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

Partner: Udemy
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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 Engine</stron
Category: Development > Data Science > Natural Language Processing (NLP)
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Price: 199.99
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Source: Impact
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