Who is this training for?
- Entry-Level
- Data Scientist
- Data Analyst
- Data Engineer
Training objectives
You'll learn how to explore, preprocess, train, and track machine learning models in Microsoft Fabric.
Summary
Applied Skills training courses are designed to validate specific skills by being oriented towards real-world scenarios. They offer a targeted alternative to traditional role-based certifications, with a focus on applying technical skills in real-world business situations. Explore the data science process and learn how to train machine learning models to realize artificial intelligence in Microsoft Fabric.
Course outline
- Get started with data science in Microsoft Fabric
- Explore data for data science with notebooks in Microsoft Fabric
- Preprocess data with Data Wrangler in Microsoft Fabric
- Train and track machine learning models with MLflow in Microsoft Fabric
- Generate batch predictions using a model deployed in Microsoft Fabric
Approach and methodology
Practical and structured approach combining focused theory and guided workshops. Participants gradually implement a data science and machine learning solution for AI in Microsoft Fabric through real-world exercises inspired by professional scenarios, promoting immediate application of learnings. They learn how to prepare and analyze data, train models, and leverage the built-in capabilities of Fabric to develop analytics and AI solutions. Led by a Microsoft certified trainer, the training focuses on interactivity and the development of directly transferable technical skills to carry out data science projects in a professional context.
Prerequisites
You need to be familiar with the basic concepts and terminology of the data.
Recommendations
Basics in data science and machine learning (models, training, evaluation) Knowledge of Python and associated libraries (e.g.: Pandas, scikit-learn) Understanding of data manipulation and preparation concepts Notions of statistics and data analysis Familiarity with data or cloud environments (Azure / Fabric – asset)
