AI & Data
NLP Challenges
NLP challenges put you inside the work of teaching machines to read and make sense of language. You'll develop skills in Natural Language Processing fundamentals, Text Tokenization and Word Embeddings, and tasks like Named Entity Recognition and Sequence labeling using NLTK.
From there you'll handle the harder edges — Encoder fine-tuning (BERT family) with Hugging Face Transformers, Custom tokenization, Relation extraction, Information Retrieval, and Multilingual NLP — building Knowledge Representation the way real NLP teams do. Each challenge you solve earns a verified credential you can share with recruiters.
- CodeIntermediateNew
Reason over a Climate Policy Knowledge Graph for an EU Think Tank
Design a knowledge graph schema covering regulations, member states, sectors, transposition dates, and source-document citations. Ingest a curated dataset of around 200 nodes th…
- Knowledge Graphs
- Knowledge Representation
- Rule Based Reasoning
Artificial Intelligence: Principles and Techniques - CodeIntermediateNew
Build a 30-Day Readmission Risk Model on De-Identified EHR Data
You receive a curated MIMIC-style de-identified EHR cohort (about 28,000 admissions, demographics, comorbidities, labs, prior-admission counts) with 30-day readmission labels. T…
- Ehr Modeling
- Risk Stratification
- Model Calibration
Machine Learning for Healthcare and Biomedicine - DesignIntermediateNew
Build an OWL Ontology for a Pharma R&D Knowledge Base
You receive a CSV-form starter knowledge base (around 4,000 compounds, 600 targets, 1,200 assays) and a list of 12 competency questions the scientists currently can't answer wit…
- Ontology Design
- Owl
- Knowledge Representation
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - CodeIntermediateNew
Adapt Machine Translation to a Niche Domain
Pick an open MT base (NLLB-200 or a strong open M2M model). Build a parallel corpus of around 8,000 sentence pairs from the company's bilingual safety standards. Fine-tune on th…
- Machine Translation
- Domain Adaptation
- Hugging Face Transformers
Natural Language Processing 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
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 - CodeBeginnerNew
Build a Multilingual Text-Mining Dashboard for Hotel Reviews
You receive 200,000 sampled reviews across 9 languages plus an English-only labeled benchmark of 1,000 reviews for sentiment and aspect (rooms, food, staff, value, location). Bu…
- Multilingual NLP
- Sentiment Analysis
- Aspect Extraction
Linguistic Engineering and Language Technologies - CodeIntermediateNew
Build a BM25 + Embeddings Hybrid Search for a Legal-Tech Document Portal
Stand up an OpenSearch cluster with BM25 indexing on the 2.4M-document corpus. Generate dense embeddings (you choose the model; justify cost and quality trade-offs) and index th…
- Information Retrieval
- Bm25
- Vector Database Basics
Data Mining and Information Retrieval - CodeIntermediateNew
Build a Multimodal Generation Pipeline for a Tourism Operator
You receive 40 sample 30-second videos shot by tour guides, the operator's brand voice doc, and SEO keyword lists for EN/PT/ES. Build a pipeline that (1) extracts a representati…
- Multimodal Generation
- Vision Language Models
- LLM Inference
Generative AI - Browse challenges
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Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- AnalysisFoundationalNew
Sentiment Analysis for Tel Aviv D2C Cosmetics Brand
You are provided with a dataset of 10,000 customer reviews (in English) with no labels. Your task is to preprocess the text, develop a sentiment classification model using NLP t…
- Text Preprocessing
- Sentiment Analysis
- Classification
Text Analytics and Natural Language Processing - 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 Or Javascript
Machine Learning (CS Elective) - CodeBeginnerNew
Knowledge-Graph Recommender for a Niche Online Bookstore
Model the catalog as a knowledge graph (nodes: books, authors, genres, themes, eras, awards; edges: wrote, in-genre, has-theme, won, similar-to). Use Neo4j or a simple Python in…
- Knowledge Representation
- Knowledge Graphs
- Python Or Javascript
Introduction to Artificial Intelligence (CS Elective) - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Meta-Learning, Transfer Learning, and Multi-Task Learning Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Design an SAT-Based Verifier for an Autonomous-Vehicle Test Lab
Model a simplified four-way intersection: agent positions, lights, and discrete time steps. Define 5 safety properties in propositional logic (e.g., 'no two agents in the inters…
- Sat Solving
- Logical Inference
- Formal Verification
Artificial Intelligence: Principles and Techniques - CodeFoundationalNew
Rule-Based Intent Classifier for a Customer-Support Triage Bot
Build a rule-based classifier in Python that runs ordered rules (regex + keyword + simple heuristics) against ticket subject + body. Use a hierarchical rule structure (high-prec…
- Knowledge Representation
- Rule Based Systems
- Python Or Javascript
Introduction to Artificial Intelligence (CS Elective) - CodeIntermediateNew
Build a Multilingual Customer-Email Classifier
You receive 28,000 labeled emails (skewed toward English and Mandarin). Try at least two approaches: (1) a fine-tuned multilingual transformer (XLM-RoBERTa or mDeBERTa) and (2) …
- Text Classification
- Multilingual NLP
- Hugging Face Transformers
Natural Language Processing - ResearchBeginnerNew
Curate a Domain Lexicon for a Climate-Tech NLP Stack
You receive 5,000 policy documents and a benchmark of 200 documents with manually tagged domain terms. Curate a lexicon of ~1,500 terms with (1) canonical English form, (2) Swah…
- Lexical Resources
- Named Entity Recognition
- Spacy
Linguistic Engineering and Language Technologies - CodeIntermediateNew
Train a Sequence Model for Wearable-Telemetry Sleep Staging at a Healthtech
You receive 220 nights of wearable telemetry from 60 subjects with PSG ground-truth labels. Train three sequence models: an LSTM baseline, a 1D-CNN+GRU hybrid, and a small trans…
- Sequence Models
- Lstm
- Hugging Face Transformers
Deep Learning - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG Architectures
Fine-Tuning Large Language Models - CodeBeginnerNew
Intelligent Agent for a Smart-Thermostat Pricing-Aware Schedule
Design an intelligent agent with: perception (read sensor history), basic learning (cluster comfort intervals from 7 days of observations), decision-making (schedule heating to …
- Intelligent Agents
- Basic Learning
- Python Or Javascript
Introduction to Artificial Intelligence (CS Elective) - CodeIntermediateNew
Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant
You receive a corpus of around 20,000 PDFs (mixed scanned and digital) totalling around 30 GB and a labeled retrieval set of 200 queries with human-judged ground-truth passages.…
- RAG Architectures
- Vector Database Basics
- Word Embeddings
Data Engineering and Big Data Systems - CodeIntermediateNew
Instruction-Tune a Small Model for an Edtech Tutor
You receive a 1.5B base model (e.g., SmolLM-1.7B or Qwen-1.8B), permission to use 2 hours of a rented A100, and a curated seed of around 5,000 math-tutoring dialogues. Augment w…
- Instruction Tuning
- Fine Tuning
- Dataset Curation
Fine-Tuning Large Language Models - CodeIntermediateNew
Domain-Adapt an NLP Pipeline from News to Customer-Support Tickets
You receive 30,000 anonymized customer-support tickets (PT-BR + ES) plus the news-trained NER and intent models. Apply continued pretraining of a multilingual encoder (e.g., XLM…
- Transfer Learning
- Domain Adaptation
- Continued Pretraining
Meta-Learning, Transfer Learning, and Multi-Task Learning - ResearchSeniorNew
Compare RNN vs Transformer for Long-Sequence Modeling
Pick a public trajectory dataset (e.g., Argoverse 2, Waymo Open, or ETH-UCY). Implement three models with comparable parameter counts (around 5M each): an LSTM baseline, a vanil…
- Hugging Face Transformers
- Rnn
- State Space Models
Neural Networks for NLP - ResearchIntermediateNew
Probe a Pretrained Encoder for Linguistic Knowledge
Take BERT-base (or DeBERTa-v3-base). Run layer-wise probes across at least 3 linguistic tasks: part-of-speech tagging, dependency arc classification, and semantic role labeling.…
- Interpretability
- Probing
- Hugging Face Transformers
Neural Networks for NLP
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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