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Recommendation Systems with TensorFlow on GCP

Approx. 13 hours to complete

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Course Summary

Learn how to build recommendation models using Google Cloud Platform (GCP) with this course. Explore different types of recommendation models and how to evaluate them using GCP tools.

Key Learning Points

Understand the different types of recommendation models and when to use them
Learn how to evaluate the performance of recommendation models using GCP tools
Build your own recommendation model using GCP

Job Positions & Salaries of people who have taken this course might have

- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- USA: $140,000
- India: ₹1,500,000
- Spain: €60,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- USA: $140,000
- India: ₹1,500,000
- Spain: €60,000
- USA: $150,000
- India: ₹1,800,000
- Spain: €70,000

Learning Outcomes

Understand the different types of recommendation models and their applications
Learn how to preprocess data for recommendation models
Build and evaluate your own recommendation model using GCP

Prerequisites or good to have knowledge before taking this course

Basic understanding of machine learning concepts
Familiarity with Python programming

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on projects

Similar Courses

Machine Learning with TensorFlow on Google Cloud Platform
Building Resilient Streaming Systems on Google Cloud Platform

Related Education Paths

Notable People in This Field

Co-founder of Coursera and deeplearning.ai
Co-director of Stanford's Human-Centered AI Institute

Related Books

Description

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

Outline

Recommendation Systems Overview
Introduction
Getting Started with Google Cloud Platform and Qwiklabs
Introduction
Types of Recommendation Systems
Content-Based or Collaborative
Recommendation System Pitfalls
Discussion
How to Send Feedback
Recommendation Systems Overview Quiz

Content-Based Recommendation Systems
Content-Based Recommendation Systems
Similarity Measures
Building a User Vector
Making Recommendations Using a User Vector
Making Recommendations for Many Users
Lab intro: Create a Content-Based Recommendation System
Lab Solution: Create a Content-Based Recommendation System
Using Neural Networks for Content-Based Recommendation Systems
Lab Intro: Create a Content-Based Recommendation System Using a Neural Network
Lab Solution: Create a Content-Based Recommendation System Using a Neural Network
Content-Based Recommendation Systems Quiz

COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
Types of User Feedback Data
Embedding Users and Items
Factorization Approaches
The ALS Algorithm
Preparing Input Data for ALS
Creating Sparse Tensors For Efficient WALS Input
Instantiating a WALS Estimator: From Input to Estimator
Instantiating a WAL Estimator: Decoding TFRecords
Instantiating a WALS Estimator: Recovering Keys
Instantiating a WALS Estimator: Training and Prediction
Lab Intro: Collaborative Filtering with Google Analytics Data
Lab Solution: Collaborative Filtering with Google Analytics Data
Issues with Collaborative Filtering
Productionized WALS Demo
Cold Starts
Collaborative Filtering Quiz

Neural Networks for Recommendation Systems
Hybrid Recommendation Systems
Lab: Designing a Hybrid Recommendation System
Lab: Designing a Hybrid Collaborative Filtering Recommendation System
Lab: Designing a Hybrid Knowledge-based Recommendation System
Lab Intro: Building a Neural Network Hybrid Recommendation System
Lab Solution: Building a Neural Network Hybrid Recommendation System
Context-Aware Recommendation Systems
Context-Aware Algorithms
Contextual Postfiltering
Modeling Using Context-Aware Algorithms
YouTube Recommendation System Case Study: Overview
YouTube Recommendation System Case Study: Candidate Generation
YouTube Recommendation System Case Study: Ranking
Summary
Neural Networks for Recommendations Quiz

Building an End-to-End Recommendation System
Introduction
Architecture Overview
Cloud Composer Overview
Cloud Composer: DAGs
Cloud Composer: Operators for ML
Cloud Composer: Scheduling
Cloud Composer: Triggering Workflows with Cloud Functions
Cloud Composer: Monitoring and Logging
Lab Intro: End-to-End Recommendation System
Cloud Composer Module Quiz

Summary
Course Summary
Specialization Summary

Summary of User Reviews

Learn about recommendation models on GCP from industry experts with this Coursera course. Students praise the course for its practical approach and real-world examples.

Key Aspect Users Liked About This Course

practical approach and real-world examples

Pros from User Reviews

The course provides hands-on experience with GCP tools and platforms
The instructors are knowledgeable and provide practical insights
Real-world examples help understand how to apply recommendation models in practice
Course material is well-organized and easy to follow
Projects and quizzes help reinforce the learning process

Cons from User Reviews

Some students found the pace of the course to be too slow
The course is focused on GCP-specific tools, which may not be applicable in all cases
Some students found the technical level of the course to be challenging
The course could benefit from more in-depth coverage of certain topics
The course may not be suitable for beginners with no prior knowledge of machine learning or programming

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