Best Machine Learning Certifications in 2026
You're researching machine learning certifications because you need proof you can do this work. Maybe you're switching careers into tech. Maybe you're a developer adding ML to your skill set. Maybe you already work with data and want to level up. Whatever brings you here, you've discovered hundreds of certifications exist, and figuring out which one actually advances your goals takes work.
This guide compares 11 machine learning certifications that can genuinely help your career. We'll show you what each costs, how long it takes, what you'll learn, and who it's for. You'll also learn when certifications help versus when they don't, and how to choose based on where you are right now.
Here's what we'll cover:
- Certification vs. certificate (they're different)
- Best certifications for career switchers
- Best certifications for ML engineers
- Best certifications for cloud practitioners (AWS, Google Cloud, Azure)
- Best certifications for Databricks platform specialists
- Strategic certification paths
- How to make your decision
Let's find the certification that matches your goals.
Certification vs. Certificate: What's the Difference?
These terms get used interchangeably, but they mean different things.
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Certifications require passing an exam. You study, take a proctored test, and get credentials proving you passed a standardized assessment. Examples include AWS Certified Machine Learning Engineer or Google Cloud Professional Machine Learning Engineer.
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Certificates mean you completed a course or program. Andrew Ng's Machine Learning Specialization gives you a certificate when you finish all coursework and projects. No exam required, just demonstrated learning through assignments.
Both have value. Certifications often carry more weight for specific job requirements because they involve standardized testing. Certificates show you invested time in structured learning and built practical skills through projects. This guide covers both types because the best choice depends on your goals and learning style.
Best Machine Learning Certifications for Career Switchers
These certifications build strong foundations without requiring prior ML experience.
1.Machine Learning in Python (Dataquest)
Dataquest's Machine Learning skill path takes a learn-by-doing approach where you write code from day one and build portfolio projects employers care about. Unlike video-heavy courses, you spend your time writing Python code and solving real ML problems.
- Cost: Up to \$49/month (often on sale)
- Time: Self-paced, typically 3-6 months
- Prerequisites: Basic Python knowledge
- What you'll learn: Machine learning fundamentals, supervised learning algorithms, feature engineering, model evaluation, building production-ready ML systems
- Format: Certificate of completion with portfolio projects
- Best for: Hands-on learners who learn best by doing
You'll work with real datasets throughout the entire path. The curriculum covers regression, classification, decision trees, random forests, and neural networks. Each lesson includes coding exercises where you implement algorithms yourself before using libraries like scikit-learn.
The biggest advantage is the portfolio projects you build. These aren't toy problems but substantial projects you can show employers: predicting housing prices, building recommendation systems, and creating ML models that solve business problems. You'll also learn how to evaluate models properly, handle imbalanced data, and prepare ML systems for production.
When you complete the path, you get a certificate of completion and a portfolio of projects you can add to your resume and discuss in interviews. The learn-by-doing approach means you're not just watching videos about machine learning but building the skills employers need.
2. Machine Learning Specialization (DeepLearning.AI + Stanford)
Andrew Ng's Machine Learning Specialization is the gold standard for learning ML fundamentals. Over 4.8 million people have taken his courses, and this three-course program updates his pioneering work with modern tools and techniques.
- Cost: \$59/month on Coursera (~\$177 for 3 months)
- Time: 3 months at 5 hours per week
- Prerequisites: Basic Python, high school math
- What you'll learn: Supervised learning (regression and classification), neural networks, decision trees, recommender systems, best practices for ML projects
- Format: Certificate (course completion)
- Best for: Anyone wanting deep ML fundamentals
Ng teaches with a specific approach that makes complex concepts stick. He starts with visual intuition, then shows you the code, then explains the underlying math. This method helps you understand not just what to do, but why it works.
The specialization covers regression, classification, neural networks, and key algorithms like decision trees. You'll also learn how professionals approach ML problems, from framing the right questions to avoiding common pitfalls.
3. Machine Learning Professional Certificate (IBM)
The IBM Machine Learning Professional Certificate gives you a complete tour of machine learning techniques through six courses. You'll cover supervised learning, unsupervised learning, deep learning, reinforcement learning, and specialized topics like time series analysis and survival analysis.
- Cost: \$59/month on Coursera (~\$354 for 6 months)
- Time: 6 months
- Prerequisites: None, but intermediate knowledge recommended
- What you'll learn: Supervised learning, unsupervised learning, deep learning, reinforcement learning, time series analysis, survival analysis
- Format: Certificate (course completion)
- Best for: Career switchers wanting comprehensive ML training
Each course includes hands-on labs where you work with real datasets using Python libraries like NumPy, pandas, scikit-learn, and TensorFlow. By the end, you'll have a portfolio of projects showing you can apply ML techniques to business problems.
IBM provides a digital badge when you complete the program, plus access to career resources including mock interviews and resume support. The certificate appears on many job postings for ML roles, and employers recognize IBM's training as rigorous and practical.
For a broader foundation that includes the data science skills surrounding machine learning, consider Dataquest's Data Scientist career path. It's a learn-by-doing program with a certificate of completion and guided projects that prepare you for the full scope of data science work, including machine learning.
Best Machine Learning Certifications for ML Engineers
When you're ready to go deeper, these certifications teach advanced techniques for building production ML systems.
4. Deep Learning Specialization (DeepLearning.AI)
After mastering ML basics, the Deep Learning Specialization teaches you to build neural networks that power modern AI. This five-course program from Andrew Ng covers the techniques behind computer vision, natural language processing, and speech recognition systems.
- Cost: \$59/month on Coursera (~\$295 for 5 months)
- Time: 5 months
- Prerequisites: ML fundamentals (Machine Learning Specialization recommended first)
- What you'll learn: Neural networks and deep learning, CNNs for images, RNNs for sequences, transformers, strategies for improving models
- Format: Certificate (course completion)
- Best for: Building cutting-edge AI applications
You'll implement algorithms from scratch before using frameworks. This deeper understanding helps when things go wrong in real projects. The hands-on programming assignments give you experience with convolutional neural networks for images, recurrent networks for text and time series, and attention mechanisms that power transformers.
Many employers specifically list deep learning skills in ML engineer job descriptions. This specialization appears on those requirements more than almost any other credential.
5. AI Engineering Professional Certificate (IBM)
The IBM AI Engineering Professional Certificate teaches you to build AI systems from scratch through six courses. You'll learn machine learning techniques, deep learning frameworks like TensorFlow and PyTorch, and how to deploy models that solve real business problems.
- Cost: \$59/month on Coursera (~\$236-354 for 4-6 months)
- Time: 4-6 months
- Prerequisites: Basic Python, ML fundamentals
- What you'll learn: Deep learning with TensorFlow and PyTorch, computer vision, natural language processing, model deployment
- Format: Certificate (course completion)
- Best for: Becoming an AI engineer
IBM refreshed this program in March 2025 with new generative AI content, so you're learning current, relevant skills. You'll work with both TensorFlow and PyTorch, giving you flexibility for different projects. The program includes hands-on work with computer vision and natural language processing, two of the most in-demand AI specializations.
By the end, you'll have a portfolio showing you can create AI applications. This matters because employers want to see what you can build, not just that you completed courses.
6. TensorFlow Developer Professional Certificate (DeepLearning.AI)
The DeepLearning.AI TensorFlow Developer Professional Certificate teaches you to build AI applications using TensorFlow, one of the most widely used machine learning frameworks. This four-course program focuses on practical skills you can apply immediately.
- Cost: \$59/month on Coursera (~\$177-236 for 3-4 months)
- Time: 3-4 months
- Prerequisites: Python experience, high school math (ML knowledge helpful but not required)
- What you'll learn: Building neural networks with TensorFlow, computer vision with CNNs, NLP systems, time series forecasting
- Format: Certificate (course completion)
- Best for: Developers wanting practical TensorFlow skills
You'll complete 16 Python programming assignments that give you hands-on experience with real-world problems. The program covers computer vision for image recognition, natural language processing for text analysis, and time series forecasting for predictions.
TensorFlow powers AI systems at Google, Airbnb, Uber, and countless other companies. Learning this framework opens doors to many ML engineering roles.
Best Machine Learning Certifications for Cloud Practitioners
If your company uses AWS, Google Cloud, or Azure, these certifications prove you can build ML systems at scale on each platform.
7. AWS Certified Machine Learning Engineer - Associate (MLA-C01)
AWS launched its Certified Machine Learning Engineer certification in late 2024 as the middle tier in their ML pathway. It validates your ability to implement ML workloads in production and operationalize them on AWS.
- Cost: \$150 exam fee
- Time: Preparation varies (certification launched late 2024)
- Prerequisites: 1+ year hands-on ML experience on AWS
- What you'll learn: Data preparation for ML, model training and evaluation, deployment and operations, monitoring and optimization
- Format: Certification (exam-based)
- Expiration: 3 years
- Best for: ML engineers working with AWS
This certification focuses on practical engineering skills rather than just theory. You'll need to understand data preparation, model training with SageMaker, deployment strategies, and monitoring production systems.
AWS recommends at least one year of hands-on experience with ML on their platform before taking this exam. The certification sits between the foundational AI Practitioner level and the advanced Machine Learning Specialty (which retires March 31, 2026).
8. Google Cloud Professional Machine Learning Engineer
The Google Cloud Professional Machine Learning Engineer certification proves you can build production ML systems at scale using Google's platform. This advanced certification covers the full ML lifecycle from data preparation through deployment and monitoring.
- Cost: \$200 exam fee
- Time: 100-150 hours preparation
- Prerequisites: 3+ years industry experience (1+ with Google Cloud recommended)
- What you'll learn: ML solution design, Vertex AI, model training and deployment, ML operations
- Format: Certification (exam-based)
- Expiration: 2 years
- Best for: ML engineers on Google Cloud Platform
You'll need to understand Vertex AI, Google's unified ML platform. The exam includes scenario-based questions where you solve real problems by choosing the right tools and architectures. Google recently updated the exam to include generative AI content, so you're learning the latest techniques.
The two-hour exam contains 50-60 questions testing your ability to design ML solutions, collaborate across teams, scale prototypes into production, and automate ML pipelines.
9. Microsoft Certified: Azure Data Scientist Associate (DP-100)
The Microsoft Certified: Azure Data Scientist Associate certification validates your ability to apply data science and machine learning techniques in the Azure environment. The DP-100 exam tests both theoretical knowledge and practical implementation skills.
- Cost: \$165 exam fee
- Time: Varies, 4-day official course available
- Prerequisites: Experience with Azure Machine Learning and MLflow
- What you'll learn: Data science on Azure, model training and deployment, ML operations with Azure tools
- Format: Certification (exam-based)
- Expiration: 12 months (free renewal)
- Best for: Data scientists working with Azure
You'll need to understand the full ML lifecycle on Azure, including data preparation, model training, deployment, and monitoring. The exam covers Azure Machine Learning, MLflow integration, and Azure AI Services.
This certification requires annual renewal, but Microsoft makes it easy and free. You complete a quick online assessment to show your knowledge stays current.
Best Machine Learning Certifications for Platform Specialists
Databricks is becoming the standard for enterprise ML. These certifications prove you can build production systems on their unified analytics platform.
10. Databricks Certified Machine Learning Associate
The Databricks Certified Machine Learning Associate certification validates your ability to perform basic machine learning tasks using Databricks and its tools, including AutoML, MLflow for experiment tracking, and Unity Catalog for data governance.
- Cost: \$200 exam fee (often 50% discounts available)
- Time: 90 minutes exam
- Prerequisites: 6+ months hands-on experience with Databricks
- What you'll learn: Databricks ML basics, AutoML, MLflow, Unity Catalog, feature engineering, model deployment
- Format: Certification (exam-based)
- Expiration: 2 years
- Best for: Data scientists and ML engineers entering the Databricks ecosystem
The 90-minute exam contains 48 multiple-choice questions. You need a 70% score to pass. Databricks often offers 50% discount vouchers through their learning festivals, webinars, and partner programs, bringing the cost down to \$100 if you time it right.
This certification serves as your foundation for working with Databricks. Many companies are adopting Databricks for their ML infrastructure because it combines data engineering, machine learning, and analytics in one platform.
11. Databricks Certified Machine Learning Professional
The Databricks Certified Machine Learning Professional certification validates your ability to design, implement, and manage enterprise-scale machine learning solutions on Databricks. This advanced certification tests your knowledge of production ML systems, not just model building.
- Cost: \$200 exam fee
- Time: 120 minutes exam
- Prerequisites: 1+ year hands-on experience with Databricks ML
- What you'll learn: Advanced ML pipelines with SparkML, distributed training, Feature Store, MLflow advanced features, Lakehouse Monitoring
- Format: Certification (exam-based)
- Expiration: 2 years
- Best for: Experienced ML engineers on Databricks
You'll need to understand distributed training and hyperparameter tuning at scale, MLOps practices including automated retraining workflows, and monitoring strategies using Lakehouse Monitoring for drift detection. The 120-minute exam contains 60 questions covering model development, deployment, and operations.
This certification demonstrates you can handle the complexity of real enterprise ML systems.
Machine Learning Certification Comparison Table
Now that you've read about each certification in detail, here's everything side by side. This makes it easier to compare your top choices and see how they stack up on cost, time commitment, and difficulty level.
| Certification | Cost | Time | Level | Format | Best For |
|---|---|---|---|---|---|
| Dataquest ML Path | Up to \$49/mo | 3-6 months | Beginner-Intermediate | Certificate | Hands-on learners building portfolio projects |
| Machine Learning Specialization | ~\$177 | 3 months | Beginner | Certificate | Anyone learning ML fundamentals |
| IBM ML Professional Certificate | ~\$354 | 6 months | Beginner-Intermediate | Certificate | Career switchers wanting comprehensive training |
| Deep Learning Specialization | ~\$295 | 5 months | Intermediate | Certificate | Building cutting-edge AI applications |
| IBM AI Engineering Professional Certificate | ~\$236-354 | 4-6 months | Intermediate | Certificate | Becoming an AI engineer |
| TensorFlow Developer Professional Certificate | ~\$177-236 | 3-4 months | Intermediate | Certificate | Practical TensorFlow skills |
| AWS ML Engineer - Associate | \$150 | Varies | Associate | Certification | ML engineers on AWS |
| Google Cloud Professional ML Engineer | \$200 | 100-150 hours | Advanced | Certification | Experienced ML engineers on GCP |
| Azure Data Scientist Associate | \$165 | Varies | Associate | Certification | Data scientists working with Azure |
| Databricks ML Associate | \$200 | 90 min exam | Associate | Certification | Entering Databricks ecosystem |
| Databricks ML Professional | \$200 | 120 min exam | Advanced | Certification | Enterprise-scale ML on Databricks |
Strategic Certification Paths
The best approach to machine learning certifications isn't a matter of collecting them all. Here are some proven paths that build naturally on each other:
| Path | Timeline | Total Cost | Best For |
|---|---|---|---|
|
Foundation Path 1. Dataquest ML Path or Machine Learning Specialization 2. Deep Learning Specialization |
6-8 months | ~\$294-472 | Complete beginners building core ML knowledge |
|
Cloud Practitioner Path 1. Dataquest ML Path or Machine Learning Specialization 2. Cloud certification (AWS/GCP/Azure) |
3-6 months | ~\$150-377 | Developers adding ML to existing cloud skills |
|
Production Engineer Path 1. Dataquest ML Path or Machine Learning Specialization 2. Deep Learning Specialization 3. Cloud or Databricks certification |
10-12 months | ~\$444-872 | Building and deploying production ML systems |
|
Platform Specialist Path 1. Databricks ML Associate 2. Databricks ML Professional |
6-12 months | \$400 | Companies using Databricks infrastructure |
Path selection tips:
- Start with foundations if you're new to ML (Dataquest ML Path or Machine Learning Specialization)
- Add cloud skills after fundamentals if your company uses a specific platform
- Stack strategically by choosing certifications that build on previous knowledge
- Consider your timeline when selecting between faster exam-based certifications and longer course-based certificates
How to Choose the Right Machine Learning Certification
Start by being honest about where you are right now. Some certifications assume you already know Python and have built ML models before. Others start from scratch and teach you the fundamentals. Jumping into an advanced certification when you're still learning the basics leads to frustration and wasted money.
Think about what you want to do with machine learning. Building models from scratch requires different skills than deploying models someone else built. Leading ML projects needs different knowledge than writing the code. Different certifications prepare you for different roles.
Consider the practical constraints:
- Time commitment: Ranges from 40 hours (some exams) to 200+ hours (comprehensive programs)
- Cost: Between \$150 (single exams) and \$500+ (multiple courses)
- Your schedule: Can you dedicate 5-10 hours per week?
- Learning style: Do you learn better through courses or exam preparation?
Check if employers care about the certification you're considering. Browse job postings in your target field and note which credentials they mention. Certifications from AWS, Google Cloud, Microsoft, IBM, and Databricks typically get recognized because these companies power most enterprise ML systems.
When You Don't Need a Certification
Certifications aren't always necessary. If you already have strong experience building ML systems, a portfolio of real projects might matter more than certificates. Many employers care more about what you can do than what credentials you hold.
Certifications work best when you're:
- Breaking into a new field and need credibility
- Filling specific knowledge gaps
- Working at companies that value formal credentials
- Trying to stand out in a competitive job market
They work less well when you're:
- Already established in ML with years of experience
- At a company that promotes based on projects rather than credentials
- Learning just for personal interest
Sometimes spending 100 hours building a portfolio project helps your career more than studying for an exam.
What Happens After Getting Certified
Update your LinkedIn profile and resume with your new credential. If the certification comes with a digital badge, display it. But don't just list the certification. Mention specific skills you gained that relate to jobs you want.
Build on what you learned by using those skills in real situations. Create a small project that applies your new knowledge. Contribute to an open-source project or write about your experience. Showing both a certification and real work makes you stand out more than the certificate alone.
Smart next steps:
- Add 2-3 portfolio projects demonstrating your certified skills
- Update your professional profiles (LinkedIn, GitHub)
- Join relevant communities (Slack channels, Discord servers, local meetups)
- Consider strategic stacking (adding complementary certifications)
Many professionals stack certifications strategically. Start with Dataquest's ML Path or the Machine Learning Specialization, then add the Deep Learning Specialization, then complete a cloud certification matching what your company uses. Each certification builds on previous knowledge.
Renewal requirements to track:
| Certification | Validity Period | Renewal |
|---|---|---|
| Course certificates (Coursera, Dataquest) | No expiration | N/A |
| AWS ML Engineer Associate | 3 years | Retake exam or complete recertification path |
| Google Cloud Professional ML Engineer | 2 years | 50% discount on renewal exam |
| Azure Data Scientist Associate | 12 months | Free online assessment |
| Databricks ML certifications | 2 years | Retake current version |
Making Your Decision
If you're new to machine learning, start with Dataquest's ML Path or the Machine Learning Specialization. Both build strong foundations, but Dataquest focuses on hands-on coding while Ng's course emphasizes theory. The IBM Machine Learning Professional Certificate offers a comprehensive middle ground.
If you're a developer adding ML skills, go for cloud certifications matching your tech stack. Choose AWS ML Engineer Associate for AWS environments, Google Cloud Professional ML Engineer for GCP, or Azure Data Scientist Associate for Microsoft's cloud.
If your company uses Databricks, start with the Machine Learning Associate certification and advance to Professional after gaining more experience. If you want to build and deploy models, follow this progression: foundations first (Dataquest or Machine Learning Specialization), then Deep Learning Specialization, then a cloud platform certification.
The best machine learning certification is the one you'll complete. Choose based on your current skills, available time, and career goals. Machine learning skills are becoming more valuable every year, but credentials alone won't get you hired. You need to develop real skills through hands-on practice. Dataquest's Machine Learning path offers learn-by-doing experience with guided projects you can add to your portfolio, preparing you for both certifications and the work itself.
Frequently Asked Questions
What's the difference between a machine learning certification and a certificate?
A certification requires passing an exam (like AWS Certified Machine Learning Engineer), while a certificate means you completed a course or program (like Andrew Ng's Machine Learning Specialization). Certifications involve standardized testing, while certificates demonstrate completion of structured learning and projects.
Do I need a machine learning certification to get a job?
Not always. Many employers care more about your ability to build working ML systems than formal credentials. However, certifications help when you're breaking into the field, switching careers, or applying to companies that specifically request them. They work best combined with a portfolio of real projects.
Which machine learning certification is best for beginners?
Dataquest's Machine Learning Path and the Machine Learning Specialization by DeepLearning.AI and Stanford are both excellent starting points. Dataquest emphasizes hands-on coding from day one, while Ng's course focuses on conceptual understanding. Both assume only basic Python and high school math. The IBM Machine Learning Professional Certificate is another solid beginner option.
How long does it take to get a machine learning certification?
Course-based certificates typically take 3-6 months at 5-10 hours per week. Exam-based certifications vary widely depending on your experience. Someone with no ML background might need 100-200 hours of preparation, while experienced practitioners might need 40-60 hours to review and prepare.
Are cloud-specific ML certifications worth it?
Yes, if your company uses that cloud platform. AWS, Google Cloud, and Azure certifications prove you can build production ML systems on their platforms. They're particularly valuable for ML engineers, data scientists, and cloud practitioners who work with these tools daily.
How much do machine learning certifications cost?
Course-based certificates on Coursera typically cost \$59/month (total \$177-\$354 depending on completion time). Dataquest costs up to \$49/month and is often on sale. Exam-based certifications range from \$150 (AWS ML Engineer Associate) to \$200 (Google Cloud, Databricks). Many offer discounts for students or through partner programs.
Do machine learning certifications expire?
Most exam-based certifications do expire. AWS certifications last 3 years, Google Cloud for 2 years, Azure requires annual renewal, and Databricks certifications last 2 years. Course-based certificates from Coursera and Dataquest don't expire, but the knowledge can become outdated. Most providers offer easier renewal paths than taking the original exam.
Can I get a machine learning certification without a degree?
Yes. Most certifications have no formal education requirements. They typically require hands-on experience or completion of prerequisite courses, but not a degree. Course-based certificates are especially accessible, often requiring only basic programming knowledge to start.
What's the best machine learning certification for career changers?
Start with Dataquest's ML Path, Andrew Ng's Machine Learning Specialization, or the IBM Machine Learning Professional Certificate. All three teach fundamentals from scratch and don't require prior ML experience. Dataquest focuses on hands-on coding, Ng emphasizes theory, and IBM offers comprehensive coverage. They give you the foundation needed for more advanced certifications or entry-level ML positions.
Should I stack multiple certifications or focus on one?
Start with strong foundations (Dataquest ML Path or Machine Learning Specialization), then add strategic certifications that build on that knowledge. Focus on one at a time to avoid burnout. The most effective approach is: foundations first, then cloud or platform-specific skills matching your company's tech stack, then advanced specializations.
