Best Machine Learning Course

This guide is going to tell you everything you need to know about machine learning courses.

That’s not exaggeration – this thing goes DEEP.

You’re going to get all of this:

If you only want one of those things instead of the whole thing – click on it and enjoy.

Let’s get stuck in:

What Is Machine Learning?

machine learning course

Before you begin your machine learning journey, let’s take a look at what you’re getting yourself into.

Simply put, machine learning is:

The science of programming computers so that they learn on their own based on the data provided to them – Aurélien Géron, Founder of Kiwisoft

So instead of programming a computer to perform a specific task, you’re programming it to:

  1. Take in information
  2. Learn from that information using algorithms, and
  3. Provide a valuable output

Instead of strict programming instructions, you’ll enable machines to improve performance through iterations and then make data-driven predictions or decisions.

You’ll essentially be trying to get a machine to ‘think’ more like a human and less like a dumb robot.

That sounds a little worrying – computers that think more like humans. And that’s because it can be. Machine learning is a branch of the big, scary field of artificial intelligence (AI).

machine learning crash course

So please, please use your newfound knowledge and power wisely: coding creative solutions to real-life problems.

I could go into the various approaches to machine learning, like:

  • Neural networks
  • Unsupervised vs. supervised learning
  • Association
  • Clustering
  • Rule-based learning

And even deeper into the potential and actual applications of machine learning, like:

  • Brain-machine interfaces
  • Fraud detection
  • Machine medical diagnosis
  • Recommendation algorithms
  • Financial market analysis
  • User-behavior analytics
  • Machine translation

But these things are outside the scope of this guide. Although they are well and truly inside the scope of the courses I recommend as the best for machine learning.

So you know a little about what machine learning is. But why learn about it?

Why Study Machine Learning?

Machine learning is a field of the future. You should get into machine learning because it is:

  • Influential
  • Interesting
  • Future-proof
  • Lucrative


Machine learning is an integral part of your daily life. In fact, just a few minutes ago you Googled something and ended up here.

Part of the reason you got to this page is because of machine learning.

Google is constantly taking in billions of pieces of information and letting that data influence how they provide their services.

And it’s not just Google. Facebook, Instagram, Netflix, Apple, any large technology company, and, because of improving availability, a lot of smaller companies use machine learning too.

Think about how often machine learning impacts on your life and effectively shapes how you live it. It influences the:

  • Results you see in Google
  • Videos you watch on YouTube
  • Movies and TV shows you watch on Netflix
  • Photos you see on Instagram
  • Friends you interact with on Facebook
  • Ads served to you that affect your buying decisions

machine learning certification

All of these things combine to dramatically shift the information you have available to you. A great question is – how has machine learning already influenced who you are as a person?

It’s not easy to answer, but it is easy to see that machine learning impacts everyone’s life.

This field is important.

Your contributions to it will affect how people live their lives.

If you’re in the right place at the right time, they could even change the course of history.


You’ve probably had jobs in the past that were painfully boring. You’d show up every day and countdown the hours until home time and eventually the weekend.

A career in machine learning will always have you on your toes.

You’ll be intellectually engaged as you solve complicated problems in creative ways.

This is the coding equivalent of taking the path less traveled. It’s new, exciting, and you’ll be free to use your creativity in your work. There won’t be anyone reigning you in.


This rapidly growing engineering field will shape the future rather than be destroyed by automation and outsourcing.

In fact, according to a study by LinkedIn, the top two spots in the list of fastest growing jobs were both in the field of artificial intelligence. On top of that, there are 10 TIMES the number of machine learning engineers as there were just 5 years ago.

10x growth in 5 years – get on that train!

There is also already evidence that companies that are adopting machine learning as part of their culture are both more productive and profitable than their peers who are ignoring this field.

As a machine learning engineer you will be adding value from day 1. This makes you (1) harder to get rid of, and (2) more likely to get a share of profits 🙂

When you’re thinking about the future and how to best position yourself, where would you rather be than at the cutting edge of AI?


Last, but certainly not least, machine learning is lucrative.

prerequisites for machine learning

The average salary for a machine learning engineer is over $100k a year. That’s not bad.

Remember though that that’s the average. While researching for this guide I found many examples of engineers making 6-figures a month!

That’s definitely not representative of the norm – but it shows you that with these skills, the sky’s the limit. With a few months of hard work and study your earning potential could go through the roof (along with the number of job opportunities available to you).

So get studying so you can start making the big bucks!

BONUS MATERIAL: Machine Learning Datasets

When you’re finished the course you choose from the list below and you’re keen to put your knowledge to the test – you’ll want some data to digest.

machine learning online course

But usable and interesting datasets don’t just fall into your lap.

That’s where Kaggle comes in.

On this data science and machine learning platform you can find awesome datasets and discuss machine learning models with people in the industry.

Check it out:

How To Study Machine Learning?


This is how most (if not all) of the formal learning you’ve ever done has taken place.

Kindergarten > Elementary School > Middle School > High School > College

This is the status quo of education in the US (and around the world – although they call them different things).

But when you think about it… is this the best way to learn?

You probably remember disliking school at least a little:

  • The teaching methods were boring and outdated.
  • The content was often irrelevant.
  • You had to show up to a specific location, at specific times, for specific periods of time.
  • You couldn’t skip ahead if you were doing well.
  • You couldn’t slow down if you wanted to understand something better.
  • You couldn’t choose who taught you or make a call on if their methods suited your learning need.

The list of flaws to traditional education goes on and on.

But some people love learning this way. So I’m going to tell you about machine learning courses that are taught in-person:


best machine learning course online

Colleges are starting to realise machine learning’s real-world value. This has led to a change in their curriculum as machine learning is worked into most computer science programs these days.

It will either be included as a subtopic of a broader course or could be taught as a dedicated subject (which is obviously better for you).

Despite the prestige and respect associated with attending college, I don’t recommend this method.

For all of the reasons listed above (the criticisms of traditional education) and a couple of other reasons:

  • It’s not always possible to do standalone courses. Even if a college offers a course on machine learning, you might not be able to attend just that class. They might want you to sign up to a 4-year degree – no thanks!
  • College is becoming painfully expensive. It’s basically extortion at this point. It’s tough to recommend spending 6 figures on this kind of education when you can get the same knowledge for considerably less online.

Storytime: I know a guy who studied machine learning at a European university. His lecturer recommended the number 1 pick from this guide. He admitted that it was more detailed than the course he was teaching and told his students to take it. So even in-person education is turning to online education these days.

If you’re still convinced that college is the way to go, you can check the course list of your preferred institution to see if they cover machine learning.

If they do, give their admissions office a call and discuss taking the course and what the requirements would be.

Code Bootcamps

machine learning training

Code bootcamps have taken off since they began in 2011. It’s a rapidly growing section of the coding education space.

A ‘code bootcamp’ is basically a course that takes place is an intense training environment that aims to make you proficient in a specific area of computer science / coding in a short and specified period of time.

They are most-often delivered in-person at specific locations determined by the provider.

Once again all of the faults of traditional education come into play – specifically having to attend at specific times, for specific periods, in a location you don’t choose. Plus you can’t determine who the providers are near you and who they employ to teach you.

On top of this, these bootcamps get expensive. The industry average is over $10,000! Not many people have a spare $10k just sitting around waiting to drop on a short course.

But there are upsides to bootcamps.

You get to meet people pursuing the same thing you are. You meet qualified people who teach you. You’ll probably have to develop a project throughout the bootcamp (real development experience). And sometimes they include networking opportunities with companies who hire in the subject area.

Having said that, there are disaster stories out there about people not getting to present their project and not being allowed to network with potential employers. You’ll really have to do your research here.

This isn’t my recommended path either because of the advantages online learning has over in-person bootcamps.

But… if you like the idea, search for ‘machine learning bootcamps’ near you.

Do your research, read as many reviews as possible, and contact the provider to get statistics on students securing relevant roles after graduating. If they don’t want to give you those numbers… you’ll know why and you’ll also know not to spend money on that bootcamp.


best machine learning course

Online courses are the way of the future.

They have a ton of advantages over traditional in-person education. Here are just a few:

  • Inexpensive
  • Relevant
  • Up-to-date
  • Self-paced
  • Location independent
  • Provided by top universities and colleges
  • Offer certification, and
  • Taught by the top experts in a given field (if you know where to look)

Seriously, the instructors for the courses recommended below aren’t just ‘good’ at machine learning – they’re the best on the planet.

Outside of the ability to meet and connect with your peers and teachers, online courses wipe the floor with in-person education methods. Even then, most online courses offer the ability for you to interact with your peers online and ask questions of your instructors about the course material.

Online courses and online education more generally are altering the way the world learns.

In the not too distant future, assuming colleges and universities continue to increase their prices without increasing the quality of their product, tertiary education will undergo a revolutionary transformation and be based almost entirely online.

Stay ahead of the curve and get involved now.

Online Course Platforms

There are a number of online course platforms that offer hundreds of courses across any number of disciplines.

Coursera, Udemy, Skillshare, and SimpliLearn are a few examples.

There are pros and cons to each of these sites that won’t be discussed here. Just know that the courses recommended in this guide have been chosen accounting for these differences and you’ll only be exposed to the best.

One major benefit of enrolling in a course through one of these platforms is that the technology and delivery are incredibly reliable. These sites have robust teams in place to ensure students are being served effectively and any issues you have will be dealt with immediately.

Online courses can also be hosted independently by professional organisations or relevant experts in the field.

When this happens the technology involved can be less reliable as it’s likely not been developed in-house. But as mentioned, these factors have been taken into account for the recommended courses in this guide.

The Big Advantage

Before I move on to what you should be looking for in a machine learning course, let’s talk about one huge advantage online courses have over in-person learning.

They’re offered by the same prestigious educational institutions.

introduction to machine learning

The number one course for machine learning *spoiler alert* is run through Stanford University. Most people haven’t been to Stanford, don’t live near there or want to move there, and couldn’t afford it anyway.

But because of online courses you have the ability to take a Stanford University course and get certification for it that includes the Stanford University branding.

Seriously… it doesn’t get better than that.

What Makes A Good Machine Learning Course?

There are hundreds of machine learning courses available.

Choosing just one is a difficult task.

This is made easier if what makes a good machine learning course is clearly defined.

To find the best, this is what you should consider:

1) Quality Content

Content is the backbone of every course.

But not all course content is created equal.

learn machine learning

Boring text-based learning with outdated examples and simple summary quizzes won’t cut it. This isn’t a high school classroom or college campus. This is the competitive online learning space:

You want course content that piques your curiosity and engages you. You also want variety throughout a course. Formulaic content will become boring and stop you from learning as effectively as possible.

Great content will encourage rapid development and interest in machine learning beyond the scope of the course. You should look forward to your next lesson and shouldn’t be thinking that working through the material is a chore.

After all, this will just be a stepping stone towards further learning and maybe even a career in machine learning.

Quality course content will be delivered in a method relevant to the subject. For machine learning this could be:

  • Videos
  • Audio presentations
  • Interactive environments (e.g. coding sandboxes)
  • Creating real programs that solve real problems using real datasets

A course with fantastic content will be both suited to self-driven learners and comprehensive enough to be recommended by experts in machine learning.

2) Qualified Instructor

Online courses can be created by anyone with enough time and an internet connection. You don’t want to take a course created by someone who knows only slightly more than you do about machine learning.

You need to check out who created the course you’re looking at taking and what makes them qualified to teach it.

You’ve probably heard the saying, “Those who can’t do teach”?

To make sure this isn’t the case here, I have only recommended courses from instructors who are world-renowned machine learning experts.

Choosing from this list means you only get “Those who are famous for doing… teaching”.

Sometimes experts are incredibly knowledgeable but struggle to effectively pass that knowledge on. Teaching is a skill and not everyone has mastered it.

Once again, we’ve got you covered:

All of the courses we’ve recommended have rave reviews from students who have successfully graduated.

3) Appropriate Pacing

A quality course allocates time efficiently to effectively cover topics and complete required projects.

Ideally it sites in a Goldilocks range: not too long and not too short.

If you find yourself feeling bored or overwhelmed, it’s a sign the course you’re in isn’t paced properly. If you’re engaged the whole time – it’s just right.

Projects should be well-spaced throughout the course with sufficient time provided to complete larger tasks and without overwhelming you with smaller tasks in between.

4) Delivery Using Multimedia

Have you ever sat down and read a textbook on anything?


That’s why people take courses. But even then, traditional learning still involves a lot of dry delivery and reading.

Online courses are different.

Because of the medium through which they are delivered, online courses are able to integrate a range of multimedia and interactive elements to enhance your understanding of a topic.

best way to learn machine learning

Think videos, podcasts, interactive coding environments… anything to get you more engaged with the content.

People can go over the top and add things that don’t add to the course. An example (that I’ve unfortunately come across during my research) would be an hour-long webcam video of the instructor spewing knowledge in an unstructured manner.

That’s not helpful. And that course didn’t make the list of recommendations.

5) Self-Directed Learning

Online education targets people who actually want to learn about a topic (imagine if high school was like that!). This provides a lot of freedom for instructors when it comes to course and assignment structure.

Engaged learners are great learners and the best courses know this.

They create a learning framework around which you can take responsibility for what you want to learn and how you want to learn it. Obviously the content is solid and if self-direction isn’t your style you can still follow along.

But if you’re more of an intellectual free spirit, a quality course will give you the ability to complete assignments in your own style, explore topics of interest in greater depth, and make meaningful discoveries about machine learning.

To do this students aren’t micromanaged and solutions to assignments aren’t defined down to the smallest (often insignificant) detail.

6) Community

As mentioned earlier, meeting peers and instructors face-to-face is the one area in which traditional in-person education beats online education.

But things can be done to maximise the sense of community in an online course.

python for data science and machine learning

Great courses have forums in which students can discuss anything from potential solutions to relevant problems, debating the theories that underpin a subject, all the way to what they’re doing on the weekend.

Some courses even encourage group projects that require you to interact with other students to foster interaction and help you make connections that could help you in the future.

These things help everyone in a course become comfortable discussing ideas, making mistakes, and asking for help when needed.

7) Accounting For Different Learning Styles

According to popular theory there are seven learning styles:

  1. Visual Spatial
  2. Auditory / Musical
  3. Verbal Linguistic
  4. Physical Kinesthetic
  5. Logical Mathematical
  6. Social / Interpersonal
  7. Solitary / Intrapersonal

These styles indicate how different people most effectively process and retain information.

Courses can’t focus on one style as it would exclude too many others. As such, a great course will include a variety of multimedia and assignment structures to ensure you and your style are engaged as often as possible.

8) Intuitive Structure (Information Flow)

It’s not always easy for an expert to think like someone arriving to a subject on day 1. They’ve probably forgotten what they didn’t know and in which order they learned it.

This makes course structure really important.

The information presented should flow in a logical structure with each new discovery leading into the next. This helps you engage with the content and prevents you getting lost or disoriented in a new topic.

It’s best if a third-party reviews and approves the course structure to make sure it makes sense to fresh, uninitiated students. On top of this, course reviews will often tell an accurate story about what previous students thought about the structure.

9) Reliable Technology

online machine learning classes

Course platform

Using an existing course provider (like Coursera or Udemy) guarantees that the course will be up and available to participants 24/7 and if students do encounter issues they will be resolved quickly by the platform’s robust support teams.

If a course is hosted independently, steps should be taken to ensure technical support is available to students and any issues can be resolved promptly and easily.

I prefer recommending courses on established learning platforms as reliability issues are damaging to the education experience.

But if a course is too good to not recommend, I will ensure the technology and team supporting the course satisfy my requirements before it goes on the best list.

Additional tools

A course on machine learning is going to involve technologies people may not have been exposed to before. Coding environments, programming languages, and tools to make learning and developing easier.

The inclusion of these technologies should be well-thought-out.

Wherever possible they should be:

  • Free (or very cheap)
  • Full-supported
  • Reliable

This saves students wasting time troubleshooting programs they are new to and prevents people who can’t afford additional tools being excluded from the learning process.

10) Room for Additional Exploration

The resources (internal and external) provided in the course should provide self-directed and advanced students the opportunity to explore machine learning to a deeper extent and perhaps even beyond the scope of the course.

There shouldn’t be too many of these extras and they should be 100% optional.

Having too many and making them mandatory can overwhelm and confuse students. It should be clear that these are for people who want to stretch themselves and their learning.

How To Find The Best Machine Learning Course

The criteria of what makes a good machine learning course have been outlined.

There was a lot in there.

Now, let’s just focus on the most important ones and how they apply to the recommended courses. I’ll also discuss in detail the content a machine learning course should be covering.

Most Important Criteria

For a course to appear as one of the best machine learning courses online, it MUST have the following:

  • The majority of the content is about machine learning. Sometimes artificial intelligence and deep learning courses can touch on machine learning. This wasn’t good enough to get you on this list. Machine learning had to be the main focus of the course.
  • The instructor for the course should be highly qualified and have real world experience with complex machine learning technologies.
  • Must have had a lot of students go through the course and be highly reviewed and rated. This way you benefit from the positive (or negative) experiences of those before you.
  • The course has to be available immediately (on-demand and self-paced) or have regular intakes (at least once a month). You shouldn’t be waiting around to learn.
  • The course must include interactive elements on top of multimedia inclusion. Obviously video content is better than written content, but just having video tutorials isn’t good enough. You should be getting real experience through these courses.

The courses included here as the best meet these criteria and address the criteria mentioned above. I’ve put in the hard work so you won’t be disappointed with any of them.

Content Quality & Depth

Delivery is important. But if the content isn’t good, the course isn’t either.

To be included as one of the best, the machine learning courses I reviewed HAD to cover the following in detail:

  • Explain the steps (once again, in detail) required to successfully create and implement a machine learning project. These will usually form some variation of the following:
    • Get data and evaluate the problem
    • Explore the data (clean, prepare, and manipulate)
    • Train the model (including testing and deployment)
    • Test the data, and
    • Improve
  • There are a number of techniques (like regression, classification, clustering, etc.) and algorithms (like linear and logistic regression, decision trees, K-Nearest Neighbors (KNN), etc.) in machine learning. A quality course will discuss a number of these techniques and algorithms in detail. Courses recommended here were chosen because they were more extensive in both the number of included techniques and algorithms, and the depth to which they were explained.
  • Courses were more likely to be included if the instructor introduced students to common machine learning tools used widely within the industry. And if they taught using languages and libraries that are popular and industry-accepted (like Python / MATLAB). You want to finish the course being as familiar as possible with the tools of the trade.

Those are some strict criteria.

You’re only seeing the best of the best. So without further delay, here they are:

Best Machine Learning Course

Machine Learning from Stanford University

Platform: Coursera
Instructor: Andrew Ng
Rating: 4.9 / 5 (85K+ ratings)
Duration: 11 weeks (5-7 hours a week)
Certification: Yes (Paid)
Cost: Free (Certification Is Paid)

Originally published in 2011, Machine Learning from Stanford University is the course that put Coursera on the online education map. It’s the gold standard of machine learning courses.

Here’s the intro video of the course so Andrew Ng can tell you about the course himself:

There’s no question it’s the best… hands down.



This course builds up from the basics to more advanced concepts at a steady pace.

It talks about all things related to the machine learning process and gives you a better understanding of the underlying mechanism of machine learning and how it relates to the bigger AI picture.

The most relevant mathematical concepts are identified and their relationship to machine learning explained. For those of us who need it, there’s also a quick refresher on linear algebra to help you out.

Through consolidating theories and mathematical concepts related to each topic, this course manages to tackle a large number of techniques and algorithms in machine learning including:

  • Gradient descent
  • Linear and logistic regression
  • Support vector machines
  • Clustering
  • Anomaly detection
  • Recommender systems

There are also a couple of weeks that focus on deep learning and neural networks to extend the scope of the course a little.

You can apply the knowledge you’ve learned from this course immediately and use these concept on current projects.

You have lifetime access to the slides and videos if you ever need a refresher.


Each week is broken up into modules that consist of several video lectures (each lasting up to 15 minutes).

The material is complex and Andrew Ng does a fantastic job of simplifying the dense concepts making them far easier to understand.

One slight issue with the delivery of the content was volume. At times the audio was too quiet, sometimes even at full volume. If you can’t hear using your speakers, a set of headphones should fix the issue.

There are quizzes sprinkled here and there throughout the modules to help refresh and test your newfound knowledge. Fear not as all quizzes are multiple choice and you you get unlimited attempts. In total these account for ⅓ of the overall grade.

There are 8 programming projects throughout the course. This is where the other ⅔ of your grade comes from.

These projects usually require you to complete code segments for key functions in a program. The programming assignments can be finished in either MATLAB or its open-source counterpart, Octave.

The quizzes and coding projects relate directly to the content you’ve just covered, so it’s not difficult to work your way through the assignment even if the lecture material wasn’t perfectly clear. This allows you to solidify your understanding with real-world examples – a great way to learn.

At the end of the course you need at least 80% to receive certification. A score of 80% will mean you’ve understood A LOT of the content and have nearly all of the programs working perfectly (so 80% means you’ve crushed it).

Note that you get unlimited quiz retries and project resubmissions should you need them.


There are no specific prerequisites for the course. But some are implied based on the complexity of the content and the pacing of the course.

You should have some basic programming skills. Things will get real complicated real quick without them.

Linear algebra is a biggie. The projects require vector and matrix operations and summation notation is used during explanations. A bit of help is offered during the course but familiarity with these things will give you a headstart.

Same thing with calculus and statistics / probability theory. You might be able to get by, but if you come to the course with a decent understanding of it you’ll appreciate the course a lot more.

Don’t be too worried if you’ve never used Octave or MATLAB before. Learning enough to complete the assignments takes a couple of hours and this process will only serve to improve what you get out of the course.


Andrew Ng

Professor Andrew Ng of Stanford University created and presents this course.

Yes, that’s THE Andrew Ng. The guy who co-founded and led Google Brain and who was the Chief Scientist at Baidu. (In his spare time he also co-founded Coursera, the online learning platform on which the course is delivered.)

This is what I meant by recommending courses by the best machine learning experts on the planet. Seriously, try to imagine what the best instructor for a machine learning course would’ve accomplished in their life… then read they guy’s Wikipedia page. You couldn’t ask for a better instructor (and you couldn’t get one either!).

Andrew Ng knows his stuff but is also able to teach it. This course has amazing ratings and he has rave reviews. Here’s just one:

Andrew Ng is able to expose what many people would make complex in a clear and simple fashion.


This course is offered by Stanford University and taught by one of their Computer Science Professors who is a world-renowned expert.

Online courses don’t get more prestigious.

Stanford University is one of the best schools in the world. Look it up. Any list of the best universities globally will feature Stanford in its top 10.

When you complete the course you even have the option to receive certification which includes Stanford’s branding:

start learning machine learning


Coursera is one of the best online education platforms on the internet.

Andrew Ng was one of Coursera’s founders which means you get all of the bells and whistles the platform has to offer – I mean, the guy knows his own platform.

The course takes a new cohort every 2 weeks, so you won’t have to wait long to be accepted.

The course is quite long which allows for the in-depth content. It requires a bit of dedication to stick with it and finish the course considering you’ll need to put in 5-7 hours a week for 11 weeks straight.

The learning is self-paced to an extent, but if you want to stick to the timeframe you’ll have to put in the time to not fall behind. If you really want to work at your own pace, perhaps one of the other options might be better for you.

There is a discussion forum available to students which aims to bring a sense of community to the online learning experience.


Here’s the best bit: this prestigious, comprehensive course is FREE!

The premium version of the course allows you to graduate with certification (the actual certificate is pictured above). You can use this as evidence of completing the course in your resume / CV when you’re going for your next job as a machine learning engineer!

As mentioned, the certificate has Stanford’s branding on it so you get a lot of value for the <$100 price tag.


Andrew Ng’s Machine Learning from Stanford University is the bee’s knees.

It ticks all of the criteria and then some:

  • Focused purely on machine learning
  • (Over) Qualified instructor
  • Highly reviewed and rated after serving millions of students
  • Regular intakes
  • Quality content that engages

I’ll let a happy graduate sum it up:

I highly recommend this course for anyone getting started with machine learning. The only problem I see is that it sets the bar very high for other courses.

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

Platform: Udemy
Instructor: Kirill Eremenko
Rating: 4.5 / 5 (60K+ ratings)
Duration: 41 hours
Certification: Yes
Cost: $

Machine Learning A-Z is a fantastic course for beginners to:

  • Machine learning
  • Popular coding languages Python & R, and
  • Mathematical concepts behind machine learning

Here’s a short video of instructor Kirill Eremenko explaining what you can expect in the course:


Machine Learning A-Z does a great job of explaining the complete machine learning process and covers a lot of ground. You won’t be disappointed with the number of techniques and algorithms in this course – honestly, there are too many to count.

This course isn’t as math-heavy as the Stanford course which makes this complex field far more approachable to machine learning beginners.

Here are the major topics of the course:

  1. Data Preprocessing
  2. Regression
  3. Classification
  4. Clustering
  5. Association Rule Learning
  6. Reinforcement Learning
  7. Natural Language Processing
  8. Deep Learning
  9. Dimensionality Reduction
  10. Model Selection & Boosting

Each of the 10 sections begins with an explanation of the theory behind the concepts of the topic. These introductions are very beneficial they highlight real world applications of what’s being learned and provide a little motivational boost by emphasising what you will eventually be able to achieve.

The subsequent videos in each section walk you through the concepts and their implementation using both Python and R.

Every concept is explained once in Python and then independently in R.

If you’re more capable with one language you can alter your own path and take the course in just one language. If you want, you can then go back and tackle it again from a different perspective.

Assignments and quizzes are included throughout the course to reinforce and test your understanding of the concepts covered up to that point.


The course is delivered across 285 lectures and totals approximately 41 hours of content.

The on-demand video is professionally produced, which really helps to keep you engaged with the content. Although an issue with the number 1 pick, the sound quality of this course is excellent.

The content is broken down into digestible pieces with no lecture going for more than 20 minutes.

Machine Learning A-Z takes full advantage of the online learning platform by including downloadable code templates (for both languages) as a bonus. These resources give students a base on which to build their own projects during and after the course.


The course markets itself as appropriate for everyone interested in machine learning – from beginners to advanced.

But it’s more suited to beginners than advanced students.

This is obvious from the only significant prerequisite of the course – having ‘at least high school knowledge in math’.

Some of the datasets, programs, and explanations are a little basic and don’t go into a great amount of detail about the inner-workings or mathematics behind the machine learning magic.

It’s up to you whether this is good for you or not. If the Stanford course seems intimidating because of its requirements (which is understandable), this course is perfect if you’re starting from scratch. If you’re a little more advanced, the Stanford course is probably a better fit.

The instructors provide installation instructions for runtime environments for both Python and R. The integrated development environments (IDE) chosen for Machine Learning A-Z are Anaconda for Python and RStudio for R.


The two instructors of this course have an abundance of passion and experience in the machine learning and artificial intelligence space.

Kirill Eremenko
Kirill Eremenko

Kirill Eremenko is the founder and CEO of SuperDataScience – a prominent online educational portal for data scientists – and works as both a data science consultant and coach.

Kirill earned his analytics stripes with Deloitte before spending years across multiple industries implementing machine learning projects.

He has degrees in physics and mathematics, though his real passion is education and passing his knowledge on to the next generation of machine learning engineers.

Hadelin de Ponteves
Hadelin de Ponteves

Hadelin de Ponteves has a master’s degree in Engineering (specialising in Data Science) and significant experience in the industry including working with the AI team at Google to implement machine learning models for business analytics.

The guy only sleeps 3 hours a day (which he’s done for the last 3 years!) and has an insatiable hunger to educate people about machine learning.


This course is offered by the SuperDataScience team who are highly respected in the field for their ability to make the complexity of machine learning simple.

Having such a reputable company behind this course also means you’ll get great support and know that the content is up-to-date.


Udemy is one of the biggest and best online learning platforms. Its technology and reliability are second to none and, because of its size, does a great job of allowing instructors to implement creative and innovative teaching practices.

Any technical issues will be sorted quickly by Udemy’s robust support team.

Udemy provides on-demand courses so there’s no need to wait for an intake of students – you can start studying today!

The learning is 100% self-paced allowing you to take the course as quickly or slowly as you like. If you’re struggling with a topic you can linger there for a little longer to make sure you really get it.

There is a Q&A for each course which provides a lot of value and allows you to connect with other students. The instructors of the course and their team are very active in the Q&A and aim to respond to questions within 24 hours.


Udemy offers very competitive rates and are frequently offer steep discounts. This makes it hard to predict what price you’ll see on their site. However, it’s usually less than $20.

The value for money at that price is INSANE!

Lifetime access to 40+ hours of content, quizzes, assignments, downloadable code templates… all for less than 20 bucks.

Plus, there’s a 30-day money-back guarantee.

What have you got to lose?


Machine Learning A-Z is a fantastic course for machine learning beginners. The lack of complicated math might be disappointing for more advanced students, but if you’ve got no prior machine learning experience, it’s got everything you need:

  • Comprehensive machine learning content
  • Qualified and respected instructors
  • Well-reviewed and rated
  • On-demand

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Data Science and Machine Learning with Python – Hands On!

Platform: Udemy
Instructor: Frank Kane
Rating: 4.5 / 5 (11K+ ratings)
Duration: 12 hours
Certification: Yes
Cost: $

Data Science and Machine Learning with Python – Hands On! is the perfect course for people with little to no understanding of machine learning.

If you just want to dip your toes into the waters of machine learning – this is the course for you!

Here’s Frank Kane introducing himself and telling you a bit about the course:


Despite this being the ‘shortest’ of the courses on this list, it still covers a lot of ground in the 12 hours of lectures:

  1. Statistics and Probability Refresher, and Python Practise
  2. Predictive Models
  3. Machine Learning with Python
  4. Recommender Systems
  5. More Data Mining and Machine Learning Techniques
  6. Dealing with Real-World Data
  7. Apache Spark: Machine Learning on Big Data
  8. Experimental Design
  9. Deep Learning and Neural Networks

People with limited Python experience will be happy to see the course gives the Python-uninitiated a speedy introduction that’s machine learning-relevant. If you’ve got the required coding/scripting experience it should be easy to pick up as it’s a very intuitive language with very few syntactical requirements.

Frank Kane does a fantastic job of avoiding confusing mathematics and explaining the complex concepts in very easy-to-understand language (avoiding jargon). This makes the course suitable for beginners but will likely leave more advanced students wanting more.

Each topic includes a demonstration using code you can use to build your own projects and experiment with. There are also detailed notes provided that will help when you come back to the code to refresh your understanding.

As you can see a section of the course is dedicated to machine learning on big data using Apache Spark. This is a unique feature of this course as is it practically demonstrates how to scale your projects onto a computing cluster – making your knowledge from the course more powerful and giving you an edge when applying for jobs.


The 12 hours of on-demand video are broken into 91 lectures. 20 of these 91 lectures are available to preview before purchasing the course, so you’re able to get a good idea of quality and content before enrolling.

The longest of the lectures goes for about 15 minutes, most are shorter. This makes the topics easy-to-follow and makes finding information you want to revise very simple.

The production quality of both the video and audio is extremely professional.


Most people will easily meet the few requirements of the course:

  • First is having a desktop computer to run Enthought Canopy which is a scientific and analytic package for Python. It can be Windows, Mac, or Linux and the program isn’t too demanding.
  • Prior coding and scripting is ‘required’ although this course is aimed at beginners to machine learning.
  • Again, only high school level math skills are required which means the course is pretty light on the otherwise heavy math that goes on in machine learning.

The instructors provides installation instructions for all necessary software and libraries including Enthought Canopy, Apache Spark’s MLlib, and Matplotlib.


Frank Kane

Frank Kane spent a combined 9 years developing and managing the recommendation engines behind Amazon and IMDb! The work he did with those companies continues to influence the hundreds of millions of people every year.

That’s some serious real-world experience.

Frank has 17 patents in the fields of distributed computing, data mining, and machine learning. Since 2012 has run his own company, Sundog Software, which focuses on big data analysis education (like this course) and virtual reality technology.


As mentioned above, Udemy is one of the best online education platforms because of its technology, reliability, and great support.

Data Science and Machine Learning with Python is available on demand so you can enrol and start learning immediately. It’s self-paced and can be completed at your own leisure.

On top of the usual discussion area provided by Udemy, this course also has a specific Facebook group which allows for greater and more personal interaction between students completing the course.


Udemy prices their courses at the low end of the market – usually through significant discounting. For this reason I can’t tell you what price you’ll see on their site. What I will say is that you shouldn’t let their low prices fool you – the quality of the courses are not affected by the savings you get.

They offer a 30-day money-back guarantee on all courses so you can do the course and if you’re not happy with its content or quality, you can get your money refunded.


Frank Kane’s Data Science and Machine Learning with Python – Hands On! is an amazing course for people starting from scratch looking. It’s not the best option for people with a lot of experience or data analysis experience but is perfect for students who want to gain an initial understanding of machine learning using a common and easy-to-use programming language.

  • Comprehensive machine learning introduction
  • Qualified instructor with real-word experience
  • Great reviews and ratings
  • Available on-demand

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The Complete Machine Learning Course with Python

Platform: Udemy
Instructor: Anthony Ng & Rob Percival
Rating: 4.1 / 5 (800+ ratings)
Duration: 18.5 hours
Certification: Yes
Cost: $

The Complete Machine Learning Course with Python is another fantastic option for machine learning beginners. You can come to this course with no prior knowledge of machine learning or Python required. If you’re looking to learn from square 1, this is a great option.

If you’re interested, here’s a brief introduction to the course from the instructor Anthony Ng:


The machine learning topics covered in the course are:

  1. Regression
  2. Classification
  3. Support Vector Machine (SVM)
  4. Decision Trees
  5. Ensemble Machine Learning
  6. k-Nearest Neighbours (kNN)
  7. Dimensionality Reduction
  8. Unsupervised Learning: Clustering

There is a specific focus on the first topic – regression. A lot of detail is provided here and this topic in particular provides a lot of value to students.

It should be noted that the course doesn’t provide a lot of detail about the mathematical concepts of machine learning. This is to make the content more accessible for those new to the subject.

Although targeted to beginners, this course does aim to get you to a decent level of competency upon completion. It does this through focusing on project-based learning – getting you to learn while you do.

Throughout the course you will be required to complete 12 machine learning projects. Some are more interesting and practical than others, but you’ll get a lot out of each of them.

Examples include machine learning programs that:

  • Identify and classify flowers
  • Make predictions on house prices
  • Identify handwritten language
  • From a human resources perspective – identify staff who are more likely to leave
  • Conduct medical diagnosis (like detecting cancer cells)
  • Segment customers (identifying underserved and profitable clusters)


The 18.5 hours of on-demand video content is broken into 95 course lectures. These lectures are intentionally kept short to make the content easier to consume and to help you find specific sections when you come back to specific topics (taking advantage of your lifetime access).

To give you an idea the longest lecture is just over 22 minutes long – which isn’t that long at all.

20 of the 95 lectures (and 5 of the 18.5 hours) are on Regression. This allows for an indepth look at this important machine learning technique and its underlying concepts.

The course was created in partnership with the team at Codestars. They only produce quality content. You can rest assured the video and audio quality is top notch and that the course takes advantage of all of the platform’s bells and whistles.

All of the datasets and source code used in the course is available for students to download.


There are no prerequisites for this course.

They specifically say in the course introduction that ‘no prior machine learning or Python experience is required’. But, as you’d expect, having a little bit of experience in either will help you get through the course a little quicker and easier.

The course provides installation instructions for all relevant applications including Jupyter (IPython) notebook, Spyder, Matplotlib, and Seaborn.


Anthony Ng
Anthony Ng

Anthony Ng has been a lecturer at Nanyang Polytechnic in Singapore since 2010. In 2018 he became the Head of Banking and Finance at this institution. In this role he teaches algorithmic trading, financial data analysis, banking, finance, investment and portfolio management.

A Financial Engineer by trade, Anthony holds a double masters degree in science (financial engineering – MFE) and business administration (MBA).

As a lecturer and online course instructor, he has a true passion for education. On top of this work he also helps Boston-based hedge fund Quantopian run Algorithmic Trading Workshops in Singapore.

Rob Percival
Rob Percival

Rob Percival has a bachelors in Mathematics from Cambridge University which he put to good use as a teacher before he began creating well-structured, super interactive, and easy-to-understand courses on complicated topics.

When he realised he didn’t have the time and expertise to create courses on all of the topics he wanted to cover, Rob created Codestars which is leading an online education revolution to change the way people learn to code.

Rob is a passionate educator who is focused on making online education mainstream and helping as many people as possible in the process.


Again, Udemy is a platform I’m more than happy to recommend because of its reliability, support, and technological innovations.

The course is available on-demand and completely self-paced. This allows you to learn in your spare time based on your schedule.

There is a Q&A area for the course full of helpful advice from people at your level. As there is no expectation of programming or Python experience the friendly support staff are more than happy to answer even the most basic of questions for people new to this area.


Udemy is a bit of a mixed-bag price-wise as they are always having sales on different courses. It’s hard to say what price you’ll see when you view this course although it’s likely to be less than $20.

This is amazing value considering the breadth and depth of the content and its high production quality. Understand that the low prices on Udemy are viable based on the volume of students who go through each course. They are not expensive but this doesn’t mean they lack value or quality – quite the opposite is true.

Udemy offers a 30-day money-back guarantee on all courses so you’ve got nothing to lose by giving this course a shot.


The Complete Machine Learning Course with Python is a great first step into the world of machine learning. It won’t push the limits of experienced students, but it is perfect for users with absolutely no experience in coding or machine learning:

  • Beginner’s machine learning content
  • Qualified instructors
  • Positively reviewed and rated
  • On-demand

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Machine Learning Certification Course

Platform: Simplilearn
Instructor: Mike Tamir & Vivek Singhal
Rating: 4 / 5 (500+ ratings)
Duration: 36 hours
Certification: Yes
Cost: $$$$$

The Machine Learning Certification Course available on Simplilearn aims to get professionals in the technology/analytics industry prepared to take on a new role in machine engineering.

As such, this course has been prepared and is presented to a very high standard and goes into great detail to ensure graduates are job-ready upon certification.

Here is a brief video to give you an idea of the course objectives:


The in-depth course content is delivered across 8 core lessons:

  1. Introduction to Artificial Intelligence and Machine Learning
  2. Techniques of Machine Learning
  3. Data Preprocessing
  4. Math Refresher
  5. Regression
  6. Classification
  7. Unsupervised learning – Clustering
  8. Introduction to Deep Learning

Throughout the course you will be exposed to support vector machines (SVM), kernel SVM, naive Bayes, decision tree classifiers, random forest classifiers, logistic regression, K-nearest neighbours, K-means clustering and more.

During the training you are required to complete 4 projects:

  1. Californian Housing Price Predictor Using US Census Data
  2. Phishing Detector Website Using Logistic Regression Algorithms
  3. Phishing Detector Website Using k-Nearest Neighbors Algorithm
  4. Handwritten Digit Recognition Using MNIST Database

In total, these projects include 28 exercises and 17 machine learning algorithms.


Simplilearn has a reputation for producing high-quality content that is professionally produced and this course is no exception.

There are two delivery options for individuals: Self-Paced Learning & Flexi-Pass:

Self-Paced Learning

180-day access to the course – all video and downloadable resources.

You get immediate access to all video and downloadable resources and are given 180 days to make your way through the course.

If you’re giving it your all, this should take about 45-50 hours – so the 180 days is plenty of time.

To receive Simplilearn certification using this delivery method you must complete 85% of the course and complete one project.

Online Classroom Flexi-Pass
On top of the 180-day access provided by the Self-Paced Learning stream, the Flexi-Pass gets you 90-day access to attend 5+ instructor-led online training classes.

These are LIVE classes taught by the course instructors (who have 15+ years of industry experience) in which you can learn in greater depth by asking questions of your instructor.

To receive Simplilearn certification using this delivery method you must attend one complete batch of the live sessions and complete one project.


Despite the target market for the course being analytics industry professionals, the prerequisites are quite basic:

  • Familiarity with Python fundamentals
  • Basic high school mathematics
  • Basic statistics understanding

If you have the mathematics & statistics down but aren’t sure about Python, Simplilearn has you covered. They give all students enrolled in this course free access to their beginners Python course too.


Mike Tamir
Mike Tamir

Mike Tamir is a machine learning influencer with some serious credentials:

The only guy more qualified for a machine learning course might be Stanford’s Andrew Ng – but even then it’s close.

Vivek Singhal
Vivek Singhal

Vivek Singhal the Co-Founder & Chief Data Scientist at CellStrat, India’s leading Artificial Intelligence and Deep Learning consultancy firm.

He has significant experience in the technology space from a startup investor and advisor through to roles with global giants like IBM and AT&T.

He holds a Bachelor of Engineering from the Indian Institute of Technology Roorkee and an MBA from Georgia State University.


Simplilearn is a leader in online training and certification. They focus on quality over quantity, hosting 400+ courses designed and updated by 2000+ respected industry experts in areas like cyber security, cloud computing, project management, digital marketing, and data science.

They differentiate themselves by effectively blending pre-recorded multimedia lectures with live online training from experienced instructors – a value-add that cannot be matched by many of their competitors.

They frequently partner global training organizations to make their certifications count as official accreditation within professional communities.

They offer robust support (24/7 teaching assistance) and their platform is one of the best online.

To bring a sense of community to their offerings they include ‘SimpliTalk’ which allows course participants to discuss relevant topics and make meaningful connections with their peers.


This course is expensive.

It can be because it targets professionals looking to upskill to a new and lucrative role in machine learning. Plus, the course is incredibly well-done and effective.

If you’re not in this position, definitely opt for one of the other courses on this list.

If you are, here it is:

  • Self-Paced Learning path – $499
  • Flexi-Pass – $799

It’s not cheap, but the course provides a lot of value and is somewhat exclusive in comparison to other courses on this list. Instead of hundreds of thousands of students, this course has had less than a thousand students.

If you opt for the Flexi-Pass delivery and attend the live lectures you’re going to get very specific lessons tailored to your learning needs – something the other courses on this list can’t even come close to providing.


Simplilearn’s Machine Learning Certification Course is a great option for professionals looking to upskill and increase their salary by moving to a new position in machine learning.

  • Detailed and high-quality machine learning content
  • Qualified and respected instructors
  • Positively reviewed and rated
  • Available on-demand (with frequent live sessions for Flexi-Pass students)

Enrol Now

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