In this course, you’ll apply your knowledge of classification models and embedding to build a ML
pipeline that functions as a recommendation engine.
In this module, we review the scope and plan for the course, define what recommendation
systems are, review the different types of recommendation systems and discuss common
problems that arise when developing recommendation systems.
Content-Based Recommendation Systems
In this module, we demonstrate how to build a recommendation system using
characteristics of the users and items.
COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
In this module, we show how the data of the interactions between users and items from
many different users can be combined to improve the quality of predictions.
Neural Networks for Recommendation Systems
In this module we show how various recommendation systems can be combined as part of
a hybrid approach.
Building an End-to-End Recommendation System
In this module we put all the pieces together to build a smart end-to-end workflow for
your newly built WALS recommendation model for news articles.
In this final module, we review what you have learnt so far about recommendation
systems and the specialization more broadly.
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.
Microsoft Power Platform App Maker
Designing & Implementing Azure AI Solution
Microsoft Azure Administrator
Developing Solutions For Microsoft Azure
Microsoft Azure Architect Design Exam
Implementing Azure Data Solution
Administering Relational Databases On Microsoft Azure