Instruction Led Course : ML and AI with Microsoft Fabric (60 Hours )
Instructor-Led Course: ML and AI with Microsoft Fabric (60 Hours) This comprehensive 60-hour instructor-led program is designed to equip learners with practical knowledge and hands-on expertise in Machine Learning (ML) and Artificial Intelligence (AI) using Microsoft Fabric, Microsoft’s unified data …
Overview
Instructor-Led Course: ML and AI with Microsoft Fabric (60 Hours)
This comprehensive 60-hour instructor-led program is designed to equip learners with practical knowledge and hands-on expertise in Machine Learning (ML) and Artificial Intelligence (AI) using Microsoft Fabric, Microsoft’s unified data analytics and AI platform. Participants will explore how to seamlessly integrate data engineering, data science, and AI development workflows in Fabric to deliver intelligent, data-driven solutions at scale.
Key Learning Objectives
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Understand the fundamentals of Microsoft Fabric architecture and its role in data analytics and AI.
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Learn how to design and build end-to-end ML pipelines within Fabric.
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Gain hands-on experience with data ingestion, preparation, and transformation for ML/AI models.
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Explore AutoML, custom model development, and integration with Azure AI/ML services.
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Implement real-world AI solutions such as predictive analytics, recommendation systems, and natural language processing.
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Learn how to deploy, monitor, and optimize models within the Fabric ecosystem.
Course Highlights
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Instructor-led training with interactive sessions and real-world case studies.
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Hands-on labs to practice ML and AI workflows in Fabric.
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Guided projects simulating industry scenarios.
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Preparation for Microsoft certification pathways in AI and Fabric.
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Access to curated study material and assessments to reinforce learning.
Curriculum
Curriculum
- 14 Sections
- 43 Lessons
- 60 Hours
- Introduction to ML, AL, and Microsoft Fabric4
- Data Preparation and Ingestion3
- Exploratory Data Analysis (EDA)3
- Machine Learning Workflow in Microsoft Fabric3
- Supervised Learning : Regression3
- Supervised Learning : Classification3
- Unsupervised Learning3
- Natural Language Processing (NLP)3
- Computer Vision3
- Deep Learning and Generative AI3
- Time Series Analysis3
- Responsible AI and Model Explainability3
- Industry - Specific Use Cases3
- Real -Time Analytics and Model Deployment3





