrid of online and university-level courses.

Course 1 – Master in Applied Data Science & AI (OPIT)

Let’s get the obvious out of the way first – you’ll need a BSc degree, or an equivalent, in a computer science or mathematical subject to take OPIT’s data science Master’s degree course.

Assuming you meet that prerequisite, this course comes in 18 and 12-month varieties, with the latter being a fast-tracked version that delivers the same content while asking you to dedicate more time to studying. It costs €6,500 to take, though early bird discounts are available, and an EU-accredited university delivers it.

The course eschews traditional exams by taking a progressive assessment approach to determine how well you’re absorbing the materials. It’s also focused on the practical side of things, with the application of data science in business problem-solving and communication being core modules.

Course 2 – MSc in Social Data Science (University of Oxford)

As the world’s leading university for seven consecutive years, according to Times Higher Education (THE) World University Rankings, the University of Oxford has outstanding credentials. And its MSc in Social Data Science is an interesting course to take because it specializes in a specific subject area – human behavior.

The degree stands on the precipice of an emerging field as it focuses on using data science to analyze, critique, and reevaluate existing social processes. It combines general machine learning models with more specialized data science tools, such as natural language processing and computer vision, to equip students with a high degree of technical knowledge.

That knowledge doesn’t come cheap, either in time or monetary commitment. The University of Oxford expects students to devote 40 hours per week to study, with overseas students having to pay £30,910 (approx. €35,795) to participate. While these investments are naturally intimidating, the university’s prestige makes the time and money you spend worthwhile when you start speaking to employers.

Factors to Consider When Choosing a Data Science Course

The four courses presented here each offer something different in terms of delivery and the expertise required of the student to participate. When choosing between them (and any other courses you find), you should consider the following questions:

• Does the course content and curriculum align with your career goals?
• Can you make time for the course within your schedule, and how much flexibility does it offer?
• Do the instructors provide the expertise you need and teach in a style that suits your preferred way of learning?
• Will you get an adequate return on your investment, both in terms of the prestige of the certification you receive and the knowledge you gain?
• Have past (or current) students recommended the course as a good option for prospective data scientists?

The Benefits of Completing a Data Science Course

Given the technical nature of the subject, you may be asking yourself what is data science course content going to deliver in terms of benefits to your life. The answers are as follows:

• Your skills improve your job prospects by putting you in pole position to enter a market that’s set for substantial growth over the next 10 years.
• The problem-solving and analytical tools you gain are useful in the data science field and other career paths.
• Any course you select puts you in contact with industry professionals who offer networking opportunities that could lead to a new job.
• You get to learn about (and experiment with) cutting-edge tools and technologies that will become the standard for modern business, and more, in the coming years.

What Is Data Science Course – It’s Your Route Into a Great Career

Let’s conclude by reiterating something mentioned at the start of the article – the data science sector will grow by 36% over the next decade or so.

That growth alone demonstrates the importance of data science, as well as why choosing the right course is so critical to your future success. With the right course, you make yourself a desirable candidate to organizations that are quickly accepting that they need data scientists to help them make decisions for the future.

Digital technologies pretty much run the modern world. From our phones and computers to manufacturing, finance, and retail, so many aspects of life rely on machines crunching unimaginable quantities of data.

As a discipline at the core of this digital era, data science is still expanding its scope. Leading organizations in this sector never seem to get enough of new talent, and the demand for data science specialists is constantly rising.

Luckily, the same digital-first environment that depends on data science also gives ample opportunities for learning this essential trade. You can easily find a data science course online, and the same goes for certifications. Better yet, there are Masters programs you can take without leaving your home.

If the prospect of online data science courses sounds exciting, this article will recommend some of the best available programs.

Top Data Science Online Courses

There’s no shortage of options to learn data science online. The courses that made our list come from prestigious institutions and offer the most comprehensive approach to the subject.

When choosing the top courses, we followed straightforward criteria. We looked into institution reputation, hands-on experiences, lecture quality, and comprehensiveness. Here are the best online data science courses that excelled in these categories.

Metis – Data Science & Analytics Training

If you’re looking for an online course with live lectures, then Data Science & Analytics Training from Metis will be a great choice. The lecturers come from leading tech companies, giving lessons that cover the complete data science process.

While there are advanced bootcamps on offer, Metis provides a comprehensive beginner data science online course with certificate, which lasts for six weeks. The price for this course is $750 (roughly 695 euros at time of writing). This course offers an accredited certificate.

Dataquest – Introduction to Python Programming

Dataquest is somewhat unique as it represents a knowledge repository for standalone learning or as a supplementary resource. If you want to learn data science with this platform, the Introduction to Python Programming course is a quality choice.

The class is brief, informative, and suited for beginners. It consists of six lessons and a practical project, with an estimated 12 hours needed to complete the self-paced course. While the introductory course doesn’t offer certification, it will open up a learning path with Dataquest that does end up in winning an expert-reviewed credential.

A third of the learning resources is available for free. The full access to Dataquest courses will require a subscription to the service with a monthly or yearly model.

Harvard University – CS109 Data Science

Getting education from Harvard is about as elite as one can get. The CS109 Data Science course embodies all the benefits of learning from a prestigious institution like Harvard. The course teaches data science essentials, including Python programming, statistics, and machine learning. The complete material is accessible on dedicated GitHub pages. You can clone the repository to get access to the entire curriculum.

Since this is just the repository of resources, going through them won’t give you a certificate. However, it’s free and completely available online, making it an educational opportunity you shouldn’t miss. With the detailed knowledge of the basics under your belt, you’ll progress to more complex (and pricier) courses with ease.

Online Data Science Master’s Programs

You might think that getting a master’s diploma requires you to physically attend a college. And while that used to be the case only a few decades ago, you can enroll in a master’s program online. Better yet, you may do so at a reputable institution with a world-leading data science department.

We picked several top-tier online data science masters programs online. Our choice was based on similar criteria as for the courses:

• How reputable is the institution?
• Does the program offer practical knowledge?
• Are the lectures comprehensive and quality-made?

With all that in mind, here are our top choices of online master’s programs in data science.

University of Aberdeen – Data Science MSc

The University of Aberdeen is one of the leading educational institutions in the UK. The Data Science MSc program is the university’s regular MSc data science online program that’s also completely available online. The curriculum includes vital skills concerning algorithms, data analysis, mathematical modeling, and more.

With full-time learning, the degree can be completed in one year. However, you can study at your own pace and take as much time as you need between individual courses. The limit for completion is six years, and enrolling in the program will cost £14,920.

Rome Business School – International Online Master in Data Science

The International Online Master in Data Science from the Rome Business School represents an excellent opportunity to learn, get in touch with industry-leading companies, and build a professional network. The school houses bootcamps across Europe and worldwide, which may increase your job market reach.

The participation fee for this program is €6,700. If paid after starting the course, applicants can split the cost into six installments, free of interest. Covering the fee in installments in advance will grant you a 5% discount. Paying in a lump sum comes with a 10% discount.

European Leadership University – Professional Master in Data Science & Leadership

The European Leadership University offers a comprehensive program that includes individual and group work, as well as interactive workshops. Completing the Professional Master in Data Science & Leadership program will earn you a master’s degree and two recognized certificates: in data science and leadership.

The program is priced at €5,000, with the option to pay the fee in five installments during the study period. Upfront payments come with a 10% discount. The program includes classes on machine learning, statistics, data collection and handling, Python programming, and more. This master’s course lasts for 19 months.

Key Skills to Learn in Data Science

Data science consists of numerous fields, some of which are more theoretical while other lean heavily towards practical applications. The later data science aspects include essential skills that you can use in the market:

• Programming languages
• Data visualization and reporting
• Machine learning and AI
• Big data
• Statistics

In programming, languages like Python, R, and SQL are used to create program environments and write specific commands. As a data science skill, the study of programming languages explores the limitations and possibilities of existing and new languages.

Data visualization deals with representing complex datasets in a more comprehensive way. It’s related to reporting and may be viewed as its subset. Visualization tools include charts, graphs, and presentations.

Machine learning might be the most well-known aspect of data science. Technologies like deep learning are at the core of AI development, enabling machines to learn from limited data input. Recently, great advances were made in unsupervised learning, which doesn’t require human input at all.

Big data refers to processing and analyzing large amounts of information. Handling massive data volumes presents specific challenges in terms of computational capacity and error reduction.

Finally, statistics form one of the cornerstones of practical data science use. Statistical analysis is helpful in business, demographics, and numerous social and natural sciences. Reliable statistics help researchers create predictive models and projections, allowing for efficient planning down the line.

Benefits of Earning a Data Science Certificate or Degree

Getting a degree or certificate in data science offers you an edge both in professional improvement and in the job market. The very process of gaining credentials is an opportunity to learn and practice essential skills. Plus, you can build a respectful portfolio along the way.

A degree or certificate means better job opportunities. Every reputable employer in the field will want to see recognized credentials from their applicants, and that’s particularly true when hiring for better-paid positions.

If you’ve already got a starting-level job in data science, credentials from reputable institutions will help advance your career. That kind of growth also creates a potential for better salaries and work benefits.

Finally, once you enroll in a data science degree or certificate program, you’ll meet other people pursuing similar interests. This will be an excellent opportunity for networking. Combined with the credentials, your new network of colleagues can help you advance even further.

Tips for Choosing the Right Data Science Online Course or Program

When you start searching for the right program online, it’s vital to consider several factors:

• The content and curriculum of the course
• Instructor expertise and reputation in the industry
• The duration of the program
• How flexible the course is
• Pricing and whether there are options for financial aid
• Testimonials or reviews from previous students

Besides these considerations, you should account for your personal preferences. Define your goals and what you want to achieve with the program. Also, it’s important for the program to match the learning style that suits you the best.

Gain the Essential Skills for the Hottest Profession Today

Our data science course suggestions include a selection of programs from the most respected industry leaders. With the high-quality courses on offer, all you’ll need to do is pick the program that matches your career goals.

Today’s job market has a high demand for data science experts. Getting certified or earning a degree in the field will help you start a career easier, which is why you should consider this important move as soon as possible.

Every time you’ve chatted with a bot on a website, you’ve seen the basis of artificial intelligence (AI) and machine learning (ML) in action. Your experiences with augmented reality, any prompts you’ve ever delivered to ChatGPT, and a host of other technologies that businesses are already leveraging show us how crucial these two fields are, both today and in the future.

AI and ML are taking over the world. And with the right AI & ML courses, you put yourself in the ideal position to forge a career in an industry that’s set for a continuous annual growth rate of 36.2% between 2023 and 2030.

Factors to Consider When Choosing an AI and ML Course

AI ML courses come in all shapes and sizes, with some delivering the basics you need to build a foundation in the subjects and others moving on from those foundational concepts and into specializations. These five things are your biggest considerations when choosing a course.

1 – Course Content and Curriculum

What does the course teach? That’s not just an important question in terms of figuring out if the course helps you develop the skills you need, but it’s a crucial one for your future career prospects. The curriculum informs every step you take on your learning journey. If the content isn’t up to scratch (or takes you in a different direction than the one you intend to go in) it’s not the course for you.

2 – Course Duration and Flexibility

Combine work and family with your personal life and existing educational commitments and you have the recipe for a quagmire of time-consuming tasks that may not fit with a long-term course. The best AI and ML courses online offer flexibility, allowing you to fit your studies around other commitments and opening the door to self-paced learning.

3 – Your Instructors

Imagine you walk into a classroom and your instructor introduces themself. They tell you they have a couple of qualifications in the fields of AI and ML, but they haven’t worked in either industry and can’t expose you to professionals who have. Is that any use to you in your career? A good instructor combines technical expertise (which they’ll need a lot of) with industry experience they can draw on to lead you down the right career path.

4 – Course Reviews and Ratings

As any online marketer will tell you, user-generated content in the form of reviews, social media posts, and simple ratings tells you a ton about what a product delivers. That’s as true for AI and machine learning courses as it is for anything else. Check out what other people have to say about the course, paying special attention to former students and what’s happened to them in the wake of earning their certification.

5 – Pricing and Affordability

Money is always a challenge when it comes to education. Some universities charge tens of thousands of euros for their courses, which is fine if you can commit time and money to a full-time educational experience. It’s not so fine if you’re working on a budget. Your course’s cost plays a huge role in determining whether you take it. Just remember one thing – people tend to get what they pay for (for better or worse).

Top AI and ML Online Courses

Machine learning and artificial intelligence courses run the gamut from fast, industry-led courses designed to get you into a job to deeper degrees designed to equip you with everything you need to advance in your career. The following four are some of the best AI and ML courses online.

Course 1 – Master in Applied Data Science & AI (OPIT)

Designed for those at the postgraduate level, this Master’s degree requires you to have a background in computer science (or a relevant alternative). It’s a 100% online course that delivers an accredited degree under the European Qualification Framework (EQF), with the course also counting toward the college credits you may need to apply for future courses. Tutors are available for direct learning 24/7 and you learn via both recorded and live content delivered over the web.

Key Features and Benefits

• Offers tons of exposure to how machine learning and artificial intelligence apply in real-world scenarios
• You get a Master’s degree from a fully-accredited institution
• Favors progressive assessments over high-stress exams
• Control your own learning by arranging the course’s modules around your schedule

Enrollment Details

OPIT’s Master in Applied Data Science & AI comes in two flavors – the regular 18-month variety and a fast-tracked 12-month course. Enrollment is annual, with intake occurring every October, and the price varies depending on when you apply. Early birds get an extensive discount, paying €4,950 compared to the regular price of €6,500. You’ll need a relevant Bachelor’s degree in a subject like computer science to apply.

Course 2 – Machine Learning Introduction for Everyone (IBM via Coursera)

If OPIT’s Master’s degree is for people who are already halfway through the metaphorical marathon of machine learning and AI, IBM’s beginner’s course is for those at the starting line. It’s a seven-hour course that teaches the basics of AI and ML, in addition to helping you get to grips with the development cycle for a machine learning model. As a primer for the concepts, it’s one of the best AI ML online courses available.

Key Features and Benefits

• Provided by a Fortune 50 company that’s one of the leaders in the AI field
• Created by a Senior Data Scientist who currently works for IBM
• You receive a sharable certificate that looks great on your LinkedIn profile
• No completion of other AI machine learning courses is required to apply

Enrollment Details

“Free” is always a nice price tag to see on anything, and that’s what you get with this course, at least when trialing the course. Enrolment is semi-regular, with batches of students accepted every few months, and you get to reset deadlines based on when you can complete its modules. IBM says the course contains seven hours of content. Your experience may vary depending on how quickly (or otherwise) you adapt to the content.

Course 3 – Post Graduate Program in AI and Machine Learning (Purdue University)

Career Karma ranks this as one of the best AI ML courses online, and it’s hard to argue given that this is a near-year-long course offered with backing from industry professionals at IBM. It’s more bootcamp than formal course, though, so expect to be put through your paces with intensive hackathons and sprints that cover a huge number of AI tools. Combine that with real-world projects (using datasets from companies like Twitter and Uber) and you have a fast-paced and valuable course.

Key Features and Benefits

• Any extremely modern curriculum that takes in real-world examples from tech industry giants
• Backed by IBM to further the real-world experience delivered
• You receive a postgraduate certificate from an established university
• The online bootcamp experience is great for people who prefer fast-paced and intensive learning

Enrollment Details

Enrollment is set for May of each year, with the course lasting for 11 months thereafter. You’ll need to hit some criteria to apply. The course asks for a minimum of a Bachelor’s degree where you’ve obtained at least 50% on your modules, as well as a couple of years of work experience. That work experience requirement may be an issue for people who haven’t started their careers. Still, it’s a cost-effective program, with the course costing £2,990 (approx. €3,400).

Course 4 – Machine Learning Crash Course (Google AI)

If time is of the essence and you just want a crash course in what machine learning is and how it applies to your business, Google provides the answer with this option. At just 15 hours, it’s a course you can complete over an intensive weekend of study. It’ll introduce you to some real-world case studies, with lectures coming directly from industry heads at Google.

Key Features and Benefits

• Contains 25 lessons (with 30 exercises) to expand and test your knowledge
• Get industry insight from Google experts who work in the AI and ML fields
• You don’t have to pay a euro to take part in this course
• Includes interactive visualizations of real-world models that are great for tinkerers

Enrollment Details

Google presumes no prior knowledge of machine learning in this course, though it recommends that you’re comfortable with programming in Python and understand complex statistical concepts. Knowledge of the NumPy library is especially helpful. Assuming you build up a knowledge base (Google offers other courses to cover these foundations), you can enroll at any time and get a free course that you can fit around your schedule.

Additional Resources for AI and ML Learning

Great AI ML courses can teach you the fundamentals and offer direct experience, ideally coming from professionals in the industry. But it’s what you do outside of your formal and certified studies that can make the biggest difference to your career prospects. These additional resources both supplement what you learn from the above courses and allow you to continue developing your skills once you have your shiny new certificate:

• Online forums and communities
• Podcasts and YouTube channels dedicated to machine learning and AI
• Books and eBooks
• Conferences, workshops, and career-centric bootcamps

Use AI & ML Courses Today to Benefit Tomorrow

Consider these facts if you need any more convincing that AI and machine learning courses are right for you. The average machine learning engineer earns between €66,585 and €118,169 per year, with jobs in AI easily climbing into the six-figure range as well. Your career prospects get a boost when you study AI and ML. But remember – a certification alone is not enough.

These are fast-evolving fields, and only those who dedicate themselves to continued learning (and the adaptation that comes with market changes) excel. Start your journey with one of the four courses in this article and then continue down the educational path.

It can often feel like a computer has a “brain,” especially given modern machines’ abilities to run complex calculations and handle instructions. But all of those machines need people behind them to program algorithms and help them to learn based on explicit instructions. That’s where machine learning comes in.

This branch of artificial intelligence brings a machine’s “brain” closer to the real thing than ever before. It’s all about teaching the machine how to do more than simply execute, as machine learning is all about making a machine “think” (based on instructions and algorithms) so it can improve over time. That ability to “think” is crucial in modern business because it gives companies the ability to analyze patterns – both operational and consumer-based – enabling them to make smarter decisions.

But these businesses need people who understand how to create machine learning models. That’s where you come in. With the right machine learning tutorial under your belt, you set yourself up for a career in a field that has only just started to show glimpses of its potential.

The Best Machine Learning Tutorials

Finding the best online tutorial for machine learning isn’t easy given the sheer volume of options available. Analyzing each one based on what it teaches (and how useful it will be to your career) takes time, though you can save yourself that time by checking out the three tutorials highlighted here.

Tutorial 1 – Intro to Machine Learning (Kaggle)

As tempting as it may be to run before you can walk, you need an introduction to the basic concepts of machine learning prior to focusing on more practical applications. Enter Kaggle’s machine learning tutorial. This seven-lesson course takes about three hours of self-guided learning to complete and will leave you with a solid grounding in machine learning that you can take into more industry-focused courses.

The majority of the seven lessons – barring the first – is split into two parts. First comes a tutorial where you’ll learn about the concepts that the lesson introduces, with the second part being an exercise that tests your new skills. Along the way, you’ll learn the basics of how machine learning models work and why you need them to explore large datasets. Other lessons focus on building and validating a model, with the later lessons introducing more complex algorithms, such as random forests, and giving you a chance to test your skills in competitions.

Though this is a beginner-focused tutorial, you’ll need a solid understanding of Python before making a start. Without experience in this programming language, you’ll feel like you’re truly lost in a random forest before you ever get to learn what that term actually means. On the plus side, the tutorial has an active discussion community (which includes the course instructor Dan Becker) that can help you along and point you in the direction of other courses that supplement this one.

Tutorial 2 – Making Developers Awesome at Machine Learning (Machine Learning Mastery)

This machine learning tutorial is less a structured course and more a series of articles and step-by-step instructional lessons that take you from the foundations of machine learning to more advanced concepts. That method of breaking the course into multiple stages is ideal for students of all experience levels. Complete beginners can start with the “Foundations” level and work their way up while those with more experience can dip into specific subjects that give them trouble or will build on their existing skills.

The course is split into four sections – Foundations, Beginner, Intermediate, and Advanced. At the Foundations level, you’ll learn about the statistical concepts and models that underpin machine learning, giving you a solid basis to move into the Python programming taught in the Beginner section. Once you have a grasp of Python, the Intermediate section teaches you about deep learning and how to code machine learning algorithms. By the time you hit the Advanced stage, you’ll be working on complex subjects like computer vision and natural language processing.

With its less structured nature, this tutorial is great for people who want to dip in and out and those who need to hone in on a specific aspect of machine learning. It’s also a good choice for beginners because it covers practically everything you’ll need to know. Unfortunately, the lack of structure means you don’t get an official certification from the tutorial. Some students may also not like the “hub” nature of the tutorial, as it links you to tons of different web pages that can lead to confusion over time.

Tutorial 3 – Machine Learning Crash Course With TensorFlow APIs (Google)

If you already have a mathematical foundation (as well as some basic understanding of machine learning), Google’s tutorial helps you take your skills to the next level. You’ll need to understand algebra, statistics, and basic trigonometry, in addition to having some understanding of Python, to get started. But assuming you have all of that, this machine learning tutorial exposes you to real-world examples of the technology in action.

It’s a 25-lesson course that contains 30 exercises covering topics like model development and testing, data representation, and building neural networks. According to Google, it takes about 15 hours of self-guided study to complete, though your time may vary depending on how much you already know before you start the course.

The biggest advantage of this tutorial is the name attached to it. Google is a major player in the tech industry and the presence of its name on your CV instantly shows employers that you know your stuff. The course material is also delivered by lecturers who work at or for Google, allowing them to bring their real-world experiences into their lessons. On the downside, the tutorial’s prerequisites make it unsuitable for beginners, though Google does offer more basic courses (both in machine learning and Python) to help you build the required foundation.

Factors to Consider When Choosing a Machine Learning Tutorial

The three options presented above all make a solid case for the best online tutorial for machine learning, though each offers something different based on your current skill level. To make the best choice between the three (and any other tutorials you find) you should consider these factors before committing yourself.

Your Current Skill Level

Diving into neural networks before you even know how machine learning works is like trying to row upstream without a paddle. You’re going to get stuck in rough waters and the end result won’t be what you want it to be. Be honest with yourself about your current skill level to ensure you don’t start a tutorial that’s too difficult (or too simple) for your abilities.

Programming Languages

There’s no getting away from the fact that you’ll need to feel comfortable with programming before taking a machine learning tutorial. Specifically, you’re likely to need some knowledge of Python, though how much depends on the course you take. Other languages can help, at least in the sense of ensuring you’re familiar with programming, but you need to check the language the course uses before starting.

Specific Topics

Though the basic idea of building a machine “brain” is simple enough to understand, the machine learning waters run deep. There are tons of topics and potential specializations you could study, and not all are useful for your intended career path. Check what the course covers and ensure those topics align with what you hope to achieve once you’ve completed the tutorial.

Time Commitment

If a tutorial takes an hour or two to complete, you don’t really need to worry about how you’ll fit it around your other commitments. But if it takes you down a machine learning rabbit hole (i.e., the Machine Learning Mastery Course), you need to get serious with scheduling. Figure out how much time you can commit to your course per week and choose a tutorial that fits around your commitments.

The Cost

On the plus side, many machine learning tutorials are available free of charge. But if you’re looking for more official certification, or you want to take a more formal course, you’ll usually have to pay for the privilege. Weigh up the course’s cost against the benefit you get out of the backend.

Tips for Getting the Most Out of a Machine Learning Tutorial

Anybody can start a machine learning tutorial, but only the truly committed will complete and actually get the most out of the materials. Follow these tips to ensure you’re spending your time wisely on the tutorial you choose:

• Set clear goals from the outset that define what you want to achieve with the tutorial and where it’s supposed to lead you.
• Dedicate time to learning every week because regularity is the key to making the information you absorb stick in your mind.
• Engage with any communities related to your tutorial to learn from your peers and ask questions about the tutorial’s content.
• Apply what you learn to real-world problems, either via the course itself or by searching for examples of what you’ve learned being put into action.
• Update your knowledge and skills regularly with further tutorials because what you learn today may be out of date tomorrow.

Find the Best Online Tutorial for Machine Learning for You

There is no single “best” machine learning tutorial on the web because each approaches the subject differently. Some assume you have no knowledge at all and will start with basics before moving you into deeper subjects. Others require you to understand the computing concepts (mathematical and programmatical) that underpin machine learning before you can get started. Understand what the course offers, and what it needs from you, before you get started.

Regardless of your choice, getting started is the most important thing you can do. Once you’ve chosen a tutorial, commit yourself to it fully to take your first step (or potentially a giant leap) into a career that’s only going to grow as machine learning models become more common in business.