Information Retrieval
If you like applying Information Retrieval, every challenge here gives you a chance to practice it on a real industry brief.
- ResearchBeginnerNew
Research Roadmap for Meridian DSP's Bid-Matching Pipeline
Using the VP of Machine Learning's context memo (vp-brief) and the pipeline case file (pipeline-case-file) to ground every recommendation in Meridian's actual system, assemble a…
- Information Retrieval
- Learning To Rank
- Research Synthesis
Open coursework - CodeIntermediateNew
Distributional Embeddings for a Multilingual Legal Search
Use a public multilingual corpus (e.g., MultiEURLEX or a subset of EUR-Lex) plus a small hand-built test set of around 100 cross-lingual query-passage pairs. Fine-tune (or evalu…
- Distributional Semantics
- Multilingual NLP
- Sentence Embeddings
Computational Semantics - DesignIntermediateNew
Build Hybrid Keyword-Plus-Embeddings Search for a Legal Document Portal
Using the product context brief (legal_portal_brief), design a hybrid retrieval system that combines BM25 keyword scoring with dense embeddings and fuses the two ranked lists wi…
- Information Retrieval
- Bm25
- Vector Search
Open coursework - CodeBeginnerNew
Build an Embedding-Based Semantic Search for a Legal-Document Corpus
Embed the 380k-document corpus using a multilingual sentence-transformer (e.g. multilingual MPNet or LaBSE). Store embeddings in FAISS or pgvector. Build a search service that r…
- Deep Learning
- Ml Applications
- Python Programming
Machine Learning (CS Elective) Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
LLM-Powered FAQ Chatbot for 40-Person SaaS Scale-up
You have access to TaskFlow's internal documentation, help articles, and a sample of 500 support tickets. Your task is to build a retrieval-augmented generation (RAG) pipeline: …
- LLM
- RAG
- Information Retrieval
Text Analytics and Natural Language Processing
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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