Artificial intelligence and machine learning are becoming essential skills for developers, data professionals, cloud engineers, automation teams, and technology leaders.
The Microsoft AI & ML Engineering Professional Certificate Guide is designed for learners who want to build practical skills in AI infrastructure, machine learning algorithms, intelligent agents, cloud-based AI workflows, model deployment, MLOps, responsible AI, and real-world AI project development.
You can start learning free by opening one of the individual courses and checking whether preview lessons are available before choosing complete enrollment.
What Will You Learn?
This professional certificate helps learners build practical skills in:
- AI and machine learning fundamentals
- AI/ML infrastructure design
- Data pipelines and data preprocessing
- Model development frameworks
- Model deployment platforms
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
- Large language models
- LLM applications
- Natural language processing
- Intelligent troubleshooting agents
- Decision-making algorithms
- Microsoft Azure AI and ML services
- MLOps workflows
- CI/CD for machine learning
- Model monitoring and optimization
- Responsible AI
- Transfer learning
- Federated learning
- Capstone project development
These skills can help learners understand how AI systems are designed, built, deployed, monitored, improved, and scaled in real business environments.
Courses Included in the Program
1. Foundations of AI and Machine Learning
Start with the foundations of AI and ML infrastructure.
This course introduces data pipelines, model development frameworks, deployment platforms, scalable AI environments, data management, data preprocessing, infrastructure architecture, and the core building blocks of modern AI systems.
2. AI and Machine Learning Algorithms and Techniques
Build stronger knowledge of machine learning methods.
This course covers supervised learning, unsupervised learning, reinforcement learning, deep learning, feature engineering, model evaluation, model optimization, statistical machine learning, generative AI, large language models, and practical AI/ML techniques for business problems.
3. Building Intelligent Troubleshooting Agents
Learn how AI-powered agents can diagnose and resolve issues.
This course explores intelligent troubleshooting agent architecture, natural language processing, decision-making algorithms, user interaction, agentic workflows, model evaluation, performance optimization, and applied machine learning for automated support scenarios.
4. Microsoft Azure for AI and Machine Learning
Develop practical knowledge of cloud-based AI and ML workflows.
This course covers Azure resources, AI/ML pipelines, model training, model deployment, data storage, AI security, cloud deployment, identity and access management, continuous monitoring, version control, and end-to-end workflow troubleshooting.
5. Advanced AI and Machine Learning Techniques and Capstone
Apply advanced AI and ML concepts in a final project.
This course covers ensemble methods, transfer learning, federated learning, responsible AI, data ethics, privacy, scalable AI systems, distributed computing, generative model architectures, GANs, and a comprehensive capstone project based on a real-world AI/ML solution.
Practical Projects You Can Build
During this learning path, learners can practise through projects such as:
- Designing AI/ML infrastructure
- Building data pipelines
- Preparing models for deployment
- Applying supervised and unsupervised learning
- Working with LLM-based applications
- Designing intelligent troubleshooting agents
- Creating AI workflows in cloud environments
- Deploying and monitoring machine learning models
- Building scalable AI systems
- Completing a capstone AI/ML solution
These projects help learners move beyond theory and understand how AI and machine learning solutions are developed in practical environments.
Who Should Take This Program?
This professional certificate is suitable for:
- AI learners with Python experience
- Machine learning learners
- Software developers
- Data professionals
- Cloud engineers
- MLOps beginners
- Technical support automation teams
- Developers interested in LLM applications
- Professionals interested in AI agents
- Learners preparing for AI engineering roles
Intermediate Python knowledge is recommended before starting. Familiarity with statistics, AI concepts, machine learning basics, and cloud workflows can also help learners progress more confidently.
Why AI and ML Engineering Skills Matter
AI and machine learning are no longer limited to research teams.
Modern organizations use AI to improve automation, customer support, fraud detection, predictive maintenance, decision-making, data analysis, personalization, and operational efficiency.
AI and ML engineers help turn ideas into real systems by designing infrastructure, preparing data, training models, deploying solutions, monitoring performance, improving reliability, and ensuring responsible use of AI.
How to Start Learning Free
Follow these steps to check for available preview lessons:
- Open the course guide link below.
- Scroll down and select one of the individual courses inside the program.
- Open the course you selected.
- Click Enroll.
- After signing in, choose Preview instead of Start Free Trial.
- You can now watch the available preview videos and start learning for free.
Preview availability may vary. Complete lessons, hands-on projects, assessments, certificates, cloud resources, and full program access may require paid enrollment. Some cloud-based activities may also require access to Microsoft Azure or a free trial.
Build Practical AI and ML Engineering Skills
AI and ML engineering combines programming, data, cloud infrastructure, model development, deployment, monitoring, responsible AI, and real-world problem solving.
This professional certificate gives learners a structured path to understand AI infrastructure, machine learning algorithms, intelligent agents, Azure AI workflows, MLOps, model deployment, and advanced AI project development.
Start Learning Microsoft AI & ML Engineering
Explore AI infrastructure, machine learning algorithms, Azure AI workflows, intelligent agents, MLOps, LLMs, model deployment, responsible AI, and real-world capstone projects.
Start Learning FreePreview availability and included learning materials may vary.
