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

gensim, From Data Preprocessing to Search — Step-by-Step Guide in gensim, python and flask

What you'll learn
- Build a full-text search engine using Python and Gensim
- Preprocess large-scale textual data for information retrieval
- Create Bag-of-Words and TF-IDF representations from raw text
- Construct a Gensim similarity index for fast search queries
- Build a search API using Flask
- Create a simple and responsive frontend using Bootstrap and JavaScript
- Integrate AJAX for dynamic result loading in the UI
- Understand the basics of search systems and document similarity
- Learn how to use real-world datasets from HuggingFace

Requirements
- Basic knowledge of Python
- Familiarity with lists, functions, and dictionaries in Python
- A working installation of Python (3.7 or above)
- Some experience with HTML/CSS is helpful but not mandatory as I will just provide you the code. Main topic of the course is building search system and not get bogged down by UI details
- Curiosity and willingness to learn by doing

Description
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 through200,000+ articlesand return the most relevant results — all in milliseconds. The best part? You’ll be doing it from scratch usingPython, 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 aGensim Dictionaryto map words to IDs

Convert your documents intoBag-of-Words (BoW)format

Transform BoW into aTF-IDF representation, ideal for ranking relevance

Part 4: Building the Search Index

Use Gensim’sSparseMatrixSimilarityto 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, functioningWikipedia Search Engineon 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.

Who this course is for:
- Python developers interested in natural language processing
- Beginners in search or information retrieval systems
- Students or professionals wanting to build real NLP apps
- Hackers and hobbyists looking to explore large-scale text data
- Anyone curious about how search engines work under the hood
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