Simran Kaur Arora | 15 Aug, 2023

The 10 Best Machine Learning Certifications for 2024

This article discusses machine learning certifications. We also evaluated their associated courses, researched their inclusions, and evaluated their relevance. Our methodology focused on finding trusted institutions with valuable instruction, then determining the best machine learning certification.

After all, machine learning is a popular field in the world of AI. We noted an exponential increase in the need for ML engineers with certifications. This article delves into which offers the best value in the field. We discuss which Machine learning certification gives the most value for the money, which provides additional value in the industry. As artificial intelligence continues to grow in popularity, so too will the need for ML certifications.

How to Evaluate Machine Learning Certifications and Courses

When evaluating machine learning certifications, several criteria can help you determine its credibility, relevance, and value. Here's a list of things you should consider:

  1. What's in the Box?: The best machine learning certifications don't just test you on jargon. They cover big machine learning techniques like supervised learning, deep learning, and other ML fundamentals.

  2. Getting Your Hands Dirty: The best learning happens by doing. Look for a certification that proves you play with real data and tackle real projects. You should need to your hands dirty with the best machine learning professional certificate. Otherwise, what's the point?

  3. Who's Teaching?: If you want a machine learning specialization program that stacks up against the best in the industry, consider the instructor. We do. We expect to learn machine learning fundamentals almost anywhere, but industry-leading techniques come from industry leaders.

  4. Street Cred: The most notable machine learning certifications come from well-known places (like top universities or tech giants). That's what makes them pack a punch in the job market.

  5. Keeping it Current: ML is always changing. You'd want a certification that's up-to-date with the latest tools and techniques. Consider the changes in machine learning solutions over the past five years. Now think of what you've read in the news today. Basic programming skills won't cut it. You'll want to stay current.

  6. Dollar Bills: Like any professional certificate program, the one you choose for machine learning should help you earn more money. It will also likely come with a cost of its own. And, while shelling out big bucks doesn't guarantee the best certification course, you'd want to make sure you’re getting your money's worth.

  7. Real Talk: Read reviews. If people are raving about a specific program, it might be legit. If they’re trashing it... well, you get the drift. The top machine learning certifications earn their reputations. So look for reviews (like ours!).

Remember, an ML cert can jazz up your resume, but there’s no substitute for diving in, experimenting, and continuous professional development. Keep it real, and keep learning!

The Best Machine Learning Certifications

Let's talk about our ranking for the best Machine Learning Certification courses. As a reminder, we used the above methodology to help guide our rankings. We like everything on this list. Those at the top are just the ones where we found the most value.

Note that every machine learning certification makes a statement. Your choice will say something about the way you learned, the institution you trust, and the hours you've spent on the subject.

Ready to see the best machine learning certifications? Let's get started.

1. Machine Learning with TensorFlow on Google Cloud Platform Specialization

Specialization comprises 5 courses and promises to take you from an overview of Machine Learning's importance to lectures about building ML models. The program consists of introductory-level lessons and covers what machine learning can and why it is so popular, followed by classes that focus on Tensorflow, an open-source machine learning framework.

These sets of lectures aim to create, train, and deploy ML models, solve numerical problems, and much more. There are also numerous hands-on opportunities to enhance the accuracy of ML using the various features available on the Google Cloud Platform.

Registration: Every 2 months on Coursera
Fee: [Financial Aid Available]
Course Duration: 5 months
Mode of Teaching: Online
Prerequisites: Computer science or engineering background.

Key Benefits

  • The course covers everything from basics like machine learning concepts to what kind of problems it can solve.
  • Teaches to create machine learning models that scale in TensorFlow and scale out the training of those models.
  • Teaches to integrate the right combination of parameters that harvest accurate, generalized models and knowledge of the theory.
  • Get hands-on labs available with the Google Cloud platform and enhance your skills.
  • Opportunity to share your information directly with Google and Publicis to be considered for open hiring opportunities.
  • Earn a Specialization Certificate to share with your professional network and potential employers.

You can sign up here.

2. Professional Certificate Program in Machine Learning from MIT

The course is highly recommended for professionals and undergraduates to shape their careers. The course ensures businesses and individuals have an education and necessary training to succeed in the AI-powered future.

The MIT faculty experts expose participants to the latest breakthroughs in cutting-edge technologies, research, and other best practices for building advanced AI systems. The program provides the foundation of knowledge that can be put to immediate use to help people and organizations advance cognitive technology.

Registration: May 2020
Fee: each course costs between $2500-$5500
Course Duration: Varies
Mode of Teaching: Online
Prerequisites: Bachelor’s degree in computer science, statistics, physics, or electrical engineering.

Key Benefits

  • Personal training from the faculty and leading industry practitioners.
  • Learn essential concepts and skills needed to develop practical AI systems.
  • Discusses the challenges posed by AI in the workplace.
  • Apply industry-relevant, cutting-edge knowledge in machine learning and AI.
  • Network with an experienced group of peers from around the globe.

You can sign up here.

3. IBM's Machine Learning with Python

This course covers the basics of machine learning using a well-known programming language, Python. The course reviews two main components: First, learning about Machine Learning's purpose and where it applies to the real world. For many, machine learning and artificial intelligence are the primary reasons to learn Python programming.

Second, it provides a general overview of Machine Learning topics such as supervised vs. unsupervised learning, model evaluation, and Machine Learning algorithms. These are exceptionally valuable for specialists in the machine learning field.

Registration: throughout the year.
Fee: Free
Course Duration: 8 weeks
Mode of Teaching: Online
Prerequisites: Python


Key Benefits

Learn new skills such as regression, classification, clustering, and SciPy

Opportunity to add new projects that you can add to your portfolio, including predicting economic trends, cancer detection, predicting customer churn, recommendation engines, and many more.

A certificate in machine learning to prove your competency

You can sign up here.

4. Machine Learning from Stanford Online

The course provides a broad introduction to statistical pattern recognition and machine learning. Differentiates between supervised and unsupervised learning as well as learning theory, reinforcement learning, and control. Explores recent applications of machine learning and design and develops algorithms for machines.

Looking for a deep dive into this? We gave an inside look at AI professional program built by the team at Stanford University.

Registration: Rolling
Course Duration: 3 months
Mode of Teaching: Online
Prerequisites: Computer science or engineering background.

Key Benefits

  • Basics concepts of machine learning
  • Generative learning algorithms
  • Evaluating and debugging learning algorithms
  • Bias/variance tradeoff and VC dimension
  • Value and policy iteration
  • Q-learning and value function approximation

You can sign up here.

5. Machine Learning at Udacity

The course consists of two modules to discuss various types of machine learning.

The first module covers Supervised Learning, a machine learning task that trains for your email to filter spam, your phone to recognize your voice, and for computers to learn a bunch of other cool stuff.

The second module teaches about unsupervised machine learning. Ever wonder how Amazon knows what you want to buy before you do? Or how can Netflix predict what movies you'll like? This section answers such questions. You'll learn about deep learning models and data engineering.

Finally, it answers, can we program machines to learn like humans? This Reinforcement Learning section teaches algorithms for designing self-learning agents like us! Use this for your own machine learning projects with Google Cloud.

Registration: throughout the year on Udacity
Fee: Free
Course Duration: 4 months
Mode of Teaching: Online

Key Benefits

Supervised Learning

  • Machine Learning is the ROX
  • Decision Trees
  • Regression and Classification
  • Neural Networks
  • Instance-Based Learning
  • Ensemble B&B
  • Kernel Methods and Support Vector Machines (SVM)s
  • Computational Learning Theory
  • VC Dimensions
  • Bayesian Learning
  • Bayesian Inference

Unsupervised Machine Learning

  • Randomized optimization
  • Clustering
  • Feature Selection
  • Feature Transformation
  • Information Theory

Reinforcement Learning

  • Markov Decision Processes
  • Reinforcement Learning
  • Game Theory

You can sign up here.

6. Professional Certificate in Foundations Of Data Science

The course provides a new lens through which to explore issues and problems. It teaches us to combine data with Python programming skills to explore encountered problems in any field of study or a future job.

The program also helps aspiring data scientists teach them how to analyze a diverse array of real data sets, including geographic data, economic data, and social networks. The course also teaches inference, which helps to quantify uncertainty and measures the accuracy of your estimates. Finally, all the knowledge is put together to teach prediction with the help of machine learning.

The Professional Certificate in Foundations of Data Science aims to make data science accessible to everyone.

Registration: 2-4 months on edX
Fee: $267
Course Duration: 4months(self-paced)
Mode of Teaching: Online
Prerequisites: This course is specifically designed for beginners who do not have any computer or statistics background and no programming experience

Key Benefits

You learn :

  • To draw robust conclusions based on incomplete information by critical thinking.
  • Python 3 programming language for analyzing and visualizing
  • data and other computational thinking and skills
  • To make predictions based on machine learning.
  • To communicate and interpret data and results using a vast array of real-world examples.

You can sign up here.

7. Certification of Professional Achievement in Data Sciences

Multiple courses such as algorithms for data science, machine learning for data science, probability, and statistics, and exploratory data analysis are covered in this course.

This course is suited for candidates having prior knowledge in statistics, linear algebra, probability, & calculus. Programming. The certification prepares students to expand their career prospects or change career paths by developing foundational data science skills.

Registration: Deadline February 15 for Fall
Fee: $24,216
Course Duration: 12 months
Mode of Teaching: Online and Campus

  • Undergraduate degree
  • Prior quantitative coursework (calculus, linear algebra, etc.)
  • Prior introductory to computer programming coursework

Key Benefits

  • Learn the basics of computational thinking using Python.
  • Learn to use inferential thinking to make conclusions about unknowns based on data in random samples.
  • Learn to use machine learning, focusing on regression and classification, automatically identifying patterns in the data and automatically making better predictions.

You can sign up here.

8. eCornell Machine Learning Certificate

Cornell’s Machine Learning certificate program focuses on the implementation of machine learning algorithms using Python. Using a combination of math and intuition, students learn to frame machine learning problems and construct a mental model to understand data scientists' approach to these problems programmatically. Implementation of concepts such as k-nearest neighbors, naive Bayes, regression trees, and others are explored with various machine learning algorithms.

The program allows the implementation of live data algorithms while practicing debugging and improving models through support vector machines and ensemble methods. Finally, the coursework explores the inner workings of neural networks and how to construct and adapt neural networks for various data types.

This program uses Python and the NumPy library for code exercises and projects. Projects are completed using Jupyter Notebooks.

Registration: throughout the year
Fee: $3,600 or $565/month
Course Duration: 3.5 months
Mode of Teaching: Online
Prerequisites: Python

Key Benefits

  • Redefine problems using machine learning terminology and concepts.
  • Develop a face recognition system using algorithms.
  • Implement the Naive Bayes algorithm and estimate probabilities distribution from data.
  • Create an email spam filter by implementing a linear classifier
  • Improve the prediction accuracy of an algorithm by using a bias-variance trade-off.
  • Use an effective hyperparameter search to select a well-suited machine learning model and implement a machine learning setup from start to finish.
  • Train a neural network.

You can sign up here.

9. University of Washington Certificate in Machine Learning

Brought to you by the professional education and continuing education program at the University of Washington, this three-course certificate program examines all aspects of machine learning.

Concepts like probability and statistical methods at the core of machine learning algorithms are taught in this course. It also practices ways to apply these techniques, using open-source tools and developing judgment and intuition to address actual business needs and real-world challenges.

Registration: throughout the year
Fee: $4,548
Course Duration: Varies
Mode of Teaching: Online

Key Benefits

  • Concepts of probability, statistical analyses, mathematical modeling, and optimization techniques
  • Supervised and unsupervised learning models for tasks such as forecasting, predicting, and outlier detection
  • Advanced machine learning applications, including recommendation systems and natural language processing
  • Deep learning concepts and applications
  • How to identify, source, and prepare raw data for analysis and modeling

You can sign up here.

10. Harvard University Machine Learning

This course teaches principal component analysis, popular machine learning algorithms, and regularization by building a movie recommendation system.

The course teaches about training data and how to use a set of data to discover potentially predictive relationships. By building the movie recommendation system, students learn how to train algorithms using training data to predict the outcome for future datasets.

The course also teaches overtraining and techniques to avoid it, e.g., cross-validation.

Registration: throughout the year on edX
Fee: Free
Course Duration: 8 weeks
Mode of Teaching: Online
Prerequisites: Python

Key Benefits

  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization, and why is it useful?

You can sign up here.

Benefits of a Machine Learning Certification

Getting a machine learning certification is like having a golden ticket in the tech world. It's not just a shiny badge—it means you know your stuff in a field that's super hot right now.

It can help you stand out when you're hunting for jobs, possibly lead to better pay, and show employers you're serious about what you do. Plus, it's a cool way to keep learning and stay on top of the latest tech trends, helping you tackle real-world problems with machine learning.

Machine Learning Jobs

An ML certification can help show potential employers you know what you're talking about. Some AI programs even offer LinkedIn badges to help you stand out from other job applicants on social media.

Of course, the value also exists in building networks. When you sign up for a machine learning certification program, you can sometimes network with other students (and instructors). These can provide valuable resources for your own ML projects.

New Experience in a Burgeoning Field

One of the best reasons to consider a certification in machine learning is access to new experiences in a burgeoning field. Expert instructors introduce bleeding-edge ideas, which you can use in your own projects.

You can use these same principles to improve machine learning models at your job. If you're already in the industry, there's no reason you wouldn't be able to implement your new machine learning skills right away.


In this article, we evaluated machine learning certifications. We looked for prestige, industry relevance, and overall value. The result presented our choice for the best machine learning certification.

Is there something you'd like to add? Please feel free to share your experience with ML certification programs. We love to evaluate professional programs, and we look forward to updating this article with fresh new ideas.

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By Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.

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