Best Machine Learning Certifications to Level up in 2024

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 2024, 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 2024.

Top 10 Best Machine Learning Certifications 2024

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1. Machine Learning by Stanford

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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.

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

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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.

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3. Machine Learning With Python: A Practical Introduction

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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.

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4. Python for Data Science and Machine Learning Bootcamp

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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.

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5. Mathematics for Machine Learning

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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.

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

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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.

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7. Applied Machine Learning in Python

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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.

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8. Machine Learning With TensorFlow on Google Cloud Platform

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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.

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9. Introduction to Applied Machine Learning

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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.

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10. Scala and Spark for Big Data and Machine Learning

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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.

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Frequently Asked Questions

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