To understand core ML concepts, build simple models, and integrate them with .NET & Azure.
- Machine Learning Specialization by Andrew Ng (Course 1: Supervised ML)
- Learn ML fundamentals, regression, classification, and real-world applications.
- Python for Machine Learning (Kaggle - Free)
- Quick intro to Python (if needed) for ML.
- Scikit-Learn for Beginners
- Learn how to use Python’s Scikit-Learn to build and train models.
- TensorFlow/Keras for Quick ML Prototyping
- Train a simple ML model without deep diving into neural networks.
- Work on Small Projects:
- Build a .NET app that calls an ML model via an API.
- Train a simple ML model on structured data (e.g., predicting house prices).
- Microsoft AI-900: Azure AI Fundamentals
- Learn Azure ML services, cognitive AI, and responsible AI.
- Deploy ML Models on Azure ML
- Train and deploy a model using Azure ML Studio.
- ML.NET Introduction
- Learn how to use ML models inside .NET applications.
- Experiment with Azure AI in a .NET project
- Use Azure AI & Cognitive Services for text analysis, vision AI, or predictions.
- Deploy a simple Scikit-Learn model in Azure ML
- Create a .NET API that calls an ML model for predictions
- Use ML.NET for basic sentiment analysis in a .NET app
| Month | Focus Area |
|---|---|
| 1 | ML fundamentals & Python basics |
| 2-3 | Hands-on ML with Scikit-Learn & TensorFlow |
| 4 | Azure ML deployment & AI-900 certification |
| 5+ | Integrate ML models into .NET apps |
✅ Understand ML fundamentals without heavy math
✅ Build basic ML models using Python
✅ Deploy models on Azure ML
✅ Integrate ML into .NET applications
This roadmap keeps things light, practical, and aligned with your expertise. Once you're comfortable, you can go deeper into advanced ML.