AI-900: Fundamental Principles of Machine Learning on Azure
Types of ML, data preparation, AutoML, the Designer and model evaluation for the AI-900 exam.
Machine Learning (ML) is a technique that uses mathematics and statistics to create a model capable of predicting unknown values.
Simple analogy: You work at a car dealership. You want to estimate the price of a vehicle based on its engine, fuel consumption, and mileage. You have historical sales data. You train a model on that data → the model learns the patterns → it can predict the price of an unknown car.
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What's inside
31 sections- 1 Table of Contents
- 2 Introduction to Machine Learning
- 3 Types of Machine Learning
- 4 Data Preparation
- 5 Algorithms and Microsoft Cheat Sheet
- 6 Azure Automated Machine Learning (AutoML)
- 7 Azure Machine Learning Designer
- 8 Evaluating Regression Models
- 9 Classification Models and Confusion Matrix
- 10 Clustering – Unsupervised Grouping
- 11 Deep Learning
- 12 Inference Pipelines and Deployment
- 13 Practical Implementation with Python and SDK
- 14 Exam Tips and Common Pitfalls
- 15 Summary and Key Points
- 16 Glossary
- 17 Extended Table of Contents
- 18 Introduction to Machine Learning
- 19 Types of Machine Learning
- 20 Azure Tools for Machine Learning
- 21 Data Preparation
- 22 Demo: Automated ML — Bike Rental Regression
- 23 Demo: Machine Learning Designer — Regression Pipeline
- 24 Classification Models
- 25 Demo: Classification — Income Prediction
- 26 Demo: Inference Pipeline and Model Deployment
- 27 Clustering Models
- 28 AI-900 Exam Tips
- 29 Summary and Next Steps
- 30 Detailed Demo: Income Prediction (Census Income)
- 31 Detailed Demo: Inference Pipeline and Deployment
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