10 Best Machine Learning Certifications (July 2024)

As artificial intelligence (AI) continues to revolutionize many sectors, the vital field of machine learning rises in importance. Because of this, there is a high demand for business executives to understand both the importance of AI and how it applies to business, as well as how to harness data.

Given all of this, a machine learning certification can open up windows of opportunity. For readers who are searching for lessons in coding they should visit our Python and Tensorflow courses.

Here is a look at the top machine learning certifications:

1. MIT Sloan Artificial Intelligence: Implications for Business Strategy

Targeting business executives, this course has 2 instructors and is led by Daniela Rus, Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. She serves as the director of the Toyota-CSAIL Joint Research Center and is a member of the science advisory board of the Toyota Research Institute.

The second instructor is Thomas Malone, Malone is a professor of information technology and organizational studies at the MIT Sloan School of Management. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology. His newest book, Superminds, appeared in May 2018. He holds 11 patents, has co-founded three software companies, and is quoted in numerous publications such as Fortune, the New York Times, and Wired.

From this course you'll walk away with the following skills:

• A practical grounding in artificial intelligence (AI) and its business applications, equipping you with the knowledge and confidence you need to transform your organization into an innovative, efficient, and sustainable company of the future.
• The ability to lead informed, strategic decision-making and augment business performance by integrating key AI management and leadership insights into the way your organization operates.
• A powerful dual-perspective from two MIT schools — the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory — offering you a sound conceptual understanding of AI technologies through a business lens.

2. Oxford Artificial Intelligence

A course designed with the intention of enabling you to understand AI, its potential for business, and the opportunities for its implementation.

This course is led by Matthias Holweg, Matthias is a trained industrial engineer and is interested in how organisations generate and sustain process-improvement practices. His research focuses on the evolution and adaptation of process-improvement methodologies as they are being applied across manufacturing, service, office, and public sector contexts.

With this course you'll have an understanding of the following fundamentals:

• The ability to identify and assess the possibilities for AI in your organisation and build a business case for its implementation.
• A strong conceptual understanding of the technologies behind AI such as machine learning, deep learning, neural networks, and algorithms.
• Insight from Oxford Saïd faculty and a host of industry experts, helping you to develop an informed opinion about AI and its social and ethical implications.
• A contextual understanding of AI, its history, and evolution, helping you to make relevant predictions for its future trajectory.

3. MIT Sloan Unsupervised Machine Learning: Unlocking the Potential of Data

This course is focused on how machine learning can harness data — no matter how small — to train an AI model.

Featuring 5 instructors this course is led by Antonio Torralba, Delta Electronics Professor of Electrical Engineering and Computer Science, Head of AI+D Faculty, EECS Department, MIT CSAIL.

In this course you'll explore how how machine learning techniques are defining the potential of data. Understand how representations can dramatically reduce the quantity of labels needed to build accurate AI models. Once you have an understanding of these basics you'll progress to learning how pre-trained AI models can impact the deployment of representation learning and generative modeling in organizations.

You'll eventually discover the importance of interpretability and causality in building accurate ML models, and at the end you will explore the realities of deploying machine learning models in your organization.

This could offers an understanding of these core data fundamentals:

• An in-depth understanding of how representation learning can address business problems and increase ROI on AI initiatives.
• Insight into the challenges, opportunities, and important considerations of generative models in an organization.
• A holistic view of the landscape of pre-trained models and how to best utilize these models in your organization.

• The ability to create transparent, interpretable ML models in your context.

4. LSE Machine Learning: Practical Applications

Upgrade your data skills and develop a technical understanding of the business applications of machine learning.

This course is designed to learn how to execute a data strategy that works, begin by discovering the appropriate use and processing of data for optimizing machine learning applications. Explore regression as a supervised machine learning technique to predict a continuous variable (response or target) from a set of other variables (features or predictors).

You'll eventually understand how tree-based methods and ensemble learning methods are applied to improve the accuracy of a prediction, but more importantly understand what neural networks are, its most successful applications, and how it can be used within a business context.

After following through with this course you will:

• Have an in-depth understanding of various machine learning techniques, including regression, ensemble learning, and tree-based methods, among others.
• The ability to code in R and apply machine learning techniques to various types of data.
• Exposure to the latest frontiers of machine learning, such as neural networks and how these can be applied in business.
• Have a certificate of competence from LSE, a world-leading social science university.

5. MIT Sloan Machine Learning in Business

This is another course that is by Daniela Rus, and  Thomas Malone. This course focuses on how to leverage transformative technology in both your thinking and business applications.

You'll begin by learning about machine learning and its growing role in business. You'll understand the role of data, and the importance of an implementation plan. Follow this by exploring the requirements for the application of machine learning using sensor, language, and transaction data. From here you'll be able to develop an implementation plan for machine learning, and consider the future of machine learning in business.

This course should give you a great understanding of the following keypoints:

• A practical action plan to strategically implement machine learning in business, designed to effectively guide your organization.
• Exposure to the technical elements of machine learning, without needing to code or program, helping you to leverage this technology in your strategic thinking.
• Insights from esteemed MIT faculty and machine learning experts, offering valuable potential for unlocking new career opportunities.

6. Cognilytica – Cognitive Project Management for AI (CPMAI) Certification

This is the most comprehensive course that is offered by Cognilytica and covers data science and machine learning.

The CPMAI methodology is the industry’s best practice methodology for successful AI & ML projects. Cognilytica’s CPMAI training and certification prepares you to succeed with your AI & ML efforts, whether you’re just beginning or are well down the road with implementation.

This program is data focused on all aspects of project management AI, and this includes data science, some of the topics that will be covered:

• Fundamentals of AI and ML Terminology and concepts
• The Seven Patterns of AI
• AI Project Management Best Practices
• Deep dive into actual AI projects using CPMAI
• Supervised, unsupervised, and reinforcement learning methods, approaches, concepts, and algorithms
• Most important aspects of Data Science relevant to AI
• How business understanding, data understanding, data preparation, model development, model evaluation, and model operationalization fit together
• Iterative and agile methods for AI
• How to build Ethical and Responsible AI systems
• How to craft an ideal AI team

This program offers features the following and offers a completion certificate:

• All Skill Levels
• Trainees have up to six (6) months to complete the training
• Access to recorded videos and training materials are provided for thirty (30) days following trainee conclusion of class
• Duration: 30 hours

7. IBM Machine Learning Professional Certificate

This certificate from IBM is aimed at those looking to develop the skills and experience necessary for a career in Machine Learning. The program consists of 6 courses that help you develop an understanding of the main algorithms and their uses. While the intermediate program is useful for anyone with computer skills and an interest in leveraging data, some background in Python programming, statistics, and linear algebra is recommended.

Here are the main aspects of this certification:

• 6-course program
• Skills in Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning
• Special topics like Time Series Analysis and Survival Analysis
• Code your own projects with open source frameworks and libraries
• Digital badge from IBM upon completion
• Duration: 6 months, 3 hours/week

8. IBM AI Engineering Professional Certificate

Another one of the top machine learning certifications, this 6-course Professional Certificate is aimed at giving individuals the tools necessary to succeed as an AI or ML engineer. It covers fundamental concepts of Machine Learning and Deep Learning, such as Supervised and Unsupervised Learning. You will also learn how to build, train, and deploy deep architectures.

Here are the main aspects of this certification:

• 6-course program
• Supervised and Unsupervised Learning with Python
• Apply popular Machine Learning and Deep Learning libraries like SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow
• Tackle problems involving Object Recognition, Computer Vision, Image and Video Processing, Text Analytics, and NLP
• Digital badge from IBM upon completion
• Duration: 8 months, 3 hours/week

9. Machine Learning by Stanford University

This class offered by Stanford University teaches the most effective machine learning techniques, and you get the chance to implement them to work for yourself. The class also provides the knowledge needed to apply the techniques to new problems. It is a broad course and an introduction to Machine Learning, Datamining, and Statistical Pattern Recognition.

Here are the main aspects of this course:

• Topics like Supervised and Unsupervised Learning
• Numerous case studies and applications
• Applying learning algorithms to build Smart Robots, Text Understanding, Computer Visions, Medical Informatics, Audio, and Database Mining
• Shareable certificate upon competition
• Duration: 60 hours

10. Advanced Learning Algorithims

This short but impressive course offers a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

Here are the main aspects of this course:

• Insights from experts
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Duration: 34 hours