6 Best Online Machine Learning Courses in 2023

When it comes to rapidly expand industries, machine learning is probably one of the most exciting out there right now.

This particular field of computing and technology has seen the most expansion in the past 10 to 15 years, with the slow but inevitable role that chatbots, ad serving, ai spam filtering, and many other fields that machine learning has been rooting itself in our day-to-day lives.

In this article, we will show you the best online machine learning courses you should complete in 2022 and

Plus, with their continued application in understanding mathematical models and pattern recognition.

With such an expansion like this of unprecedented scale, there hasn’t been a better time to start working in this field.

As machine learning continues to open up opportunities in virtually every sector, the skills needed to program and understand it are only going to become even higher in demand than they are now.

However, to do that, you’re going to need to learn about machine learning for yourself.

And with a subject as technical as this, you’re going to need the right resource to get started.

Fortunately, with the growth of machine learning has come a new wave of teaching courses that can help you learn the ropes of this exciting new field.

And we’re going to show you some of the best of them that you can find out there, right now!

Machine Learning (On Coursera)

Starting this list strong, we have a great course for people who would consider themselves beginners to many aspects of machine learning.

This course on machine learning was created and is taught by Andrew Ng, a Stanford professor who is an expert in the field of artificial intelligence, as well as co-founder of Google Brain, as well as the co-founder of the online learning service Coursera (get used to that name). We’ll be seeing it a lot on this list.

One of the features that will likely stick out to many people with some familiarity in this field is that the course assignments for open-source programming given by the course are done in Octave.

While this may be somewhat frustrating for people who are more experienced with programs such as Python or R, this is one of the best ways that newcomers can get to grips with the ins and outs of machine learning.

This course is often considered the benchmark to match or beat when it comes to machine learning courses, so you know that it probably has a lot of things going in its favor.

Overall, with a great course that has well-rounded content in pretty much every aspect that a newcomer to machine learning might want to know, this course should be your first port of call when it comes to machine learning courses.

Pros

• This course is taught by an industry professional, with both academic and real-world experience on the subject.
• Perfect course for newcomers, with a balanced amount of content for each part of the course.
• Octave as the main open-source programming tool is great for newbies.

Cons

• Some of the assigned coursework and optional lab studies could be a little unengaging for some people.

Machine Learning A-Z (On Udemy)

If you’re interested in learning about the algorithms that go into machine learning, then Machine Learning A to Z is probably the course for you.

In terms of learning the many processes that go into programming machine learning, this course is probably one of the most thorough out there.

With over 10 separate sections of this course, from the baby steps of data processing, all the way up to language processing and fine-tuning the processes of machine learning, this course has lessons and assignments for virtually every step of the machine learning process.

There’s a reason that they’ve named this course A to Z, and not A to C, after all!

What’s more, the instructors behind this course, Kirill Eremenko, and Hadelin de Ponteves are experts in the field of data science, meaning that you’ll be in safe hands while learning in this course.

Plus, the open-source programming assignments and lessons take place in Python and R, meaning that the skills that you’ll be learning in this course can be used in industry-standard programs that are used in a variety of sectors.

This is probably one of the best Udemy courses on machine learning out there right now.

So, make sure to keep an eye out for this one when it is on sale!

Pros

• Course instructors are responsive, and can clearly explain high-level concepts in an accessible way.
• Python and R are used across sectors, so you’ll be learning to use a professional program from the start.
• The course takes you through many steps and phases that go into machine learning, giving you all you need to know.

Cons

• The course does require some previous experience or knowledge of higher-level mathematics, some of which many customers may not have interacted with since high school.

Machine Learning Foundation (On Class Central)

Over to Class Central, we go for this next entry in our list, with a course entry from the University of Washington.

As with many of the best courses in this field, you will be in safe hands when it comes to mentors and instructors.

Emily Fox and Carlos Guestrin, both professors of statistics and machine learning systems respectively, are experts in their respective fields.

You’ll find that both professors use their experience and knowledge to better help you understand the machine learning systems that you’ll be getting to grips with.

However, what sets this course apart from others isn’t the who, but how you’ll be learning the ropes of machine learning.

While many other courses take a more abstract approach to machine learning that gives you the mathematical tools first to help understand the coding used in machine learning, Machine Learning Foundation’s course does the reverse.

By going through the case studies provided in this course, your learning material will allow you to see these equations and processes in action first, which can then give you a tangible understanding of how these equations work in real time.

As a result, this is a foundational course that is excellent for people that learn best through getting stuck into the real-world uses and getting thrown into the metaphorical deep end, rather than understanding the academics and theory first.

Both are legitimate ways of understanding machine learning, but it’s great to see that there are courses on this theory-heavy subject that can accommodate tactile learners too.

Plus, with this course being available in over 10 different languages, from English to Arabic to Chinese to Korean, this is an option that is great for people across the world to learn.

Pros

• The course is taught in a variety of different languages
• In a case study-based course, lats you learn through practical experience over theory.
• Experienced professors are there to help you at every step.

Cons

• While you will learn these case studies through Python, you won’t be programming original coding much.

Machine Learning Crash Course (On Google AI)

Going over to Google AI education for our next entry in this guide, we have their multimedia Machine Learning Crash Course.

Considering that Google is one of the foremost leading companies when it comes to research, having their knowledge in a course that is open to everyone is an opportunity that you aren’t going to want to pass up, especially if you are at all interested in understanding machine learning.

With a variety of different types of materials that you can access in this course, from articles on the subject to 15 hours worth of content, and 25 lessons, as well as learning from real-world case studies, there are plenty of different ways and paths for you to get to grips with machine learning here.

Not only that, but this course is free to access for everyone, meaning that this is the perfect course to get started with if you are on a tight budget (or no budget at all, for that matter)!

So, if you are looking to get a firm grasp of the basics in this exciting new field, and don’t know where to start, We would recommend checking out Google’s crash course.

After all, with a price tag at zero, you’ve got nothing to lose!

Pros

• Plenty of course material to get you started on machine learning.
• The course comes from one of the leading companies in machine learning.
• This course is free to try!

Cons

• You do not receive a certificate of completion upon finishing the course.

Intro To Machine Learning (On Udacity)

When it comes to machine learning, we’ve already brought up the fact that many people interested in the subject will get to grips with it in different ways.

Some prefer practical experience, while others need a firm grasp of the theory first before they can start to shine.

This is where a course like Intro To Machine Learning can come in handy, as they have plenty of learning material that can cover both of these paths to understanding for beginners.

Plus, with the learning timeline for this course lasting only a week, this is a program that won’t bog down your time and routine with a course that just won’t end.

By the time you are finished with Intro To Machine Learning, you will have a firm grasp of the basics of this field that will help satisfy your curiosity or work and as an excellent base for you to learn the more complicated aspects of machine courses in more intense learning courses.

Plus, with the price tag on this course currently sitting at zero, there’s nothing to lose with you take a look at this course for yourself!

So what are you waiting for? Click on the link, and see if this beginner’s course is for you!

Pros

• Plenty of options for getting to grips with the course content here, for both theoretical and practical students.
• The course is short and sweet, not taking more than a week for most students to complete.
• This course is taught by industry experts
• The course is also free to try for anyone!

Cons

• This course is geared towards beginners, so people looking for more in-depth and intermediate content should probably look elsewhere.

Machine Learning For All (On Coursera)

Back to Coursera, we go for this entry, where the University of London has contributed its learning cause on getting to grips with machine learning.

Through this course, you’ll be learning about real-world applications that use machine learning, such as the world-beating Go program, AlphaGo.

It’s content like this in the course that allows you to appreciate what machine learning is great for, as well as how you can set up your one learning project for open-source machine learning.

Plus, as the course progresses, you’ll also start to take on more projects that revolve around collecting the data that you’ll need to help you understand the theories that underpin how machine learning works.

Accessibility is the main aim of this course, allowing many people who have little to no prior knowledge of programming and higher mathematics to get to grips with a brand-new field of technology.

In short, this course is intended to be a gateway to allow more people to appreciate this field, as well as help students, decide on whether or not they want to continue learning and working in this aspect of computer science.

Pros

• One of the most approachable academic courses that will help you through the beginner learning phase of machine learning courses.
• The instructor on this course teaches computer science at the University of London, so you’ll have an active expert teaching you.
• The mixture of lectures and reading make this a great course for theory-minded students.

Cons

• The more practical aspects won’t be accessible until later in the course.

Features To Look For In A Machine Learning Course

So, with a variety of courses shown here, you have plenty of options open to you.

However, with so many active experts in the field lending their own experience to the learning and education on this subject, there are plenty more excellent courses where these came from.

So, if you want to keep searching for a course that is more tailored to your needs, keeping the following features in mind should help you navigate through your searches.

Accessibility

Arguably, with such a new subject and field as machine learning, this is one of the most important aspects that you can look for in a course on this particular kind of computer science.

Having a course that will ease you into this mathematics-heavy field will be a priority for anyone unfamiliar with machine learning.

Most of the courses we have provided come from experts in the field, so they will have plenty of information that will help you understand machine learning.

However, if some are geared towards a wider market of potential students who have next to no experience may leave those at a more intermediate level wanting better instructions and tasks to complete in their own time, and vice versa.

Most courses that are advertised will usually also state what their intended skill level or prerequisites are before signing up, so keep your eyes peeled for that when searching.

Skill Level

Tied to the point of accessibility, you will also need to take your experience level into account when deciding on a course for you.

If, for example, you are someone that wouldn’t consider themselves academically minded, or only has a limited understanding of higher mathematics, you should probably narrow your searches to beginner-level courses, or at the very least, courses that will cover the fundamentals of machine learning in detail.

On the flip side, if you are experienced in either of these fields, you may want to consider searching only for courses that cover the finer and higher-level details of machine learning, rather than wasting time and resources going back over the basics.

Previous Experience

Similar to the point of skill level, many of the intermediate courses that we have covered here will often require you to have plenty of experience with higher mathematics.

Or at the very least, a comprehensive understanding of high-school-level math.

Course Length

A course length will often dictate how much course content you are getting.

Longer courses tend to span a much wider range of experience levels and a more accommodating for a wider audience, but will also usually be more expensive.

Shorter courses will often be a great way to get a grip on the essentials and can cost little to no money for you.

However, a lot of experts or intermediate skill level students will be left wanting.

Frequently Asked Questions

What Is Machine Learning?

We probably should’ve explained this first!

Machine learning refers to how artificial intelligence’s algorithms can learn through the data that is imputed into them.

It’s the technology behind finance forecasts, as well as self-driving cars.

How In-Demand Are Machine Learning Skills?

So, how useful exactly is this knowledge in the job market?

Well, considering that anywhere a fifth up to half of the positions in many tech companies will ask for ‘machine learning’ as part of their required skills, we’d say that it’s pretty vital here.

Especially when considering how big the tech industry is projected to get shortly!

Final Thoughts

So, with all the options open to you, e wish you the best on your machine learning journey!