Earning a machine learning (ML) certification can propel your career forward in a field that is ripe with opportunities to advance. In fact, the demand for AI and ML jobs is expected to grow 71 percent in the next five years. This makes sense given the fact that the machine learning market is predicted to grow from just over $21 billion USD in 2022 to nearly $210 billion by the year 2029.
The Jefferson Frank Salary Survey found that 84 percent of surveyed professionals perceive certifications as helpful for standing out in a competitive job market.
A machine learning certification is a credential that you earn by taking an exam or completing a series of courses. Obtaining certification demonstrates to employers that you possess theoretical and practical understanding of algorithms.
Also read: What are the Types of Machine Learning?
Top machine learning certifications
AWS Certified Machine Learning – SpecialtyAmazonintermediate300
Cornell Machine Learning CertificateCornell Universityintermediate3750
Machine Learning on Google Cloud SpecializationCourserabeginner to intermediate1600
Google Cloud Machine Learning Engineer Professional CertificateCourseraintermediate to advanced200
IBM Machine Learning Professional CertificateCourserabeginner to intermediate280
Microsoft Certified Azure AI Engineer AssociateMicrosoftintermediate165
Stanford University Machine Learning SpecializationStanford University and DeepLearning.AIbeginner80
AWS Certified Machine Learning – Specialty
AWS Certified Machine Learning – Specialty is designed for those who have least one year of experience in development or data science and want to prove their expertise in creating, training, refining, and deploying machine learning models on the AWS Cloud.
In the Jefferson Frank Salary Survey, more than two-third of respondents found AWS certifications to be an important factor in increasing their earning potential. In fact, those who earned this certification received an 18 percent increase in salary.
Those who wish to obtain this certification should first fulfill the following prerequisites:
2+ years of experience with the AWS CloudUnderstanding of basic ML algorithmsExperience with basic hyperparameter optimizationExperience with ML and deep learning frameworksKnowledge of and ability to follow model-training, deployment, and operational best practicesExam details
The proctored online exam features 50 multiple choice and multiple response questions on the following topics:
Data engineeringExploratory data analysisModelingMachine learning implementation and operationsCandidates have 180 minutes to complete the exam and must receive a passing score of 750 or higher.
Amazon provides plenty of free resources to assist with preparation, such as an exam guide and sample questions.
For additional help with prep, Udemy offers a prep course for this certification.
Best for: Machine learning professionals looking to specialize in the AWS Cloud environment
Cost: $300 USD
Cornell University Machine Learning Certificate
This online certification for machine learning entails nine courses:
Problem Solving with Machine LearningEstimating Probability DistributionsLearning with Linear ClassifiersDecision Trees and Model SelectionDebugging and Improving Machine Learning ModelsLearning with Kernel MachinesDeep Learning and Neural NetworksLinear Algebra: Low DimensionMatrix and Linear Algebra: High DimensionEach course takes two weeks for a total of three and a half months to complete the certification. One should expect to put aside between six and nine hours of work per week to make progress towards this certificate.
Upon completing all nine, learners possess the following knowledge and skills:
Implementing machine learning algorithms using PythonFraming machine learning problemsBuilding a mental modelDebugging and improving modelsAdapting neural networks for various data typesPrerequisites
Candidates need not be enrolled at Cornell as a full-time student in order to obtain this certificate. It’s strongly recommended to have experience with:
Linear algebraMultivariate calculusProbability theoryPythonStatisticsThe program’s website offers a free readiness test to gauge whether this certification is right for prospective learners.
Best for: Professionals with experience in data analysis, data science, developing, programming, software engineering, and statistics who need an accelerated certification program at a moderate cost level
Cost: $3,750 USD
Machine Learning on Google Cloud Specialization
Google also offers a Machine Learning on Google Cloud Specialization certification via Coursera. This certification comprises five courses:
How Google Does Machine LearningLaunching into Machine LearningTensorFlow on Google CloudFeature EngineeringMachine Learning in the EnterpriseIn these courses, users will learn:
Basic machine learning conceptsMachine learning model development, training, and deployment using Keras and TensorFlow2.x for the Google Cloud platformBigQuery MLVertex AI AutoML and BigQuery MLMachine learning best practices for enterprisesExploratory data analysisThe program takes about four months to complete with a suggested pace of six hours per week.
According to the certification website on Coursera, this certification is intended for the intermediate level, meaning those with Python programming experience. However, it covers foundational concepts, so it is likely suitable for beginners as well.
The cost of this certification will vary depending on the pace at which an individual completes the courses. After a seven-day free trial, Coursera assesses a monthly subscription fee of $39.99. The total below assumes a four-month duration.
Best for: beginners who want to acquire foundational machine learning skills for GCP.
Cost: $1,600 USD
Google Cloud Machine Learning Engineer Professional Certificate
Building on the essential skills from the Machine Learning on Google Cloud specialization certification, Google also offers the Google Cloud Machine Learning Engineer Professional Certificate. The exam tests candidates abilities in:
Framing ML problemsDeveloping ML modelsArchitecting ML solutionsAutomating and orchestrating ML pipelinesDesigning data preparation and processing systemsMonitoring, optimizing, and maintaining ML solutionsPrerequisites
Candidates are recommended to have at least three years of industry experience, including at least one year of experience designing and managing solutions using Google Cloud.
Candidates may want to obtain the Machine Learning on Google Cloud Specialization certificate before pursuing this certificate, but it’s not a requirement.
This certification is valid for two years before recertification is necessary.
The exam is a combination of multiple choice and multiple select questions that candidates have 200 minutes to complete.
Candidates may take the exam at an authorized location or in an online proctored environment.
Google provides a free exam guide, sample questions, and an on-demand webinar.
For additional help, consider Coursera’s prep course for this certification.
Best for: Machine learning engineers with some experience who want to specialize in architecting and deploying machine learning models to the Google Cloud Platform
IBM Machine Learning Professional Certificate
IBM offers a course-based machine learning certificate through Coursera. It entails successful completion of six courses that are designed to provide a theoretical understanding of and practice with key machine learning topics: