Optimizing a Website for Google Search course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to modern web search systems, focusing on the algorithms, machine learning techniques, and infrastructure that power search engines like Google. You'll explore how search engines crawl, index, and retrieve information, followed by in-depth coverage of ranking models, evaluation metrics, and optimization strategies. The course is structured into six modules, spanning approximately 12–14 weeks of part-time study with a weekly commitment of 6–8 hours. Hands-on exercises and a final project reinforce theoretical concepts with practical applications.
Module 1: Foundations of Web Search
Estimated time: 14 hours
- How search engines crawl and index web pages
- Understanding inverted indexes
- Query processing fundamentals
- Introduction to ranking basics and relevance scoring
Module 2: Ranking and Information Retrieval Models
Estimated time: 21 hours
- TF-IDF and vector space models
- Learning-to-rank techniques
- Personalization in ranking
- Contextual ranking strategies
Module 3: Evaluation and Optimization
Estimated time: 21 hours
- Evaluation metrics: precision, recall, MAP, and NDCG
- Analyzing search performance trade-offs
- Optimizing ranking pipelines for relevance
- Scalability considerations in search systems
Module 4: Machine Learning for Search
Estimated time: 14 hours
- Applying ML algorithms to ranking problems
- Feature engineering for search systems
- Real-world case studies in search optimization
Module 5: Advanced Topics in Search Systems
Estimated time: 10 hours
- Overview of neural ranking models
- Large-scale indexing challenges
- Query understanding and semantic search
Module 6: Final Project
Estimated time: 20 hours
- Design and implement a mini search engine
- Apply ranking and relevance optimization techniques
- Evaluate system performance using standard metrics
Prerequisites
- Familiarity with basic algorithms and data structures
- Introductory knowledge of machine learning concepts
- Programming experience (preferably Python)
What You'll Be Able to Do After
- Explain how search engines retrieve and rank web content
- Implement core information retrieval models like TF-IDF and inverted indexes
- Apply learning-to-rank and machine learning techniques to improve search relevance
- Evaluate search system performance using precision, recall, MAP, and NDCG
- Optimize search pipelines for scalability and real-world deployment