Intent Technology

-   Lesson Objectives:     
-   Learn about basic search algorithms.    -   Understand how indexing works.

-   Lesson Content:     
-   Sequential search    
-   Binary search    
-   Indexing techniques: inverted index,    forward index.

-   Exercises:     
-   Implement a simple sequential search algorithm in Python.    
-   Explain the difference between sequential and binary search. strategies to improve search results, optimize search performance, and provide a better user experience. 

Lesson Objectives:

-   Explore advanced search algorithms.

-   Understand the concept of relevance ranking.

-Lesson Content:

-   Hash tables for search

-   Trie data structure

-   Relevance ranking algorithms: TF-IDF, BM25.

Exercises:

-   Implement a Trie data structure for efficient search.

- Calculate TF-IDF scores for given documents and queries.

Lesson Objectives:
-   Learn how to measure relevance in search results.

-   Understand relevance feedback techniques.

Lesson Content:

-   Precision, recall, F1 score

-   Evaluation metrics for search

-   Relevance feedback: Rocchio algorithm.

Exercises:

 -   Calculate precision, recall, and F1 score for a given set of search results.

 -   Implement the Rocchio algorithm for relevance feedback.

Lesson Objectives:

    
-   Learn techniques to optimize search performance.

-   Understand caching and query optimization.

Lesson Content:

-   Query optimization techniques

-   Caching strategies

-   Parallel processing for search optimization.

Exercises:

    
-   Identify and explain two query optimization techniques.

-   Design a caching strategy for a search application.

Lesson Objectives:

-   Understand how to integrate search functionalities into applications.

-   Learn about popular search libraries and APIs.

Lesson Content:

-   Integration of search APIs

-   Using Elasticsearch for search

-   Implementing search in web applications.

Exercises:

-   Set up Elasticsearch on your local machine and index sample data.

-   Integrate a search API into a simple web application.

Lesson Objectives:

-   Learn about user interface design for search.

-   Understand how to handle user queries effectively.

Lesson Content:

-   User interface design principles for search

-   Autocomplete and suggestions

-   Handling misspelled queries.

Exercises:

-   Design a search interface with autocomplete feature.

-   Implement a spell checker for search queries.

Lesson Objectives:

-   Understand the importance of search analytics.

-   Learn how to analyze search logs.

Lesson Content:

-   Logging search queries

-   Analyzing search logs

-   Improving search based on analytics.

Exercises:

-   Set up logging for search queries in your application.
-   Analyze search logs to identify popular queries and trends.

Lesson Objectives:

-   Learn techniques to handle large volumes of data in search applications.

-   Understand distributed search architectures.

Lesson Content:

-   Sharding and replication

-   Distributed search architectures

-   Scalability considerations.

Exercises:

-   Design a sharding strategy for a large dataset in Elasticsearch.
-   Implement a distributed search architecture using a cloud service.