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
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 - 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) - 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) - 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 Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- 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
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction
You receive a curated extraction dataset (2,000 train, 500 val, 500 test contracts with span-level labels across 12 clause types) and a fine-tunable 7B base model (e.g., Llama-3…
- Fine Tuning
- Fine Tuning
- Parameter Efficient Tuning
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) - AnalysisIntermediateNew
Catastrophic-Forgetting Audit on a Domain Fine-Tune
You receive the fine-tuned 7B chemistry model and its base, plus a benchmark basket (MMLU subset, GSM8K, IFEval, a small instruction-following set). Run all 4 benchmarks on both…
- Catastrophic Forgetting
- LLM Evaluation
- Fine Tuning
Fine-Tuning Large Language Models - Browse challenges
Explore role
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 - 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 - 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 - CodeIntermediateNew
Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor
You receive 240,000 labeled support tickets across 14 queues, with English, Bahasa Indonesia, and Tagalog. Fine-tune a multilingual transformer encoder (XLM-RoBERTa-base is a st…
- Hugging Face Transformers
- Fine Tuning
- Multilingual NLP
Deep 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
- ResearchSeniorNew
Benchmark Long-Context Architectures on a Legal-Doc Retrieval Task
You receive a public legal-QA dataset (e.g., LongBench's legal split or similar) filtered to documents over 50,000 tokens. Implement or wrap 3 architectures: a sliding-window Tr…
- Long Context Architectures
- State Space Models
- Hugging Face Transformers
Advanced Deep Learning - CodeIntermediateNew
Train a Domain-Specific Reranker for a Legal-Tech Search Box
You receive 20,000 (query, document, relevance-label) triples from the firm's contract corpus. Fine-tune a small cross-encoder (e.g., ms-marco-MiniLM-L-6-v2 or BAAI/bge-reranker…
- Cross Encoder Reranker
- Fine Tuning
- Ir Evaluation
Information Retrieval and Search - 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 - 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 - CodeIntermediateNew
Build a Small Transformer from Scratch and Train It on Code
Implement multi-head self-attention, RMSNorm, rotary positional embeddings, and a causal LM head from scratch — no Hugging Face shortcuts for the model code (you may use Hugging…
- Hugging Face Transformers
- Self Attention
- Pytorch Or Tensorflow
Neural Networks for NLP - AnalysisBeginnerNew
Build a Topic-Modeling Pipeline for Citizen Feedback
Take the 60,000 comments (anonymized). Build a BERTopic pipeline with multilingual sentence embeddings (Catalan + Spanish + occasional English). Tune number-of-topics via topic-…
- Topic Modeling
- Bertopic
- Multilingual NLP
Natural Language Processing - 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 - CodeIntermediateNew
Build a Sequence Model for Sign-Language Word Recognition
You receive about 12,000 short (1-3s) webcam clips covering a 50-word vocabulary, with body+hand pose features pre-extracted (e.g., MediaPipe Holistic landmarks per frame). Buil…
- Sequence Models
- Hugging Face Transformers
- Pose Estimation
Machine Perception - CodeBeginnerNew
Reason about Drone Mission Plans with Probabilistic Logic
Build a small Bayesian network (around 12 nodes) capturing weather, no-fly-zone proximity, battery state, operator certification, and mission risk. Implement exact inference (va…
- Bayesian Networks
- Probabilistic Inference
- Knowledge Representation
Introduction to Artificial Intelligence - CodeIntermediateNew
Design a Visual Search Backend for a Boutique Luxury Marketplace
You receive a catalog of 80,000 luxury items (image + sparse metadata) and a labeled query set of 300 user photos with hand-picked target items. Choose an embedding strategy (CL…
- Visual Search
- Word Embeddings
- Clip
Deep Learning for Computer Vision - 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 - ResearchIntermediateNew
Multi-Task Learning for a Healthtech Triage Model
You receive 40,000 anonymized de-identified intake-form records with two labels: urgency tier (4 classes) and routed sub-specialty (12 classes). Train (1) two independent classi…
- Multi Task Learning
- Transfer Learning
- Hugging Face Transformers
Meta-Learning, Transfer Learning, and Multi-Task Learning
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.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.