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

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

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

The answer is through programming languages.

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

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

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

Factors to Consider When Choosing a Python for ML Course

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

Course Content and Curriculum

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

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

Instructor’s Expertise and Experience

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

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

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

Course Duration and Flexibility

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

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

Hands-On Projects and Real-World Applications

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

Course Reviews and Ratings

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

Pricing and Value for Money

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

Top Python for ML Courses Reviewed

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

Python for Machine Learning

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

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

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

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

Machine Learning A-Z: AI, Python & R

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

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

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

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

Machine Learning With Python by IBM

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

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

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

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

The Complete Machine Learning Course With Python

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

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

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

Programming for Everybody (Getting Started With Python)

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

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

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

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

Additional Resources for Learning Python for Machine Learning

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

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

The Path to Python

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

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

If we think of “computer science” as an umbrella term for so many aspects of computing today, its importance is immediately apparent. Artificial intelligence (and the programming that lies behind it) falls into the computer science category. The same goes for machine learning, data science, networking, cybersecurity, and so many other elements of what make modern computing technology tick.

You need a solid grounding in computer science – both general concepts and theory – to move into one of these areas of specialization. And if you need to get that grounding on a budget, these free computer science courses teach you what you need to know and come with a handy certification.

Top Free Certified Computer Science Online Courses

As surprising as it may seem, you don’t have to pay money to get an education in computer science that employers actually care about. Free courses exist. And many of these free online computer science courses deliver a certification that proves your knowledge and comes from an institution that employers respect.

Course 1 – CS50: Introduction to Computer Science (Harvard University)

We’re stretching the definition of “free and certified” with the first course on the list. Though it’s free to take (and you get an audit of your performance without paying a penny), the verified certification for Harvard’s CS50 course costs $189 (approx. €175).

Assuming you’re willing to part with the cash, this course gives you a certificate from one of the United States’ most respected institutions, in addition to a crash course in computer science fundamentals. Over 11 weeks of self-paced learning (you’ll need to commit at least 10 hours per week to the course) you’ll develop a fundamental understanding of computer science and the programming that underpins it.

Concepts covered include data structures, abstraction, web development, and algorithms, creating a course that melds the math of modern computing with the theoretical concepts you’ll apply in the real world. Prospective programmers enjoy some diversity, too, as the course teaches the basics of several languages. Python, C, JavaScript, and HTML are all covered, though not in enough detail for you to achieve mastery in any of them. Still, as online certified courses for computer science go, CS50 delivers a prestigious certificate and exposes you to ambitious peers who may offer networking potential beyond the course content.

Course 2 – CS50’s Computer Science for Business Professionals (Harvard University)

It’s hard to look beyond Harvard when it comes to free computer science courses because you’re getting education and certification from a top university. With CS50 Computer Science for Business Professionals, Harvard moves beyond the tech-centric approach of its usual CS50 course to demonstrate how computer science principles apply in a real-world setting.

It’s a short course, clocking in at six weeks of study and only requiring two-to-six hours of work per week. That makes it perfect for professionals who want to boost their knowledge without a full-time commitment. You’ll tackle more high-level concepts in computer science, including the fundamentals of cloud computing and how to build technology stacks. All of which makes this like a speed run through of what you need to know about computing on a business level.

That’s not to say you won’t learn any technical theory. Several programming languages are covered (albeit in short-form style), as are the basics of computational thinking. But like CS50 above, certification comes at a cost, even if the course itself is free. Paying for an optional upgrade with EDX (through which the course is offered) is the only way to nab your certificate, if you do get a free course audit to demonstrate completion regardless.

Course 3 – Introduction to Computer Science and Programming Using Python (Massachusetts Institute of Technology)

Offered in conjunction with the EDX platform, this computer science online course takes a Python-focused approach to its teaching. Unlike CS50, which covers a wide range of topics in brief, MIT’s course focuses on how computer science is like a tool that you can use to create software and algorithms. Python 3.5 is the technology behind that tool and you’ll learn how to use it by examining and analyzing real-world problems.

The nine-week course starts by demonstrating the basics of Python (some self-learning and expansion of these concepts may be required) before moving into algorithms. Once you’ve gotten to grips with basic algorithm creation, you’ll learn how to test what you create and how those algorithms become the building blocks of complex data structures.

You have to make a substantial time commitment with this course, with MIT requiring you to spend at least 14 hours per week on your studies if you wish to stick to the nine-week schedule. And though effective in teaching you the basics of Python, the course is really a primer for a second MIT course – Introduction to Computational Thinking and Data Science – that requires payment. But it’s a useful course as a standalone product, but you’ll have to pay a fee to EDX if you want a course-centric certificate.

Factors to Consider When Choosing a Free Certified Computer Science Online Course

The trio of free online computer science courses discussed above each offer something different. Depending on your choice, you’ll get a bottom-up crash course in the theory, a practical understanding of how computer science works in a business context, or an in-depth guide to using Python. But when choosing between the three courses above (or any other courses you find) you must consider the following factors.

The Course Content and Its Relevance to Your Goals

The big question here is – what do you want to achieve with the course?

Sure, having a certificate, especially one with a major university’s name on it, is nice. But if that certificate demonstrates that you’ve learned skills that you don’t need for your intended career path then it’s not worth the paper it’s printed on.

Think of choosing a course like making an investment on which you expect a return. Outline your goals – both learning-centric and career-based – for taking the course. Then, find a course that helps you to reach those goals through laser-focused learning on topics you’ll use in the future.

Course Duration and Flexibility

For a young learner without full-time work or family commitments, taking on a computer science online course that requires months of study may not be a big deal. But that’s not the case for everybody. If you have limited hours available during the week, you need a course that you can fit into those hours rather than one that forces you to fit your life around the course.

Thankfully, most free online computer science courses make allowances for schedule flexibility by taking a self-paced learning approach. You’ll get access to all of the course resources upfront, allowing you to choose when you study. You may be able to get ahead during one week in preparation for a week where you know you can’t commit as much time, giving you the flexibility you need to fit the course into your schedule.

The Instructors and Their Expertise

Would you want to learn the theory of how to pilot a plane from somebody who’s never been up in the air? Of course you wouldn’t, and you must adopt the same attitude when choosing a computer science course.

Check the faculty list associated with the course (most reputable courses tell you who created them) and dig into their individual credentials. What have they done in the computer science industry? Where did they learn what they know? The answers to these questions tell you if your instructors and, by extension, your course are credible.

The Value of the Certification

When it comes to certification, look beyond the website that offers the course and instead focus on the institution that created it. For example, CS50’s Computer Science for Business Professionals is offered via the EDX platform, which doesn’t mean much to potential employers. But that certificate comes with a stamp of approval from Harvard University, which is a school that’s going to immediately raise eyebrows if it’s on your CV.

The point is that reputation matters, though it’s the reputation of the course creator that matters above that of the course platform. The more prestigious the name on the piece of paper, the more valuable the certificate is in the eyes of employers.

Tips for Successfully Completing a Free Certified Computer Science Online Course

With the tips for sifting through the sands of free computer science courses established, let’s round things off with some quick tips that’ll help you succeed in your studies:

• Set clear goals for your education from the outset, with those goals aligning with your current experience level and desired outcomes.
• Create a study schedule that fits around your commitments and stick to it as closely as you can.
• Don’t skip assignments or practical sessions because everything included in the course is there to teach you something valuable.
• Engage with the course community both to get advice from your peers and to potentially create networking opportunities.
• Dedicate time to revision and research when preparing for exams or practical assessments to ensure you fully understand the course content.

Get Certified for Free and Improve Your Job Prospects

Given the importance of computer science to modern business – even the simplest of companies use software and have networks – it’s reasonable to want to build your knowledge of the subject. Free online computer science courses allow you to do that in exchange for a time commitment, with many allowing you to inject some flexibility into your study schedule.

Explore the three courses highlighted here, and look beyond them to more specialized courses once you’re confident in the foundational knowledge you’ve built. And remember – even a certificate from a free course has value in the job market if that course was created by a recognized institution.

As a well-known programming language, Python dominates the data science field. Its prominence in the industry represents the main reason why so many job offers include Python skills as a hard requirement.

Of course, all of the hype around Python has practical ramifications. This programming language is suitable for people without a programming background. If you have a sufficient grasp of technology, chances are you’ll get how Python works in a few weeks.

Besides being beginner-friendly, Python is practically built for math and statistical analysis. Plus, data visualization becomes nearly effortless when you use specific Python libraries dedicated to the task.

The point is that Python makes numerous data science tasks and operations easier. If you’re interested in data science, learning this versatile programming language will take your professional development to a new level.

Fortunately, you can find plenty of courses teaching everything from the basics to advanced functions in Python. Let’s look at the best Python data science tutorial and course options.

Factors to Consider When Choosing a Python Data Science Course

Before you start a particular course, it would be best to consider the specifics. The criteria that should guide your decision include:

• The content of the course: Some courses will be introductory, while others will offer advanced lessons. You should start with a course that aligns with your proficiency level.
• Instructor’s expertise: Ideally, you’ll want an industry expert to teach you about Python. Experienced lecturers or proven professionals will know all of the ins and outs, and they’ll be able to transfer that knowledge to you.
• Course duration and flexibility: If you’re looking for a course, you don’t want an experience that will last an entire year. On the other hand, you shouldn’t expect too much from an hour-long course. Additionally, the course structure should be flexible enough to allow you to complete it at your own pace.
• Practical projects and applications: Python is a living programming language that sees plenty of use in the real world. On that note, the course you take should offer a hands-on experience and show you how to apply your new knowledge in practice.
• Course reviews and ratings: Although this shouldn’t be your primary clue when making a decision, taking a look at what others say about the course certainly won’t hurt. You’ll want to stay away from courses with mostly negative reviews, especially if the reviewers make unsubstantiated claims.
• Pricing and value: Course pricing may vary from ludicrously expensive to free. While our list doesn’t include any outrageously overpriced courses, you’ll find a quality free one in there. The bottom line here is straightforward: Does the course fit in with your budget and what do you get for the price?

Top Python Data Science Courses and Tutorials

ILX Group – Python E-Learning

This Python data science course deals with the basic functionality of the programming language and teaches you how to apply it in practice. It contains in-depth information about command running, dictionaries, methods, and shell scripting. No final exam is necessary to complete the course.

Key Topics

• The basics of Python programming
• File and data operations
• Logging and test infrastructure
• Conditional statements
• Networking
• Shell scripting
• Django web framework

Instructor’s Background

Information about the instructor for this course isn’t available on ILX Group.

Course Duration and Format

The course is in e-learning format and is delivered entirely online. It will take you about eight hours to complete. Instead of a final exam, you’ll complete the course by submitting the required project that must meet specific set criteria.

Pricing and Enrollment

Enrolling in this course will cost €450 +VAT. You won’t need to fulfill any additional requirements to make a start. Paying the one-time fee will grant you a full year of access to the course resources.

Pros

• Provides a solid foundation for Python programming
• No limitations on enrollment or availability
• Offers practical knowledge and projects

Cons

• E-learning tools used throughout the course aren’t defined
• No information about the instructor or their credentials

Python Institute – Data Analysis Essentials With Python

The Python Institute is a group devoted to Python education. The Data Analysis Essentials with Python is only one of the courses this institution provides. It’s an intermediate-level program focused on data analysis using the tools within the Python programming language.

Key Topics

• Data analysis
• Algorithmic and analytical thinking
• Data visualization
• Statistics
• Data mining and modeling
• Programming
• Data-based decision-making

Instructor’s Background

No instructor information can be found on the Python Institute site regarding this particular course. However, it’s worth mentioning that the institute is run by industry experts with substantial experience in the IT sector. These experts are also responsible for the institute courses.

Course Duration and Format

The Data Analysis Essentials with Python course will last for up to six weeks, provided you devote about eight hours weekly to studying the material. The course is delivered online.

Pricing and Enrollment

One of the greatest advantages of this course is its pricing: Data Analysis Essentials with Python is completely free. However, this course isn’t for beginners. You’ll need previous knowledge of the key concepts in Python programming. The Python Institute recommends completing their beginner courses or coming into this program with some experience.

Pros

• Course designed by industry professionals
• Free for all users
• May serve as a preparatory course for Python Certified Associate in Data Analytics (PCAD) certification

Cons

• No information about the lecturer
• Exact delivery methods aren’t specified

Python-Course – Fundamental Python Course

The Fundamental Python Course is designed as a comprehensive introduction to programming methods in Python. The course will take you through the fundamentals of the programming language and include practical solutions in the Python environment.

Key Topics

• Python introductory lessons
• Script editing and execution
• Working in the Python shell
• Expressions, operators, assignments, and variables
• Dictionaries, stacks, loops, and lists
• Handling files and exceptions
• Conditional statements
• Packages and modules

Instructor’s Background

The instructor for live courses is Bernd Klein. A Python expert with a Saarland University diploma in Computer Science, specializing in computer languages, Klein has taught at the Saarland University, EWH, Koblenz, and the University of Freiburg, where he still holds a teaching position.

Klein is also the founder of the programming language teaching platform, Bodenseo.

Course Duration and Format

The course lasts for five days and includes a live class format. While Klein usually holds classes in person, courses are currently provided online. To participate on this course, you’ll need a network-ready computer with a microphone. No additional software is needed.

Pricing and Enrollment

The on-site variant of the course costs €1,450 per day, while open classes start from €349 daily. There are no other requirements for the course.

Pros

• Taught by an experienced lecturer
• Offers a complete coverage of Python-related subjects
• Advanced optional topics

Cons

• Very pricey compared to the competitors
• Doesn’t provide a certificate

Additional Resources for Mastering Python Data Science

If you want an alternative to an actual Python data science course, you may wish to turn to other resources that will help you master the subject. In particular, these would be books and digital resources like forums, eBooks, podcasts, YouTube channels, websites, and blogs.

For some of the best Python forums and online communities, check out the following:

• Python org forums
• StackOverflow Python forum page
• FreeCodeCamp Python category

Great books on Python include:

• Head-First Python, by Paul Barry
• Think Python, by Allen B. Downey
• Learn Python 3 the Hard Way, by Zed A. Shaw
• Python Crash Course, by Eric Matthes

If printed media isn’t your style, you can find an excellent list of free Python eBooks on Codeburst.io.

On the other hand, you might not want to read too much while learning Python. In that case, you’ll be glad to learn that there are numerous podcasts on the subject that you can tune in to right now:

• The Real Python Podcast
• Django Riffs
• Talk Python to Me
• Teaching Python
• The Python Podcast

Unsurprisingly, YouTube also has plenty of Python data science course and tutorial channels. Here are our top picks:

• The New Boston
• Sentdex
• Real Python
• PyCon – This isn’t a particular YouTube channel, but rather a search query. Browse the search results on YouTube, and you’ll find videos for Python-dedicated conferences from around the world.
• Michael Kennedy

Finally, there’s an abundance of blogs and websites dedicated to Python resources and knowledge:

• Python Blogs
• The PyCharm Blog on Jet Brains
• The Invent with Python Blog
• The Python Library Blog
• Finxster

Learn to Program in Python Like a Pro

The internet is full of quality Python data science tutorial and course pages. You can find free and premium resources to hone your skills in the programming language or get familiar with the fundamental concepts.

Whichever resource type you choose, rest assured that learning practical Python skills will be a valuable addition to your resume. After all, data science is a constantly developing field in which expanding your knowledge base and skillset can only be a huge plus. If you’ve found a program you like in this article, don’t hesitate to jump right into it and expand your horizons.

Imagine that you own a business that has thousands of customers. You have data on every one of these customers, ranging from basic contact information to data about their purchasing habits. What you have is a huge dataset, and you want to extract information from that dataset in the form of patterns and insights with which you can make decisions.

You’d need a data scientist.

Data scientists specialize in shining a spotlight on the most important insights found in large datasets. They use a range of tools – from complex algorithms to artificial intelligence – to make that spotlight shine brighter. And in a world of Big Data, the data scientist’s role is more important now than ever. With these six courses, split between beginner, intermediate, and advanced levels, you put yourself in a prime position to become the data scientist that so many companies need.

Best Data Science Tutorials for Beginners

Everybody has to start somewhere, and these data science beginner tutorial options are the ideal first step on your journey into the field.

Data Science Tutorial for Beginners (Java T Point)

If you’re looking for a succinct explanation of what data science is, what it involves, and how it applies in the modern world, Java T Point’s tutorial answers the key questions. It’s structured as a long-form article rather than a set of modules or lessons, but it’s well-organized and covers all of the key points in enough depth to make it a handy primer for the data science novice.

This data science tutorial covers a range of topics, from basic explanations of the components of data science to descriptions of the types of jobs available for those who enter the field. It also digs into some of the machine learning aspects of data science, such as decision trees, so you can see how AI ties into modern data science practices.

Granted, the fact that it’s not a traditional course means there’s no community underpinning the tutorial or certification for completion. But as a primer that gives you some foundational knowledge, it’s a superb starting point.

Data Science Full Course – Learn Data Science in 10 Hours (Edureka)

Offered via YouTube, this data science tutorial makes the lofty claim of being able to teach you all you need to know about the subject in 10 hours. While that isn’t strictly true (the more complex aspects are covered superficially), it’s still a great primer for those looking to build a solid foundation in the subject.

The tutorial is a great choice for visual learners, and it covers topics like data categorization, statistics, and the data lifecycle. Charts, graphs, and other visual learning tools abound, with the constant narration helping you to understand what you’re seeing on screen.

As a full 10-hour video, the tutorial could do with being broken up into separate lessons to make it easier to keep your place. But as long as you’re happy to record time stamps (or don’t mind the full 10 hours in one sitting), the course delivers plenty of useful information.

Best Data Science Tutorials for Intermediate Learners

After completing a few of the best data science tutorials for beginners, you’re ready to get your feet wet with intermediate courses that dig into the coding that underpins data science.

Data Science with Python Tutorial (Geeksforgeeks)

Python is the programming language of choice for data scientists, as evidenced by the fact that 69% of data scientists report using Python daily. It’s no surprise, either, as Python is an extremely flexible language that’s ideal for creating the algorithms needed in data science due to its vast range of libraries. The challenge you face is twofold – figuring out how to code in Python and understanding what libraries you need to confront common data science challenges.

Geeksforgeeks offers a data science tutorial that confronts both of those challenges and helps you see how Python applies to the data science field in a practical sense. Starting with a brief introduction to the data science field (the beginner-level tutorials in this list offer more depth), it then dives into everything you need to know about Python. You’ll learn about the basics of Python, such as functions and control statements, before moving into how you can use the language for visualizing data and creating machine learning models.

It’s a highly specialized tutorial, though it’s one that’s essential for prospective data scientists, given the popularity of Python in the field. Unfortunately, there’s no certification for completion. However, it’ll equip you with so much Python knowledge that you can feel confident moving into a more advanced study without worrying about your coding chops.

Data Science and Machine Learning Essentials (Microsoft via Udemy)

Like the above course, Microsoft’s offering covers Python, albeit in far less depth. However, it stands out because it also covers a couple of other languages used commonly in data science – namely R and Azure Machine Learning. As a result, the course is an excellent choice for intermediate data scientists who want to get to grips with the main three programming languages they’ll likely use in the field.

It’s a five-week course, with Microsoft recommending between three and four hours of learning per week, and it’s delivered in English. Each weekly module is capped with a quiz that tests your knowledge. The modules cover everything from data science basics to creating machine learning models in Azure Machine Learning.

Of course, the biggest benefit of this course (aside from the content) is the Microsoft-approved certification you get at the end. Any employer who sees Microsoft on your CV will sit up and take notice. Still, you’ll need to build on what you learn here with a more advanced data science tutorial, ideally one that covers more real-world applications of working with data.

Best Data Science Tutorials for Advanced Learners

Once you’re secure in your foundational knowledge and you have a good idea of how to apply data science practices, you’re ready to step into a more advanced data science tutorial. Here are two options.

Data Science Tutorial – Learn Data Science From Scratch (DataFlair)

Think of DataFlair’s main data science tutorial page as a hub world in a video game. There are dozens of different directions in which to take your studying, and you’re in complete control of where you go and what you learn. The page hosts over 370 tutorials (free of charge) that cover everything from the basics of data science to using data mining and Python to parse through massive data sets.

The sheer depth of coverage makes this set of tutorials ideal for the advanced learner. The more basic sides of the course can fill in any knowledge gaps that weren’t covered in previous tutorials you’ve taken. And on the more advanced side, you’ll be exposed to real-world examples that show you how to apply your theoretical knowledge in a practical environment. There’s even a set of quizzes that you can use to test your understanding of what you read.

There are some drawbacks, namely that this data science tutorial doesn’t offer a certificate and is less interactive than many paid courses. However, self-paced learners who thrive when presented with pages of theoretical knowledge will find almost everything they need to know about data science in this collection.

MicroMasters® Program in Statistics and Data Science (Massachusetts Institute of Technology)

By the time you’re at the advanced stage of learning data science, you’ll probably want an official certification to take pride of place on your CV. This mini-Master’s degree comes from the Massachusetts Institute of Technology (MIT), which is one of the world’s leading technology and engineering schools.

The course lasts for one year and two months, with between 10 and 14 hours of study required per week, making it a choice only for those who can commit to a part-time consistent learning schedule. It’s also not a free data science tutorial, as you’ll have to pay £1,210 (approx. €1,401) for the program.

If you can vault those hurdles, you get a graduate-level course that teaches you how to develop the machine learning models used in modern data science. Plus, having the letters “MIT” on your course certification (and the networking opportunities that come with learning from some of the institutions leading professors) makes this course even more valuable.

Find the Best Data Science Tutorials for Your Skill Level

Whether you’re taking your first tentative steps into the world of data science or you’re an advanced learner looking to brush up your skills, there’s a data science tutorial out there for you. The six highlighted in this article represent the best data science tutorials available (two for each skill level) on the web.

Let’s close by answering a key question – why complete one of these tutorials?

Precedence Research has the answer, stating that the data science field will enjoy a compound annual growth rate (CAGR) of 16.43% between 2022 and 2030. Rapid growth means more job opportunities (and higher salaries) for those with data science skills. Use these tutorials to build your skill base before shifting your career focus to a field that looks set to explode as Big Data becomes more crucial to how companies operate.

Did you know that machines can learn, too, similarly to humans?

In machine learning, software applications can be trained to parse data, learn from it, and then make informed decisions based on their findings. This outcome prediction has proven to be invaluable in numerous industries, including IT (malware threat detection), healthcare (disease diagnosis and prognosis), manufacturing (business process automation), and finance (fraud detection).

The importance of machine learning in today’s technology-driven world can’t be understated. So, if you’re considering a career in data science, software engineering, or artificial intelligence (AI), this is the skill to learn.

Fortunately, learning this skill is now accessible to almost anyone. Just go online and find a machine learning course for beginners.

We’ve gathered our three favorites to help you narrow your search (and avoid wasting time on subpar courses). We aim to make it easy to select the perfect free machine learning course and crush it online.

Criteria for Selecting the Top Beginner-Friendly Online Picks

The internet offers seemingly endless learning resources. This is undoubtedly great news, as it levels the playing field for eager learners worldwide. But be careful; not all online resources will be valuable to you. Some will just waste your time.

So, how can you comb through the sea of content and find a course worth pursuing? By knowing precisely what you’re looking for, of course. Check out our selection criteria to track down a great online course.

Course Content and Structure

Most courses you find online will come with a description. The more detailed it is, the better. By carefully reading the description, you’ll better understand what the course covers and how it is structured.

These descriptions can sometimes read fluffy to get as many learners to apply. But try to look past the buzzwords and extract only the essential information – what the syllabus looks like, how many hours it takes to complete the course, and how the lessons are spaced.

If there are video lessons, check previews to ensure you’ll only work with high-quality video and audio outputs throughout the course.

Instructor Expertise and Teaching Style

If the course’s content is sound, it’s time to move on to the person (or people) who will present it to you. After all, anyone can read a bunch of words from a book. It takes an experienced and knowledgeable instructor to help you truly understand the learning material.

So, before signing up for the course, do a little research on the instructor. Check out their bio to learn about their expertise and experience in the field.

Beyond that, play a lecture or two to ensure their teaching style suits you. Having issues with the little things like their voice or body language can impact your concentration and, in turn, your success.

Platform Features and User Experience

Now that we’ve covered what you’re learning and who you’re learning it from, the only question is where the learning will take place.

Take a more in-depth look at the platform hosting your chosen course. Ensure it offers a seamless user experience, as glitches and downtime aren’t exactly ideal for a learning environment.

Also, the more exciting features the platform has, the easier it will be to stick to the course. Different learning material formats, interactive elements, discussion forums, and progress tracking are just some of the features that can significantly improve your learning experience.

Community Support and Resources

The lack of personal interaction in online learning can make you feel like you’re all alone. This can be incredibly challenging if you’re struggling with a lesson or a part of the course. So, when looking for the perfect online class, ensure you’ll get a chance to interact with other learners or even experts in the field.

Asking questions, sharing insights, collecting feedback, and receiving support and motivation should be a part of every learner’s journey.

Cost and Accessibility

If your chosen course checks all your boxes, don’t celebrate just yet. First, check whether you can access it and how much it costs.

Access can sometimes be limited by your country or device, so make sure nothing stands between you and learning online.

As for the cost, you’ll find plenty of high-quality courses free of charge. If there is a fee to pay, check whether you can purchase just the individual class or you have to subscribe to the platform. The latter approach is better for those who want to acquire multiple skills and work on their education long-term.

Top Beginner-Friendly Online Picks for Free Machine Learning Courses

Here are the top three beginner-friendly machine learning courses we’ve chosen based on the selection criteria above. Each one should help you learn the fundamentals of this field and how to use machine learning effectively as a skill.

Supervised Machine Learning: Regression and Classification by Andrew Ng

If you want to learn more about machine learning, why not consult one of its leading figures? That’s what you can do if you take this course. You’ll learn from Andrew Ng, a prominent computer scientist and a pioneer in machine learning and AI. All things considered, it’s no wonder this is probably the most popular free machine learning course online.

During this course, you’ll master the key concepts of machine learning (supervised and unsupervised learning and best practices) and learn how to apply them in practice. Some of the skills you’ll gain include:

• Linear regression
• Logistic regression for classification
• Gradient descent
• Regularization to avoid overfitting

This is one of the best beginner courses for entering the machine learning field. It offers abundant knowledge, a flexible schedule, and resettable deadlines. The only downside is that you must enroll in the entire specialization to receive a certificate upon completion.

Machine Learning Crash Course by Google

Google is a major disruptor in the AI industry. So, a free machine learning course offered by this tech giant is seriously a big deal. As the name suggests, this is a crash course, so expect a fast-paced and intense approach to machine learning.

Throughout 25 lessons, you’ll learn about specific machine-learning areas through video lectures from Google researchers, real-world case studies, written guides, and hands-on exercises.

The key topics this course covers include:

• A deep dive into neural networks
• The inner workings of gradient descent
• Model training and evaluation
• The importance of loss functions

The course is relatively short (15 hours) yet informative, so it can be an excellent choice for those pursuing machine learning while having a job. However, if you’re an absolute beginner, you’ll have to do some reading before starting the course, which some may view as a downside.

Practical Machine Learning With Scikit-Learn by Adam Eubanks

If you’re looking for something even shorter than Google’s Crash Course, you’ll love this course on Udemy. You’ll learn the most powerful machine-learning algorithms in a little over an hour. This course focuses on Scikit-Learn, a Python machine-learning library ideal for beginners.

Here are some of the algorithms this course covers:

• Linear regression
• Polynomial regression
• Multiple linear regression
• Logistic regression
• Support vector machines
• Decision trees

This is the perfect course for kick-starting your machine-learning journey. However, some learners might find it too limited in scope. Also, the course lacks interaction with the instructor, which might be a deal breaker for some learners.

Tips for Success in Learning Machine Learning Online

Imagine going through all the trouble of finding the perfect machine learning free online course, only to abandon it halfway through. There’s no judgment here, of course. We know how difficult it can be to persevere with learning outside the traditional classroom and school system.

So, here are some tips to help you complete a machine learning course for beginners triumphantly:

• Set clear goals and expectations. Before starting the course, remind yourself of why you’re doing it and how it fits your career development. Don’t just buy the course for the sake of buying it; these impulse purchases rarely translate to success.
• Dedicate consistent time for learning. Like with many things in life, consistency is key. But this time, there’s no one to keep you on track besides yourself. So, work on your self-discipline and commit to regular study sessions.
• Engage with the community and seek help when needed. Online learning can feel like an isolating experience. But it doesn’t have to, provided you’ve selected the right platform. If you ever feel stuck, don’t hesitate to seek help from the community. These simple interactions will help you stay motivated and focused.
• Apply learned concepts to real-world projects. As soon as you gain a fundamental understanding of machine learning, try to put this knowledge to practice. Seeing how the theory you’re learning sets you up for success is a great incentive to keep learning.
• Continuously update skills and knowledge. Are you done with the machine learning course for beginners? Great, it’s time to look for a more advanced one. Continuously learning and improving your skills is the only way to stay on top.

Considerable Knowledge at No Cost

You won’t make a mistake regardless of whether you put your trust in Andrew Ng or Adam Eubanks or go the Google route. What you will do is gain valuable knowledge about an even more valuable skill: machine learning.

If you want to master your knowledge of machine learning, consider pursuing a Bachelor’s degree in Modern Computer Science from the Open Institute of Technology. The syllabus includes two courses focusing on machine learning and numerous others that will skyrocket your career opportunities.

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.