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
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
- Transformers
Deep Learning - 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…
- Lora
- Fine Tuning
- Parameter Efficient Tuning
Open coursework - 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 
CodeBeginnerNewBuild a Pricing-Aware Scheduling Agent for Smart Thermostats
Using only the materials provided, build an intelligent agent with four working parts: perception (read the sensor history), learning (cluster each household's comfort intervals…
- Intelligent Agents
- Basic Learning
- Python Programming
Open courseworkPractice 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
- 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
- Transformers
Open coursework - 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 - 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
Open coursework - CodeIntermediateNew
Fine-Tune a Sequence-to-Sequence Model for Code-Doc Generation
Take a small base model (CodeT5+ or a distilled CodeLlama-Instruct). Build the dataset by mining around 8,000 high-quality function-docstring pairs from permissively-licensed Py…
- Seq2seq
- Transformers
- Lora Fine Tuning
Neural Networks for NLP - 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.
- 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 - 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
Open coursework - 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…
- Transformers
- Self Attention
- Pytorch
Neural Networks for NLP - 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
- Transformer
- Pose Estimation
Machine Perception 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
- DesignBeginnerNew
Build an Attention-Visualization Tool for Translation Quality Audit
You will load a small open-source EN-FR transformer (e.g., Helsinki-NLP Opus-MT-en-fr), build a Streamlit or Gradio demo that lets the user paste English source, see the French …
- Attention Mechanisms
- Neural Mt
- Tool Design
Open coursework - 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
- 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 - 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
Open coursework - 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
- Transformers
Advanced Deep Learning - 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
Open coursework - 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
- Transformers
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
- Transformers
Open coursework - 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
Description-Logic Reasoner for Insurance-Policy Coverage Checks
You receive 50 representative coverage rules in plain English (from the current rule engine) and a sample of 1,000 anonymized claim cases with the current engine's outcomes (cov…
- Description Logics
- Owl
- Reasoning
Open coursework - DesignIntermediateNew
Visualize Embedding Drift for a RAG Knowledge Assistant
You receive weekly snapshots over 12 weeks of around 50,000 document embeddings each (1024-dim). Design and build a visualization tool that: (a) projects each snapshot to 2D wit…
- Embeddings
- Dimensionality Reduction
- Umap
Open coursework - 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
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.