Practical Data Science

Description

In this course, you will :

• Prepare data, detect statistical data biases, perform feature engineering at scale to train models, and use AutoML to train, evaluate, and tune models.
• Using a feature store, you can store and manage ML features, as well as debug, profile, tune, and evaluate models while tracking data lineage and model artefacts.
• Create, deploy, monitor, and operationalize full-stack machine learning pipelines.
• Create pipelines for data labelling and human-in-the-loop to improve model performance with human intelligence.

Syllabus :

• Analyze Datasets and Train ML Models using AutoML
• Build, Train, and Deploy ML Pipelines using BERT
• Optimize ML Models and Deploy Human-in-the-Loop Pipelines