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Machine learning (ML) is a rapidly evolving industry and one of the most in-demand skillsets for programmers, data scientists, and aspiring artificial intelligence professionals. Certifications—a formal recognition of your ML expertise by a reputable certifying body—can help you stand out in a competitive job market, driving many ML professionals to seek machine learning certifications from Google, IBM, AWS, and other top AI companies.
Typically, ML certification programs are taught by industry experts or professors and come with course material in the form of videos, quizzes, assignments, and readings, all culminating in a final certification exam—and possibly resulting in career advancement. Here are my top picks for the best machine learning certifications:
- Understanding Machine Learning: Best for Understanding ML Basics
- Machine Learning Specialization: Best for Developing ML Practical Skills
- IBM Machine Learning Professional Certificate: Best for Mastering Data-Centric ML
- Microsoft Azure Data Scientist Associate Certification: Best for Showcasing ML Expertise in Microsoft Azure
- AWS Certified Machine Learning: Best for Validating ML Expertise in AWS
- Google Professional Machine Learning Engineer: Best for Demonstrating ML Skills Using Google Cloud Solutions
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Machine Learning Certification Comparison Chart
When selecting a machine learning certification, it’s important to take into account the certifying body, duration of the program, and course fee. You should also make sure it aligns with your artificial intelligence and ML experience level and offers study resources and techniques that fit your learning style.
| AI Certification | Certifying Body | Level | Duration | Cost | Recommended Prerequisites |
|---|---|---|---|---|---|
| Understanding Machine Learning | DataCamp | Beginner | Two hours | $13 per month | No prerequisites |
| Machine Learning Specialization | Stanford and Deeplearning.AI | Beginner | Two months(10 hours per week) | $49 per month | Basic coding knowledge |
| IBM Machine Learning Professional Certificate | IBM | Intermediate | Three months (10 hours per week | $39 per month | Python programming skills Knowledge in statistics and linear algebra |
| Microsoft Azure Data Scientist Associate Certification | Microsoft | Intermediate | 100 minutes | $165 | Knowledge in Azure ML and MLflow |
| AWS Certified Machine Learning | AWS | Advanced | Three hours (65 questions) | $300 | At least two years of hands-on ML experience |
| Google Professional Machine Learning Engineer | Google Cloud | Advanced | Two hours(50-60 questions) | $200 + tax | More than three years of industry experience |
TABLE OF CONTENTS
Top 6 Machine Learning Certificates and Certifications
Machine learning certificates provide valuable skills for anyone seeking a career in artificial intelligence and data science. Beginner courses introduce the basics of machine learning, statistical concepts, data analysis, and Python programming, while intermediate courses cover more in-depth lessons on ML, AI models, deep learning, and more. Advanced learners can validate their expertise in machine learning algorithms, model tuning, and real-world ML applications.
Our list covers both certificates and certifications. Certificates verify that you completed a course or training, while certifications are industry-recognized credentials demonstrating your specific skillset and knowledge.
Understanding Machine Learning
Best for Understanding ML Basics | Beginner Level
This non-technical course offered by DataCamp covers the fundamentals of machine learning and its relation to data science and AI. It discusses machine learning jargon, different types of ML, evaluating ML models, and deep learning use cases. DataCamp offers the first chapters for free, so you’ll be able to learn about the basics of machine learning, relevant fields, and the process of an ML workflow. Paid subscribers will be able to access in-depth ML lessons and explore deep learning applications such as computer vision and natural language processing (NLP).
Why I Picked It
I chose this course because it offers clear, concise, and easy-to-understand lessons for beginners looking for a short course on machine learning basics with no coding involved. It provides hands-on experience to reinforce learning and real-world examples so learners can get a better understanding of how ML is used in various use cases. The course is also accessible for beginners who want to explore the lessons before committing financially, as DataCamp offers free chapters. You need to upgrade to DataCamp’s paid version, which starts at $13 per month, billed annually.
Skills Acquired
- Basic knowledge of machine learning
- Supervised vs unsupervised learning
- Machine learning workflow process
- Machine learning models
- Deep learning use cases
Key Course Details
The following is a high-level overview of what you need to know about course requirements, fees, duration, format, and content:
Course Requirements
- No prerequisites
Course Fee, Duration, and Format
- Free (Chapter One)
- Starts at $13 per month, billed annually for full access
- Two hours to complete
- Self-paced online learning via DataCamp
Course Content and Assessments
- What is machine learning?
- Machine learning models
- Deep learning
The remaining two chapters are only accessible to paid users. These chapters include assessments and exercises on different types of learning, hyperparameter tuning, sentiment analysis, spotting ML bias, and more.
Machine Learning Specialization
Best for Developing ML Practical Skills | Beginner Level
Stanford and DeepLearning.AI offer this beginner-friendly machine learning specialization that introduces key artificial intelligence concepts and teaches how to build and train ML models using Python. Unlike most other certification programs, this three-course ML specialization targets total beginners, requiring only high school math knowledge and basic coding skills.
With its low barrier to entry and immersive, comprehensive, hands-on learning experience, the Machine Learning Specialization provides the perfect option for aspiring AI professionals looking to break into the field.
Why I Picked It
This certification is ideal for beginners planning to break into machine learning, data science careers, software development, and other relevant fields. It allows you to master fundamental AI concepts and develop practical machine-learning skills through a comprehensive program divided into three courses taught by AI visionary Andrew Ng. After completing the program, learners can earn a shareable certificate awarded by Stanford Online and DeepLearning.AI, known to offer industry-recognized online AI programs.
Skills Acquired
- Building ML models with NumPy and scikit-learn
- Applying best practices for ML development and use
- Building and training a neural network on TensorFlow
- Creating recommender systems with a collaborative filtering approach
Key Course Details
The following is a high-level overview of what you need to know about course requirements, fees, duration, format, and content:
Course Requirements
- Basic coding (for loops, functions, if/else statements)
- High school-level math (arithmetic, algebra)
Course Fee, Duration, and Format
- $49 per month
- Two months at 10 hours a week
- Self-paced online learning via Coursera
Course Content and Assessments
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, Reinforcement Learning
IBM Machine Learning Professional Certificate
Best for Mastering Data-Centric ML | Intermediate Level
IBM’s Machine Learning Professional Certificate is designed to help intermediate-level tech professionals master practical, up-to-date machine learning concepts and skills that they can apply to the analysis of real-world datasets. Through six courses, you’ll learn exploratory data analysis for machine learning, supervised ML, unsupervised ML, and deep/reinforcement learning. All of this culminates in a final capstone project where you’ll train a neural network, construct regression models, create recommender systems in Python, and more.
Why I Picked It
IBM’s six-course professional certificate is an ideal program for scientists, business analysts, and software developers who want to improve their analytical skills in data science and machine learning. However, the certificate is also highly useful to ML professionals aspiring to a variety of data-focused roles. It stands out for its focus on real-world skills that allow AI professionals to prepare for a career in machine learning. Through this professional certificate, you will master current and in-demand practical ML skills from a reputable certifying body and leading AI company.
Skills Acquired
- Practical ML skills and knowledge experts use
- Knowledge of KNN, PCA, and non-negative collaborative filtering
- Comparing and contrasting ML algorithms using Python
- Predicting course ratings by training a neural network
- Constructing regression and classification models
Key Course Details
The following is a high-level overview of what you need to know about course requirements, fees, duration, format, and content:
Course Requirements
- Python programming skills
- Knowledge of statistics and linear algebra
Course Fee, Duration, and Format
- $39 per month
- Three months at 10 hours a week
- Self-paced online learning via Coursera
Course Content and Assessments
- Exploratory Data Analysis for Machine Learning
- Supervised Machine Learning: Regression
- Supervised Machine Learning: Classification
- Unsupervised Machine Learning
- Deep Learning and Reinforcement Learning
- Machine Learning Capstone
At the end of the six-course program, you need to complete the capstone project, which includes developing a final presentation and evaluating your peers’ projects.
Microsoft Azure Data Scientist Associate Certification
Best for Showcasing ML Expertise in Microsoft Azure | Intermediate Level
The Microsoft Azure Data Scientist Associate Certification is a 100-minute, online exam for intermediate data scientists and developers familiar with using data science and machine learning techniques to develop and run machine learning workloads on Azure. The skills tested in the exam include machine learning solution design and prep, model training, data exploration, model deployment, and model retraining—all about Microsoft Azure. To help you prepare for the exam, Microsoft offers 13 hours of course material, 100 exam prep videos, a practice assessment, and an exam sandbox where you can practice answering questions in the same interface you’ll see during exam day.
Why I Picked It
The Microsoft Azure Data Scientist Associate certification allows you to stand out in a competitive AI job market. Earning this certification demonstrates your ability to manage data preparation, train and deploy models, and monitor ML solutions with Python, Azure Machine Learning, and MLflow. Professional developers, data scientists, and ML engineers who want to validate their abilities to deploy and maintain machine learning workloads on Azure will find this credential valuable in positioning themselves as experts in data science and machine learning.
Skills Acquired
- Creating suitable working environments for data science workloads
- Training machine learning models
- Implementing pipelines
- Preparing for production
- Managing, deploying, and monitoring scalable machine learning sol
Key Course Details
The following is a high-level overview of what you need to know about certification requirements, fees, duration, format, and content:
Certification Exam Requirements
- Experience with Azure machine learning and MLflow
- Subject matter expertise in data science best practices
Course Fee, Duration, and Format
- $165
- 100 minutes to complete the assessment
- Online-proctored exam
Certification Exam Content
- Designing and preparing a machine learning solution
- Exploring data and training models
- Preparing a model for deployment
- Deploying and retraining a model
AWS Certified Machine Learning
Best for Validating ML Expertise in AWS | Advanced Level
The AWS Certified Machine Learning Speciality is a three-hour exam that validates your ability to build, train, tune, and deploy machine learning models on AWS. The exam can be taken in-person or online and will test how well you can state the intuition behind basic ML algorithms, perform hyperparameter optimization, and follow model training and deployment best practices. For those looking to prepare, check out the AWS Skill Builder, where you’ll find helpful course material and practice questions.
Why I Picked It
This certificate is for professional developers and data scientists who have worked with ML in AWS and want to validate this skillset for employers, perhaps to land a more senior data science role. I chose this certification because earning AWS Certified Machine Learning Specialty demonstrates your expertise in building, training, tuning, and deploying ML models on AWS. Additionally, gaining certification from a highly respected certifying body like AWS offers a competitive advantage for professionals seeking to stand out in the AI industry.
Skills Acquired
- Designing, deploying, optimizing, and maintaining ML solutions
- Selecting the appropriate ML approach for a business problem
- Identifying appropriate AWS services to implement ML solutions
- Implementing scalable, cost-optimized, reliable, and secure ML solutions
Key Course Details
The following is a high-level overview of what you need to know about certification requirements, fees, duration, format, and content:
Certification Exam Requirements
- At least two years of hands-on experience in ML using AWS Cloud
- Ability to express intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization, ML, and deep learning frameworks
- Ability to follow model training, deployment, and operational best practices
Course Fee, Duration, and Format
- $300
- Three hours to complete the exam
- Pearson VUE testing center or online proctored exam
Certification Exam Content
There are two types of questions on the exam, such as multiple choice and multiple response. The certification exam covers the following domains:
- Domain 1: Data Engineering
- Domain 2: Exploratory Data Analysis
- Domain 3: Modeling
- Domain 4: Machine Learning Implementation and Operations
Google Professional Machine Learning Engineer
Best for Demonstrating ML Skills Using Google Cloud Solutions | Advanced Level
The Google Professional Machine Learning Engineer Certificate is an exam for ML professionals who build and optimize ML models using Google Cloud technologies and best practices. Sections of the exam include architecting low-code ML solutions, collaborating with teams to manage models, scaling prototypes into ML models, serving and scaling ML models, automating ML pipelines, and monitoring ML solutions. Google offers learning materials in their Machine Learning Engineer Learning Path, where you’ll find 14 courses and one lab. Google also offers an eight-course series through Coursera for preparing for the exam.
Why I Picked It
Professional Machine Learning Engineer allows working ML engineers and developers to showcase their knowledge of Google Cloud technologies, ML engineering best practices, and ML techniques. By earning this industry-recognized certification, you demonstrate a strong understanding of Google Cloud’s ML ecosystem and its real-world use cases, making you a valuable asset for any organization looking to apply Google Cloud to ML workflows.
Skills Acquired
- Designing low-code AI solutions
- Scaling prototypes into ML models
- Automating and orchestrating ML pipelines
- Collaborating within and across teams
- Serving and scaling models
- Monitoring AI solutions
Key Course Details
The following is a high-level overview of what you need to know about certification requirements, fees, duration, format, and content:
Certification Exam Requirements
- Three-plus years of industry experience, including at least one year of designing and managing solutions using Google Cloud
Course Fee, Duration, and Format
- $200 plus tax where applicable
- Two hours (50-60 items)
- Onsite-proctored exam at a testing center or online-proctored exam
Certification Exam Content
- Section 1: Architecting Low-Code AI Solutions
- Section 2: Collaborating Within and Across Teams to Manage Data and Models
- Section 3: Scaling Prototypes Into ML Models
- Section 4: Serving and Scaling Models
- Section 5: Automating and Orchestrating ML Pipelines
- Section 6: Monitoring AI Solutions
Key Benefits of Earning a Machine Learning Certification
Below are some of the best reasons to get your machine learning certification, ranging from launching an ML career to staying current with ML techniques.
Validate Your Machine Learning Skills and Knowledge
Machine learning certifications demonstrate your ability to apply your skills to real-world problems, making you a valuable asset to your employers. Telling a potential employer that you have extensive experience developing ML solutions in Google Cloud is one thing, but showing them a certificate from Google provides you an edge. With certificate programs or online courses, you can pass assessments and earn certificates from reputable institutions that prove you have the knowledge and skills to stand out in the ML and data science job market.
Start a Career in Machine Learning
Machine learning certifications can be a valuable tool for launching a career in the field. If you’re not sure about the difference between ML, deep learning, and generative AI, some certifications are designed to help total novices learn the basics. Introductory courses discuss the fundamentals of machine learning, practical ML skills, data analysis, and more.
Learn the Current ML Techniques, Tools, and Trends
Certificate programs provide learning materials that draw from techniques, tools, and frameworks that professional data scientists and ML engineers use in their real-world jobs. This makes a program a valuable option for even the seasoned ML professional looking to update their skill set to match the current best practices while gaining recognition for it.
Learn at Affordable Prices Compared to College Degrees
Compared to college degrees in computer science or data analysis, which cost over six figures, these certification programs enable you to earn credit for machine learning skills and gain a respected credential for as low as a couple hundred dollars.
Go at Your Own Pace
Certificate programs enable you to prepare for certification exams and take machine learning courses at your speed, from the comfort of your home. This makes them ideal for busy tech professionals.
How to Choose the Best Machine Learning Certification for You
When deciding on a machine learning certificate, it’s important to take into account the following considerations:
- Price: Find a certification that works with your budget.
- Prerequisites: Make sure the courses and/or exams cater to your specific experience level.
- Specialty: Some programs are designed for professionals familiar with a specific solution (for example, Google’s certificate is for building ML models using G Suite technologies), so pick a program that focuses on the tools you plan to work with.
- Learning Materials: Whether you’re learning a new ML subject from scratch or filling in some gaps to prepare for the exam, check out the study materials and online courses to see if they are sufficient for your needs.
- Career Goals: Pick a certificate program that will help you land the job or get the promotion you desire.
In sum, the best machine learning certification will be one that fits your budget, experience level, and timeline, and helps you achieve your specific machine learning and AI career goals.
How I Evaluated Machine Learning Certifications
To evaluate the various machine learning certifications on the market and find the best ones, I looked at the cost of the certification, the reputation of the certifying body, the quality of learning materials, and the accessibility.
- Cost: I looked into how much the machine learning certification costs in terms of time commitment and exam and course fees.
- Reputation of the Certifying Body: The credibility of the certifying body came into my consideration since they’re likely to provide cutting-edge courses and will look great on your resume to future employers.
- Quality of Course/Exam Preparation Material: I examined the learning materials to see if they would adequately prepare an aspiring machine learning professional for the jobs they’re applying to. I focused on hands-on learning experiences like projects and assignments.
- Accessibility: To assess the accessibility, I checked out how easy it was for users to prepare for and take the exam from home, as well as factors like access to course instructors and self-paced learning.
Frequently Asked Questions (FAQs)
To prepare for machine learning certification, start by reading the exam’s prerequisites. If you feel you fall short, check to see if the certifying body offers preparation materials that will help you round out your knowledge. Most programs offer a learning path that includes online videos, practice questions, and readings. Some course-based programs even offer feedback on your assignments as well as live discussions hosted by course facilitators.
The ML certification that you feel most confident passing should be your first one. Take the AWS credential exam, for instance, if you have prior experience working with AWS on ML projects. Take beginner-friendly certificates initially if you’re a complete novice. That’ll ensure you get the credentials you deserve as quickly as possible.
Though it may be difficult without on-the-job experience, you can still get a machine learning job with just a certificate by using these tactics:
• Learn ML in Public: Take on machine learning and coding projects and write about your findings and the process on social media to position yourself as an expert.
• Pretend You Have the Job: Find areas of interest where you can apply machine learning to improve some aspect, then create an ML model or solution and share it.
• Focus on Smaller Companies: Startups will be more likely to hire you for a niche ML skill than big companies, who generally want people with advanced degrees.
• Prepare for Interviews: Practice talking about the machine learning projects you’ve worked on, the latest technologies and trends, and your relevant technical skills.
If you do these activities consistently, you’ll have a greater chance of landing a machine learning job with nothing but your certification. At any rate, your prospects will be better than if you merely sent out resumes highlighting your certificate and in-course projects.
Bottom Line: Best Machine Learning Certifications
A machine learning certification is one of the most affordable and time-efficient ways to validate your machine learning knowledge and expertise—and get hired in a lucrative new role. Once you’ve completed the exam, you can list the formal certificate on your LinkedIn profile and resume to help you land machine learning jobs or get a promotion. With many machine learning certifications and courses available, the best option offers a comprehensive curriculum, practical skills, and an industry-recognized credential. My recommendation lists the top machine learning programs, but consider your long-term career goals and the specific ML area you want to pursue before deciding which option is right for you.
If you’re looking for additional AI certifications to help you stand out, check out our list of the top AI certifications.