Published 6/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 54m | Size: 992 MB
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 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 DataUnderstand what “search” really means in the context of information retrievalLearn about keyword search vs. vector-based search (TF-IDF)Explore where real-world search data comes from — databases, APIs, and raw dumpsDownload and work with a massive dataset: 200K Wikipedia articles from HuggingFacePart 2: Preprocessing for SearchLearn practical text preprocessing: tokenization, stopword removal, normalizationUse NLTK to clean and tokenize each Wikipedia articleStructure raw text data into a searchable formatPart 3: Vectorizing the TextCreate a Gensim Dictionary to map words to IDsConvert your documents into Bag-of-Words (BoW) formatTransform BoW into a TF-IDF representation, ideal for ranking relevancePart 4: Building the Search IndexUse Gensim’s SparseMatrixSimilarity to index all 200K articlesExplore how similarity scores are computed between the query and all documentsWrite Python code to return top matches for any search queryPart 5: Save and Reuse Your Search EngineSave key components: dictionary, index, raw docs, TF-IDF modelBuild a clean and reusable search function that returns top N results from any queryPart 6: Web Interface with FlaskBuild a lightweight Flask app to serve your search engineCreate a clean HTML interface using BootstrapConnect the frontend to your Python backend using AJAX for real-time resultsImplement “Load More” functionality without refreshing the pageFinal OutcomeA complete, functioning Wikipedia Search Engine on your local machineCapable of querying and ranking 200,000 documents in real timeEasily customizable for your own datasets or search-related applicationsThis course is perfect for:Developers who want to learn NLP by building something realLearners tired of theory-heavy courses with no practical outcomeStudents or professionals exploring information retrieval or search engineeringAnyone curious about how search engines like Google, Wikipedia, or Stack Overflow workBy 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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