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
Open coursework - 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
- Transformer
Meta-Learning, Transfer Learning, and Multi-Task Learning - 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) - CodeBeginnerNew
Train a Word-Alignment Model for Low-Resource Catalan-Aranese
You receive a 35,000-sentence Catalan-Aranese parallel corpus plus a 1,200-pair manually annotated word-alignment test set. Train (1) a classic statistical alignment baseline (e…
- Alignment
- Neural Mt
- Low Resource Mt
Machine Translation Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- 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
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
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
Linguistic Engineering and Language Technologies - 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
Open coursework - Browse challenges
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Strategy Analyst
Frame the business question, model the options, build the recommendation. From market sizing to competitive analysis, this role is where strategy consulting meets in-house decision-making.
- 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 Programming
Introduction to Artificial Intelligence (CS Elective) - 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 - CodeIntermediateNew
Extract Structured Lease Terms for a Commercial Real-Estate Platform
You receive 500 anonymized lease PDFs and a labelled gold set of 150 leases with the 14 fields filled in. Build a pipeline that does (1) layout-aware PDF parsing (Unstructured, …
- Information Extraction
- Pdf Parsing
- Named Entity Recognition
Linguistic Engineering and Language Technologies - 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…
- Transformers
- Rnn
- State Space Models
Neural Networks for NLP Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
Why Ewance
- 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
Open coursework - 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
Open coursework - 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 - 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 - CodeIntermediateNew
Build a Domain-Specific Named-Entity Recognizer for Legal Contracts
Start from a strong English NER base (spaCy transformer or LegalBERT). Fine-tune on a provided 1,200-contract labeled dataset for the 9 entity types. Handle long contracts (ofte…
- Named Entity Recognition
- Sequence Labeling
- Domain Adaptation
Natural Language Processing - 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 - 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
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 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 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.
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