Best Machine Learning Certifications to Level up in 2021

Best Machine Learning Certifications to Level up in 2021

Learning how to teach machines how to learn is not as convoluted as it sounds. Machine learning started off as a discipline that was shrouded in mystery, but really it just builds on mathematical and algorithmic techniques that have been around for decades. We’ll show you, in our list of the best machine learning certifications in 2021, that you can learn the subject yourself by investing some time and effort in the process.

But let’s pump the brakes a little bit before that. There are a lot of misconceptions when it comes to machine learning and some of its related disciplines. Let’s clear the air on those, so you know what you’re getting into when you set about on your machine learning journey.

An Orientation to Machine Learning Certifications

Machine learning is the science of teaching computers to learn things on their own. We do that by feeding them with a lot of data and helping them find patterns in it.

A good way to understand machine learning is to look at how regular programming works. When you write a program on your computer, you tell the computer what to do using a simple algorithm. The computer follows that algorithm and provides you with output based on that.

Machine learning turns that process on its head by giving computers the ability to act on datasets without explicitly being told what to do. The realization that this was possible was revolutionary in the field of computer science.

For more of a deep-dive on machine learning, its application, and the distinction between machine learning, artificial intelligence, and deep learning – skip over to our extra credit section.

For now, and before we dive into our course reviews, here’s what you’ll need to consider when choosing a course.

Factors to Consider When Picking a Machine Learning Certification

Here are a few things to check before you decide on a machine learning certification.

Prerequisites

Machine learning is a discipline that builds on knowledge in data processing and mathematics. Because of that, every course will require some basic knowledge in those things.

If you’re just starting out, then it helps to know some math, especially statistics, linear algebra, and some calculus. Having strong mathematical foundations isn’t necessary, but it does help accelerate your learning.

The next thing that’ll help is some programming experience. It doesn’t matter what language, as long as you know how to implement code and work with algorithms. So before you choose a course, even if it’s for beginners, take some time to take stock of what you’ll need to know before getting started.

Projects and Datasets

We saw that machine learning is a field that relies on data processing. When you’re learning it, it helps if you actually get to work on datasets and put the algorithms you’ve learned to work. Always choose a course that is interspersed with practical examples and projects, so you know how to apply the things you’re learning.

Libraries and Frameworks

There are certain tools in use today that make it simpler to implement machine learning algorithms. Technologies like TensorFlow and Scala are popular among those who work in the field. Knowing how to use them can also boost your employability. When choosing a machine learning certification, lean towards ones that will teach you how to use these frameworks.

Now that you know how to choose the right machine learning certification for you, here’s our list of the best ones available in 2021.

Top 10 Best Machine Learning Certifications 2021


1. Machine Learning by Stanford

  • 60 hours to complete $79 for a certificate (free to audit) Our rating  
  • Course Highlights
  • Taught by: Andrew Ng
  • Uses illustrations to visualize concepts
  • Level: Beginner

Why we like it

This course makes machine learning accessible to learners and provides the perfect foundation for deeper learning in the subject.

The Machine Learning course by Stanford on Coursera is something of a gold standard in this domain. This is a course that is recommended by professors and professionals to anyone trying to pick up the basics of machine learning. Over 4 million students have enrolled in the course, which is a testament to its popularity, and also serves as a great community to turn to for discussions.

This course is taught by Andrew Ng, a name that you’re probably familiar with if you’ve tried to learn AI or machine learning online in the past. He has a unique ability to do deep-dives on seemingly arcane topics like machine learning and still keep the course refreshingly accessible. In this particular course, he uses teaching aids like illustrations to visualize concepts.

Beginner learners will enjoy how well this course is structured. It starts with an introduction to the topic and then goes into mathematical basics like linear algebra and logistic regression. The last week of the course focuses on applying students’ learnings by building an OCR application.

If you’re looking to learn machine learning, you can’t go wrong starting off with this course. The one thing certain students might miss is working more with code, since the course focuses more on theoretical concepts in machine learning.

Pros

  • Large student community
  • Instructor who makes concepts accessible
  • Well-structured

Cons

  • Not enough work with actual code


2. Machine Learning A-Z: Hands-On Python & R in Data Science

  • 44 hours 29 minutes $94.99 Our rating  
  • Course Highlights
  • Most in-depth course available
  • Practically focused learning style
  • Level: Beginner

Why we like it

The Machine Learning A-Z course is full of practical examples on how to use Python to create machine learning algorithms.

We saw that the Machine Learning course by Stanford could have done with more coding examples. If you like to get your hands dirty with actual code, then the Machine Learning A-Z: Hands-On Python & R In Data Science course is exactly what you’re looking for. This course uses Python and R and comes with several code templates to help learners understand how to apply concepts in data science and machine learning.

With a 44-hour runtime, this is one of the most in-depth machine learning courses out there. It starts right at explaining the difference between terms like machine learning, deep learning, and artificial intelligence. It then goes into mathematical concepts in machine learning while using code examples to help learners understand how they’re used.

This is a great course to work through if you’re a beginner in the field of machine learning. As the creators of the course explain, having a good understanding of high school mathematics is more than enough to get started with it. It also helps to know some basic Python programming.

The sheer scope of the course might be intimidating to some. 44 hours of video can translate into hundreds of hours of practice. So if you’re looking just to dip your toes in the field, this is not the course for you.

Pros

  • Practical code examples
  • In-depth
  • Beginner-friendly

Cons

  • Scope of the course can be intimidating to some


3. Machine Learning With Python: A Practical Introduction

  • 5 weeks (4-6 hours per week) $99 for a certificate (free to audit) Our rating  
  • Course Highlights
  • Taught by: Saeed Aghabozorgi (IBM)
  • Uses both supervised and unsupervised learning
  • Level: Beginner

Why we like it

This course teaches machine learning from the perspective of supervised and unsupervised learning, a distinction that is often overlooked by other courses.

When IBM teaches you machine learning, you know that it’s going to be worth your time. The Machine Learning with Python: A Practical Introduction course is by Saeed Aghabozorgi, a senior data scientist at the company. Aghabozorgi’s experience in the domain shines through in the course.

The unique approach that this course takes is its focus on the distinction between supervised and unsupervised learning. The former provides an algorithm with a labeled dataset that it can train on, while the latter provides unlabeled data and lets the algorithm work out patterns on its own. Studying machine learning through this lens is a great way to get job-ready since these are approaches used in the industry.

This course also covers a wide range of machine learning algorithms that are widely used today. That includes popular ones like regression, classification, and dimensional reduction. All that learning is capped off with a project where students create a recommendation system.

This course is a tad heavy on the theory, though. You will have to learn a lot of different machine learning algorithms from a theoretical perspective and may not get to apply all of them in the project.

Pros

  • Reputed course from an industry leader
  • Coverage of supervised and unsupervised learning
  • Wide range of algorithms covered

Cons

  • Heavy on theoretical details


4. Python for Data Science and Machine Learning Bootcamp

  • 25 hours $94.99 Our rating  
  • Course Highlights
  • Covers most Python libraries
  • Includes supplementary PDF materials
  • Level: Intermediate

Why we like it

This course covers most of the Python libraries that can be used for machine learning applications.

One of the great things about Python is all the different libraries that you can simply hit up to get different jobs done. For example, the NumPy library can be used to carry out all kinds of mathematical operations. The Python for Data Science and Machine Learning Bootcamp course teaches you how to use these libraries to create powerful machine learning algorithms.

The course is created by Jose Portilla, a data scientist who has publications in fields like microfluidics and materials science. Portilla’s approach is to give students an intuition for how machine learning can be applied effectively to real-world problems. This problem-solving approach makes this course fun to work through.

What also helps is that there is some very helpful supplementary material that comes with the Python for Data Science and Machine Learning Bootcamp. Make sure to peruse the PDFs and work through the assignments to get a full understanding of the topics that are covered.

This course could have done a better job of explaining some of the theoretical aspects of machine learning a little better. You might have to work through extracurricular content to better understand some of the mathematical concepts underlying the code explained in it.

Pros

  • Explains how Python libraries can support machine learning applications
  • Takes a problem-solving approach
  • Helpful supplementary content

Cons

  • Not enough theoretical rigor


5. Mathematics for Machine Learning

  • 4 months (4 hours per week) $49/month Our rating  
  • Course Highlights
  • Details the underlying mathematics
  • Includes multiple practical projects
  • Level: Beginner

Why we like it

If you’re looking for a refresher on the mathematics that underlies machine learning algorithms, then this is the course for you.

Machine learning is a discipline that is built on mathematical concepts. If you want to work in the field, you need to have a good understanding of the abstract ideas that can be applied to real-world problems. The Mathematics for Machine Learning specialization by Imperial College London fills in the gaps in your understanding of those concepts in a systematic manner.

First, let’s note that this is a specialization on Coursera, which means that it is a set of courses. This particular one has courses in linear algebra, multivariate calculus, and a technique called Principal Component Analysis (PCA). The learning path is structured well, and you eventually gain a great understanding of all the math that underlies machine learning algorithms.The project work in these courses is quite fun. For example, the linear algebra course includes a project that shows how machine learning can be used to rotate images of faces. It’s a great way to understand how all of the theories you’re learning can turn into software eventually.

Before getting into this specialization, you need to know that it takes about four months to complete. That is a big ask for some. But if you've resolved to take a systematic approach to machine learning, then this course is a must-do.

Pros

  • Systematic approach to mathematics for machine learning
  • Fun projects
  • Well-structured

Cons

  • Four months can be a big investment for some


6. Machine Learning, Data Science and Deep Learning With Python

  • 14 hours 20 minutes $94.99 Our rating  
  • Course Highlights
  • Taught by: Frank Kane (Amazon IMDB)
  • Culminates in a large practical project
  • Level: Intermediate

Why we like it

This course comes from an instructor with valuable industry experience and a great understanding of applied machine learning.

Frank Kane has a world of experience in the field of machine learning. He spent almost a decade working at Amazon and IMDB, and his code powers recommendations that go out to millions of Internet users. That’s who you’re learning from when you take up the Machine Learning, Data Science and Deep Learning with Python course on Udemy.

Kane uses his experience in the industry to lift the veil on the techniques that real engineers use while developing machine learning algorithms. The course goes over a whole host of techniques that come in handy when training computers on datasets and trying to tease out patterns in data. If you’re someone who wants an overview of machine learning and still go in-depth, then this is a great course to work through.

Every concept is explained in a clear, concise manner in this course. Kane follows a method where he explains each algorithm or technique and then demonstrates it using Python code. He doesn’t go too much into the mathematical basis for these algorithms and focuses more on how they translate to code.

This course does culminate in a project. However, it would’ve helped if there were more projects interspersed through the course, given the breadth of the topics it covers. You need to have some knowledge of coding in Python in order to do this course.

Pros

  • Taught by an industry professional with extensive experience
  • Broad range of topics and also in-depth
  • Clear explanations and code examples

Cons

  • Not enough project work


7. Applied Machine Learning in Python

  • 34 hours to complete $49/month Our rating  
  • Course Highlights
  • Taught by: Kevyn Collins-Thompson
  • Focused on practical application
  • Level: Intermediate

Why we like it

The Applied Machine Learning in Python course focuses on how machine learning techniques can be used on real-world data without too much emphasis on the underlying mathematics.

Once you know how to work with data in Python, it comes time to apply those machine learning techniques to real-world data. The Applied Machine Learning in Python course shows students how different algorithms can be used to study data, implemented using Python code. This focus on practical applications is both fun and serves to illustrate the power of machine learning techniques.

The course is taught by Kevyn Collins-Thompson, an Associate Professor at the University of Michigan School of Information. He uses the SciKit Learn library to implement different machine learning techniques. The course has an easy-to-follow progression, where each week is a module on one broad machine topic. It goes over supervised learning, evaluation, and unsupervised learning, after an introduction to machine learning in the first week.

This course is a part of the University of Michigan’s specialization in Applied Data Science with Python. Once you finish this course, you can move on to courses in applied text mining and applied social analysis that are part of the specialization. Having this learning path ahead of you is a great way to gain direction as a machine learning student.

You will notice that certain assignments in this course require information that is not covered in the video lectures. You might have to do some Googling to get that information, which is a useful skill to have if you’re going to work in software development.

Pros

  • Focus on practical applications
  • Learning path to gain direction as a student
  • Logical progression of modules

Cons

  • Assignments require information not in lectures


8. Machine Learning With TensorFlow on Google Cloud Platform

  • 9 weeks $49/month Our rating  
  • Course Highlights
  • Understand Google’s approach to machine learning
  • Explores ‘Feature Engineering’
  • Level: Intermediate

Why we like it

This course teaches students how to use Google’s Cloud Platform infrastructure to execute machine learning applications.

Google generates volumes of data every single second. Working with all that data requires machine learning since you can’t process all of it using more manual techniques. The Machine Learning with TensorFlow on Google Cloud Platform specialization is your peek into how the tech giant uses machine learning to wrangle all the data that it generates.

The course is designed in a way where students learn about Google’s approach while simultaneously gaining an understanding of general machine learning approaches. The first course is on how the company does machine learning. You then launch into learning it for yourself using an open-source machine learning platform called TensorFlow. Learning to use this technology instantly makes you more employable as a machine learning engineer given how commonly it’s used.

The final course is the cherry on the cake of this specialization. That course is on feature engineering, a topic that isn’t covered by a lot of machine learning courses. Here, students learn how to determine which the most important features of a particular dataset are and how to build them.

This is a specialization, so it will take you a couple of months to get through. The videos in each course are short in length and it sometimes feels like the creators could have combined them so that they are easier to consume.

Pros

  • Teaches Google’s machine learning approach
  • Well-structured
  • Unique focus on feature engineering

Cons

  • Short videos could have been combined


9. Introduction to Applied Machine Learning

  • 7 hours to complete $49/month Our rating  
  • Course Highlights
  • Taught by: Anna Koop (Machine Intelligence Institute)
  • Geared towards software professionals
  • Level: Intermediate

Why we like it

Professionals can use this course to build on their knowledge and apply machine learning to business problems.

Machine learning courses are not only for students and those looking to land jobs. Software professionals working in fields like data science and application development can use these courses to level up and make an upward career move. If you’re looking to gain some skills in the machine learning field, then the Introduction to Applied Machine Learning course is what you’re looking for.

This course is provided by the Machine Intelligence Institute at the University of Alberta, a division that’s dedicated to studying and teaching machine learning. The instructor, Anna Koop, is a senior scientific advisor at the institute. Thanks to the institute’s narrow focus on machine learning and the instructor’s experience in the field, students are assured an in-depth understanding of applying machine learning algorithms.

The entire focus of this course is teaching students how business problems can be solved using machine learning. In just four weeks, you’ll go from an introduction in the subject to being able to identify ways in which you can apply these algorithms, no matter which industry you work in.

This course is a quick affair, requiring only about seven hours to complete. It requires that you know some basic data science principles to apply machine learning algorithms. Some students might find that the course is too fast-paced and glosses over some important theoretical concepts.

Pros

  • Created by an institute that specializes in machine learning
  • Designed for professionals
  • Fast-paced

Cons

  • Glosses over some important theoretical details


10. Scala and Spark for Big Data and Machine Learning

  • 10 hours $94.99 Our rating  
  • Course Highlights
  • Deep-dive on Scala
  • Includes interactive assignments
  • Level: Advanced

Why we like it

This course focuses narrowly on Scala, giving students an in-depth understanding of the much sought-after skill.

We’ve seen technologies like Python and TensorFlow pop up in discussions about machine learning earlier. Another important piece of technology that you should consider investing time in is Scala, which you can run on Apache Spark. The Scala and Spark for Big Data and Machine Learning course teaches you just that, giving you important information on how to use the technology from a machine learning perspective.

When it comes to learning to use Scala, courses on the Internet don’t get a lot more comprehensive than this one. Jose Portilla, who we encountered in an earlier course, takes students through everything from installing Scala on a computer right through to applying machine learning techniques like Principal Component Analysis using the software.

Portilla lays out the finer details of machine learning in Scala in a way that’s very easy to understand. The core course content is supported by interactive assignments which serve to test students and show them where the gaps in their understanding might lie. If you work through the videos and assignments, you will find yourself being able to work with Scala right after finishing the course.

It warrants mentioning that the last time this course was updated was in 2019. As a result, some of the installation instructions and syntax details might be different in 2021. But this is something you can solve easily with some Googling. This course is worth your time if you have a foundation in machine learning and want to tap into the power of Scala.

Pros

  • Very comprehensive
  • Teaches Scala, a sought-after skill in the field
  • Interactive assignments to refresh learnings

Cons

  • Some content is outdated


Frequently Asked Questions

There are several programming languages that have come to be associated with programming for machine learning applications. By far the most popular is Python, which is now used by more than 8 million developers around the world.

If you’re just getting started, then you should learn Python. But there are other programming languages out there that can get the job done. That includes Java, JavaScript, Julia, and R.

There are a few topics in mathematics that it helps to be familiar with if you’re trying to learn machine learning. That includes probability theory, calculus, statistics, and linear algebra.

You don’t need to dive too deep into any of these to get started with machine learning. You just need to be conversant with basic concepts in each of them, mostly to understand how the algorithms that ,you use manipulate data.

There are three main approaches to machine learning, and each has different algorithms associated with it.

Supervised learning involves giving a computer labeled data and input features so that it can find patterns. For example, you tell a computer which basketball matches a team won, and which it lost. Those are the labels. Then you input information on which players were on at different times and the number of rebounds, shots, etc. If you do this right, the computer may be able to find a correlation between your strategy and when you win.

In unsupervised learning, the computer isn’t told all those things. It’s simply given a whole lot of data and allowed to find patterns on its own.

Then finally, we have reinforcement learning. In this approach, we train computers a lot like humans are taught things. They are left to interact with a data environment and work to find patterns. When they find something valuable, they are rewarded, so that particular approach is reinforced. In this way, the computer begins to learn what you’re looking for in a dataset.



Extra Credit: A Deep Dive on Machine Learning

Here’s everything you need to know to support your decision to pursue a machine learning certification.
robot solves problem on chalkboard

How Computers Learn

Researchers have used several different techniques to make autodidactism possible in computers. That includes relatively simple approaches like clustering and decision trees to more advanced methods like artificial neural networks.

Machine learning also usually requires large datasets. This is because the process involves computers looking through data to find patterns and anomalies in them. The larger your dataset, the more likely the computer is to be able to work out these patterns.

This might sound like it has to do with automating the data analytics process. But that’s not what the essence of machine learning is. The main idea behind the discipline is using the incredible computational power we have at our disposal to find interesting patterns in data and make useful inferences from them.

Applications of Machine Learning

Machine learning can be applied to any domain where large amounts of digital data is produced. It has been used to do everything from diagnosing diseases using image-based data to finding patterns in legal cases.

Just to understand the uses of machine learning better, let’s take an example that you’re probably familiar with. When you login to your Netflix account, you see a section that shows you movies and TV shows based on ones that you’ve already watched. But how does Netflix know that you’d like certain pieces of content based on your watch history?

Well, they aren’t just guessing. Netflix makes those choices using a recommender system that’s built using machine learning models. They use all the data they have on the stuff you’ve already watched (genre, director, watch-time, release year) to determine what you might be interested in watching next.

The service does all that without explicitly telling their computers what to show you or what kinds of movies to look for. That’s the kind of thing that becomes possible thanks to machine learning.

Now when it comes to this area of computer science, there is a lot of jargon thrown around. Let’s take a look at some terminology related to machine learning and learn how it’s distinct from other disciplines.

Machine Learning vs. Artificial Intelligence vs. Deep Learning

These are terms that tend to get confused and are often used interchangeably. But they are all different things, and it’s important to know the distinctions between them.

Artificial intelligence is the concept that underlies several different pioneering technologies in computing. It’s kind of an umbrella term that’s used to refer to applications that get computers to learn and solve problems in a way that makes it seem like they’re imitating human cognition.

We saw what machine learning is in the previous section. There are a couple of things that characterize the practice. Machine learning implies training algorithms on data. The goal of the training is to carry out tasks like regression or clustering to find patterns in the data.

Finally, we get to deep learning, which is in some ways a form of machine learning and an advancement of it. Deep learning makes use of neural networks to achieve things that standard machine learning models never could. One of the main things that becomes possible with them is what’s known as feature extraction, where abstract representations of data are created so that classical machine learning algorithms can be applied to them.

When you start learning machine learning, you’re working with systems that are artificially intelligent in some ways. You’re also creating a foundation on which you can learn deep learning, if you so please.

How to Get the Most out of a Machine Learning Certification

Our list goes over all of the best machine learning certification courses available online. But simply working through videos or landing a certification won’t make you a great machine learning engineer. Here are a few things that you can do to build strong foundations in the subject and get job-ready.

Get Your Mindset Right

Machine learning is an intimidating prospect to many, even those who have worked on data or programming before. There seems to be something that’s mystical about the field that keeps people from diving into it. None of that needs to be the case.

Machine learning is just like any other subject in the computer sciences. You don’t need to start with precursor math or know every algorithm there is in order to know how to work with these tools.

Instead of focusing on those things, try to gain a broad understanding of what the field is and what problems it solves. Then choose the right tools to solve that problem and learn how to use them, whether frameworks or algorithms. Realizing that machine learning doesn’t need to scare you is an important epiphany to have when you’re getting started.

Pick a Tool

The world of machine learning is rife with all kinds of tools and programming languages that you can use to work with those tools. It can be confusing which one to start with, and the temptation to try to learn all of them is very real. Try to keep that temptation at bay.

You don’t need to know how to use every machine learning framework in order to be successful in this enterprise. If you decide on Weka Workbench, then stick with that. If you know some Python, then choose a course that uses it. The last thing you want to do in your learning journey is to get in your own way by wanting to do too many things.

Practice on Datasets

All the theoretical machine learning knowledge you accrue isn’t worth much if you don’t know how to apply it. Working on actual data gives you the opportunity to understand how different algorithms process information and provide results.

But where do you find datasets you can work with? Fortunately, there are resources online for that. For example, the Machine Learning Repository is full of different kinds of datasets that you can download and play around with yourself.

Build a Portfolio

Ultimate, what most of us want is to land a job in machine learning. If that’s your goal, you should be building a portfolio of projects as you learn so that you have something to show for all the learning you’ve done.

A portfolio doesn’t consist only of programs that you’ve written. Coursework that you’ve completed in machine learning always looks good on a portfolio, too. You can also include competitions that you’ve been part of where you worked with machine learning data. Kaggle hosts such competitions regularly, and they’re a great way to learn new concepts hastily and create a new portfolio entry.

Final Thoughts

If there is one thing you should take away from everything we’ve gone over, it’s that you can study machine learning. You don’t need to be intimidated by it for any reason.

It’s also very possible to study the subject through online courses. That’s a better approach than just reading books since you get to work through projects and real-world problems. Not only does that serve to build your understanding of the subject, but it also serves as a great item to put on your portfolio.