5 Popular Machine Learning Certifications and Certificates: Your 2024 Guide

Machine learning certifications and certificates can help you stand out from other candidates for data science and programming jobs. Whether you complete a course or pass an exam, these credentials represent accomplishment. They can help you demonstrate your knowledge, experience, and credibility in machine learning. Continue reading to explore five popular machine learning certifications and other programmes, and learn how to choose one that's right for you. 

Certificate vs certification: What's the difference?

Many people use the terms certificate and certification interchangeably. However, they can differ in content, structure, and meaning depending on the offering organisation. Certificates typically emphasise training and academic accomplishment, whereas certifications indicate professional experience or that you've passed a certification exam that requires specialised skills. 

How to choose a machine learning programme that's right for you

Assessing the programme’s suitability for your objectives is the key to selecting the proper option. To identify which learning path is best for your career goals, consider these factors:

• Course content. Identifying focus areas for your professional development can help you narrow your choices. Note specific skills you want to gain, such as feature engineering, exploratory data analysis, or model evaluation, and match them to programme descriptions. 

• Duration. Juggling professional development while working full-time, going to school, or caring for other obligations can be challenging. Make sure the workload and deadlines associated with your chosen learning path feel manageable. 

• Outcome. Consider why you want to earn a certificate or certification. If you're looking for a new role, browse job postings for positions you're interested in and make a note of requested skills and credentials. Some employers request certifications by name. If any of the qualifications are skill-based or you want to study prior to taking an exam, you may benefit from a certificate programme. 

• Cost. Although it depends on the organisation offering the certification, certification prices sometimes include only the exam price. Prior training or study materials may be available at a separate cost. Research the price of your chosen certificate before enrolling, and consider whether or not you'll need instruction. 

• Prerequisites. If you're looking for an introduction to machine learning, consider selecting a certification programme at the beginner level. Beginner-level certificate programmes cover essential concepts to build foundational expertise. Those with background knowledge in the machine learning field may benefit more from intermediate-level or advanced programmes that focus on machine learning techniques and technical skills. 

5 machine learning certifications and certificates 

Both certificates and certifications are valuable tools for advancing your career and building more expertise. In the following list, you’ll find five popular machine learning certificates and certification programmes.

1. Andrew Ng’s Machine Learning Specialisation 

Artificial intelligence (AI) visionary Andrew Ng’s Machine Learning Specialisation is a beginner-friendly, three-course educational programme designed to help course takers master fundamental AI concepts and develop practical machine learning (ML) skills, such as building and training machine learning models.

The highly-regarded specialisation is offered jointly by Stanford University and DeepLearning.AI. Upon completing the programme, you will receive a shareable certificate that can be cited on your CV to demonstrate your knowledge and skills to potential employers. 

Requirements: The course is suitable for beginners who know basic coding and high school-level maths concepts.

Cost: The course costs 49 USD per month through a subscription to Coursera. 

2. IBM Machine Learning Professional Certificate 

IBM’s Machine Learning Professional Certificate is an online, six-course educational programme that equips learners with practical ML skills, such as supervised learning, unsupervised learning, neural networks, and deep learning. The programme also introduces course takers to specialised topics such as time series analysis and survival analysis. 

Upon completing the programme's six courses, you will receive a Professional Certificate from IBM and Coursera that indicates you have completed the course. You can add this certificate to your CV to demonstrate your knowledge and skill set to potential employers. 

Requirements: The course is accessible without prerequisites, but IBM suggests that you possess some related experience and are at an intermediate knowledge level. 

Cost: The course costs 49 USD per month through a subscription to Coursera. 

3. AWS Certified Machine Learning - Specialty 

Amazon Web Service’s (AWS) Certified Machine Learning - Specialty Certification indicates your expertise in building, training, and running ML models in AWS. 

To earn the certification, you must take and pass a 180-minute exam consisting of 65 multiple-choice and response questions. AWS designed the exam for professional developers and data scientists. It tests your understanding of ML algorithms, ability to implement hyperparameter optimisation, and perform best practices when training, deploying, and operating machine learning models. 

Requirements: Test-takers should have at least one year of hands-on experience developing, running, or architecting AWS ML and deep learning workloads. 

Cost: 300 USD (plus tax) 

Prepare for AWS with AWS

You can prepare for this exam by taking a course designed by AWS itself on Coursera.  In AWS’ Introduction to Machine Learning on AWS, you’ll explore the services that do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training, and virtual agents.

4. Google Professional Machine Learning Engineer Certification 

Google’s Professional Machine Learning Engineer Certification indicates your expertise in designing, building, and producing machine learning models using Google Cloud and industry-proven techniques. To earn the certification, you must take and pass a two-hour exam consisting of 50 to 60 multiple-choice questions covering topics such as framing ML problems, architecting ML solutions, and developing ML models.  Certifications are valid for two years, after which holders must recertify to maintain certification. 

Requirements: The exam is accessible to all, but Google recommends that test takers have three or more years of industry experience and at least one year of experience designing and managing ML solutions in Google Cloud. 

Cost: 200 USD (plus tax) 

Get ready with Google.

Google’s Professional Machine Learning Engineer certification will signal your expertise in crafting and implementing machine learning solutions with Google Cloud to potential employers and clients. What better way to prepare for a Google exam than by taking a course offered by Google? 

In Google Cloud’s Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate, you’ll explore how to design, build, and productionalise ML models to solve business challenges using Google Cloud technologies.

5. University of Washington Machine Learning Specialisation 

The University of Washington’s Machine Learning Specialisation is a four-course online educational programme covering the major areas of ML, including prediction, classification, clustering, and information retrieval. Through the course, you’ll also analyse large and complex data sets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. 

Upon completing the specialisation, you will receive a shareable certificate that you can cite on your CV to signal your knowledge and skill set to potential employers. 

Requirements: The specialisation has no prerequisites but is recommended for those with intermediate knowledge of the field and some related experience.  

Cost: The course costs 49 USD monthly through a Coursera subscription.

Hands-on projects to gain experience in machine learning 

If you still need to get ready to enroll in a certification programme, you can still start practising your machine-learning skills today. Below is a list of machine learning projects to learn independently or build your portfolio. 

• (FREE) ChatGPT Prompt Engineering for Developers

• Unsupervised Machine Learning for Customer Market Segmentation

• Cervical Cancer Risk Prediction Using Machine Learning

• Medical Insurance Premium Prediction with Machine Learning

Build your credentials with Coursera. 

Participating in certificate and certification programmes can help you develop your knowledge and skills while building your CV. On Coursera, you’ll find various options to deepen your understanding of machine learning. For example, in Andrew Ng’s beginner-friendly Machine Learning Specialisation, you’ll master critical concepts and gain the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. Ng's recently updated Machine Learning Specialisation is an ideal place to start if you’re looking to break into AI or build a machine learning career.