Build a 200K Wiki articles Search Engine (Python & Gensim)

Build your own search engine using Python and real-world data — no academic overload, just practical, hands-on coding.

In this course, you’ll create a Wikipedia-style search engine that can scan through 200,000+ articles and return the most relevant results — all in milliseconds. The best part? You’ll be doing it from scratch using Python, Gensim, Flask, Bootstrap, and just a few key libraries. This course is built for action-oriented learners who love building while learning.

Here’s a detailed breakdown of what this course offers:

Part 1: Understanding Search and Data

• Understand what "search" really means in the context of information retrieval

• Learn about keyword search vs. vector-based search (TF-IDF)

• Explore where real-world search data comes from — databases, APIs, and raw dumps

• Download and work with a massive dataset: 200K Wikipedia articles from HuggingFace

Part 2: Preprocessing for Search

• Learn practical text preprocessing: tokenization, stopword removal, normalization

• Use NLTK to clean and tokenize each Wikipedia article

• Structure raw text data into a searchable format

Part 3: Vectorizing the Text

• Create a Gensim Dictionary to map words to IDs

• Convert your documents into Bag-of-Words (BoW) format

• Transform BoW into a TF-IDF representation, ideal for ranking relevance

Part 4: Building the Search Index

• Use Gensim’s SparseMatrixSimilarity to index all 200K articles

• Explore how similarity scores are computed between the query and all documents

• Write Python code to return top matches for any search query

Part 5: Save and Reuse Your Search Engine

• Save key components: dictionary, index, raw docs, TF-IDF model

• Build a clean and reusable search function that returns top N results from any query

Part 6: Web Interface with Flask

• Build a lightweight Flask app to serve your search engine

• Create a clean HTML interface using Bootstrap

• Connect the frontend to your Python backend using AJAX for real-time results

• Implement "Load More" functionality without refreshing the page

Final Outcome

• A complete, functioning Wikipedia Search Engine on your local machine

• Capable of querying and ranking 200,000 documents in real time

• Easily customizable for your own datasets or search-related applications

This course is perfect for:

• Developers who want to learn NLP by building something real

• Learners tired of theory-heavy courses with no practical outcome

• Students or professionals exploring information retrieval or search engineering

• Anyone curious about how search engines like Google, Wikipedia, or Stack Overflow work

By the end of this course, you’ll have built a project you can showcase, extend, or even deploy — all using just your Python skills.