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
| Certification | Certifying Body | Experience Level | Cost (in USD) |
|---|---|---|---|
| AWS Certified Machine Learning – Specialty | Amazon | intermediate | 300 |
| Cornell Machine Learning Certificate | Cornell University | intermediate | 3750 |
| Machine Learning on Google Cloud Specialization | Coursera | beginner to intermediate | 1600 |
| Google Cloud Machine Learning Engineer Professional Certificate | Coursera | intermediate to advanced | 200 |
| IBM Machine Learning Professional Certificate | Coursera | beginner to intermediate | 280 |
| Microsoft Certified Azure AI Engineer Associate | Microsoft | intermediate | 165 |
| Stanford University Machine Learning Specialization | Stanford University and DeepLearning.AI | beginner | 80 |
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.
Prerequisites
Those who wish to obtain this certification should first fulfill the following prerequisites:
- 2+ years of experience with the AWS Cloud
- Understanding of basic ML algorithms
- Experience with basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- Knowledge of and ability to follow model-training, deployment, and operational best practices
Exam details
The proctored online exam features 50 multiple choice and multiple response questions on the following topics:
- Data engineering
- Exploratory data analysis
- Modeling
- Machine learning implementation and operations
Candidates 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 Learning
- Estimating Probability Distributions
- Learning with Linear Classifiers
- Decision Trees and Model Selection
- Debugging and Improving Machine Learning Models
- Learning with Kernel Machines
- Deep Learning and Neural Networks
- Linear Algebra: Low Dimension
- Matrix and Linear Algebra: High Dimension
Each 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 Python
- Framing machine learning problems
- Building a mental model
- Debugging and improving models
- Adapting neural networks for various data types
Prerequisites
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 algebra
- Multivariate calculus
- Probability theory
- Python
- Statistics
The 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 Learning
- Launching into Machine Learning
- TensorFlow on Google Cloud
- Feature Engineering
- Machine Learning in the Enterprise
In these courses, users will learn:
- Basic machine learning concepts
- Machine learning model development, training, and deployment using Keras and TensorFlow2.x for the Google Cloud platform
- BigQuery ML
- Vertex AI AutoML and BigQuery ML
- Machine learning best practices for enterprises
- Exploratory data analysis
The program takes about four months to complete with a suggested pace of six hours per week.
Prerequisites
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 problems
- Developing ML models
- Architecting ML solutions
- Automating and orchestrating ML pipelines
- Designing data preparation and processing systems
- Monitoring, optimizing, and maintaining ML solutions
Prerequisites
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.
Exam details
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
Cost: $200
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:
- Exploratory Data Analysis for Machine Learning
- Supervised Learning: Regression
- Supervised Learning: Classification
- Unsupervised Learning
- Deep Learning and Reinforcement Learning
- Specialized Models: Time Series and Survival Analysis
These courses teach the following machine learning subjects:
- Python
- Ridge Regression
- Clustering Techniques
- Statistical Hypothesis Testing
This certificate takes seven months to complete with approximately three hours of work each week. However, learners can progress at their own pace as their schedule permits.
Though offered in collaboration with IBM, the coursework involves hands-on projects that develop widely applicable skills not specific to IBM software and products.
Prerequisites
Learners should be familiar with Python and have a solid understanding of statistics and linear algebra. However, since the courses start with foundational concepts and theory before taking on more complex topics, this certification is beginner friendly.
The cost 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 seven-month duration.
Best for: those with some programming experience seeking a low time and cost commitment in developing cloud-agnostic machine learning skills.
Cost: $280 USD
Microsoft Certified Azure AI Engineer Associate
Microsoft’s exam-based Certified Azure AI Engineer Associate certification verifies candidates’ ability to implement AI solutions using Azure Cognitive Services and Azure Applied AI services.
Prerequisites
This certification is a good starting point for beginners. Candidates should have experience using REST APIs and software development kits (SDKs) and be proficient in C# or Python.
Exam details
The exam assesses candidates knowledge and skills in the following areas:
- Planning and managing an Azure Cognitive Services solution
- Computer vision solutions
- Natural language processing solutions
- Knowledge mining solutions
- Conversational AI Solutions
Candidates pass with a score of at least 700.
To prepare for the exam, candidates have access to free, self-guided study materials. Otherwise, Microsoft offers four-day, instructor-led prep courses for a fee of $1,450. Alternatively, candidates may want to check out Udemy’s prep course for this certification, which only costs $19.99. However, the prep course includes a series of practice tests without live interaction with an instructor. So if you already have a baseline understanding of the relevant Azure products, the Udemy prep course will likely be sufficient.
Best for: Those with programming and API experience looking for a cost-effective way to specialize in Azure solutions
Cost: $165 USD
Machine Learning Specialization Certificate
Stanford University, in collaboration with DeepLearning.AI, offers this beginner-friendly, self-paced Machine Learning Specialization certificate online through Coursera. By the end of this program, learners will understand:
- Basic coding
- Core math skills, such as arithmetic and algebra
- The fundamentals of machine learning
- Supervised learning
- Unsupervised learning
- Machine learning best practices
- How to use machine learning techniques to build AI-powered applications
To obtain this certification, one must complete three courses:
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, Reinforcement Learning
It takes approximately two months to earn this certification, and you should expect to dedicate about nine hours each week.
The cost 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 two-month duration. To receive the certificate of completion, learners must pay an additional $79.
Best for: beginners who want to acquire a solid grasp of basic math and coding skills before learning the fundamentals of machine learning.
Cost: $80 USD
Which machine learning certification is right for you?
The right machine learning certification will depend on a range of factors:
- Your experience level
- Whether you want general or specialized ML knowledge for a particular company
- Whether you prefer a course-based or exam-based certification
- Time and financial constraints
Experience level
If you’re interested in exploring and trying out machine learning, your best options will be:
- Machine Learning on Google Cloud Specialization
- IBM Machine Learning Professional Certificate
- Machine Learning Specialization Certificate offered by Stanford and DeepLearning.AI
Those with some programming experience under their belt should try out these intermediate certifications:
- AWS Machine Learning – Specialty
- Cornell University Machine Learning Certificate
- Microsoft Certified Azure AI Engineer Associate
The most advanced certification is the Google Cloud Machine Learning Engineer Professional Certificate.
Generalist vs specialist
Most of the certifications here are specific to a particular cloud provider. However, the following provide learners with general cloud-agnostic foundational knowledge:
- Cornell University Machine Learning Certificate
- IBM Machine Learning Professional Certificate
- Stanford/DeepLearning.AI Machine Learning Specialization Certificate
Exam-based vs course based
Some certifications here require learners to complete courses without the pressure of one exam. If you’re averse to tests, these course-based certifications are worth checking out:
- Cornell University Machine Learning Certificate
- IBM Machine Learning Professional Certificate
- Machine Learning on Google Cloud Specialization
- Machine Learning Specialization Certificate offered by Stanford and DeepLearning.AI
The benefit of course-based certifications is that learners have more structured learning paths, and the skills acquired with each course build on one another.
Exam-based certifications, on the other hand, allow learners more freedom in terms of how to prepare. Plus, the certification process involves one key step: taking the exam. In this way, recipients can move on to the next certification or career milestone more quickly.
Time and money
The amount of time and financial resources required to obtain a certification is no small factor in choosing the right certification.
In terms of time, if you want to get the certification over and done within a shorter period of time, then one of the exam-based options might be better for you. Microsoft Certified Azure AI Engineer Associate takes four to seven days to prepare and is the cheapest exam-based option at only $165.
The Cornell Cornell University Machine Learning Certificate is least advantageous in terms of cost and time, as it’s the most expensive and meets synchronously. However, it’s a great option for those who want to work directly with highly knowledgeable university faculty in an interactive setting.
Costing only $80 and taking only two months to complete, the Stanford/DeepLearning.AI Machine Learning Specialization Certificate is the least expensive and most time-effective option here.
A machine learning certification is worthwhile
Fortunately, if you’re currently employed in a machine learning role or a related field, your employer will likely subsidize at least part of your certification. For example, for Amazon certification recipients, 65 percent of employers are at least partially subsidizing their employees’ certifications.
Regardless of experience level, learning format preferences, schedule, and budget, there is a machine learning certification for everyone who’s interested in pursuing a career in this hot field. A machine learning certification is worth pursuing because it shows your commitment to the field. It also certifies your knowledge of machine learning theories, methods, and best practices that you can apply on the job.
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