Microsoft
Microsoft AI & ML Engineering Professional Certificate
Microsoft

Microsoft AI & ML Engineering Professional Certificate

Prepare for a Career in AI & ML Engineering. Build, deploy, and innovate with advanced techniques and real-world projects. Intermediate programming knowledge of Python required.

 Microsoft

Instructor: Microsoft

Access provided by Coursera Learning Team

21,832 already enrolled

Earn a career credential that demonstrates your expertise
4.6

(159 reviews)

Intermediate level

Recommended experience

6 months to complete
at 7 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
4.6

(159 reviews)

Intermediate level

Recommended experience

6 months to complete
at 7 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design and Implement AI & ML Infrastructure: Develop environments, including data pipelines, model development frameworks, and deployment platforms.

  • Master AI & ML Algorithms and Techniques: Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges.

  • Develop Intelligent Troubleshooting Agents: Create AI-powered agents capable of diagnosing and resolving issues autonomously.

  • Leverage Microsoft Azure for AI & ML Workflows: Set up, manage, and optimize the entire AI & ML lifecycle using Azure.

Details to know

Shareable certificate

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Taught in English

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Advance your career with in-demand skills

  • Receive professional-level training from Microsoft
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from Microsoft

Earn 50% off your Microsoft Azure AI Engineer Associate AI-102 Certification Exam

Professional Certificate - 5 course series

What you'll learn

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Data Security
Category: Application Frameworks
Category: Tensorflow
Category: Scalability
Category: Data Pipelines
Category: Scikit Learn (Machine Learning Library)
Category: Artificial Intelligence
Category: Application Deployment
Category: MLOps (Machine Learning Operations)
Category: Data Management
Category: Machine Learning
Category: Data Processing
Category: Infrastructure Architecture
Category: Cloud Infrastructure
Category: PyTorch (Machine Learning Library)
Category: Data Cleansing

What you'll learn

Skills you'll gain

Category: Unsupervised Learning
Category: Deep Learning
Category: Supervised Learning
Category: Reinforcement Learning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Neural Networks
Category: Applied Machine Learning
Category: Feature Engineering
Category: Dimensionality Reduction
Category: Generative AI
Category: Data Modeling
Category: Business Logic
Category: Predictive Modeling
Category: Decision Tree Learning
Category: Large Language Modeling
Category: Performance Metric
Category: Machine Learning Algorithms
Category: Statistical Machine Learning

What you'll learn

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Natural Language Processing
Category: Large Language Modeling
Category: Performance Tuning
Category: Agentic systems
Category: Machine Learning
Category: Performance Testing
Category: Artificial Intelligence
Category: Test Case
Category: Python Programming
Category: Debugging
Category: Generative AI Agents
Category: Prompt Engineering
Category: Human Computer Interaction

What you'll learn

Skills you'll gain

Category: Microsoft Azure
Category: Cloud Security
Category: Scalability
Category: CI/CD
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Application Deployment
Category: MLOps (Machine Learning Operations)
Category: Data Pipelines
Category: Software Versioning
Category: Cloud Computing
Category: Network Troubleshooting
Category: Data Transformation
Category: Continuous Monitoring
Category: Data Quality
Category: Data Storage

What you'll learn

Skills you'll gain

Category: Applied Machine Learning
Category: Distributed Computing
Category: MLOps (Machine Learning Operations)
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Scalability
Category: Machine Learning Methods
Category: Generative AI
Category: Deep Learning
Category: Microsoft Azure
Category: Information Privacy
Category: Tensorflow
Category: Machine Learning
Category: Artificial Neural Networks
Category: Data Ethics
Category: Ethical Standards And Conduct
Category: Artificial Intelligence

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Instructor

 Microsoft
238 Courses1,685,770 learners

Offered by

Microsoft

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