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

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

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

Build a 200K Wiki articles Search Engine (Python & Gensim) udemy course free download

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

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.

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