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Self-paced · Always open

Practice Problems

Sharpen your ML skills with permanent practice problems. Submit your predictions anytime and compare your score on the global leaderboard.

4 problems · 8 topics · 6 submissions

AllAll Levels BeginnerBeginner MediumIntermediate AdvancedAdvanced

TopicsAll CV_Classification CV_Dectection CV_Segmentation Gensim_GloVe Multi-output NLP_Classification SpaCy Tabular_Regression

1 problem found

Beginner

Large Movie Review Dataset (IMDB) with Pretrained Word Embedding Approaches

F1 Score · Classification

Building upon classic NLP classification benchmarks, this project leverages pretrained word embeddings to construct sentiment classification models on the Large Movie Review Dataset (IMDB). Instead of relying strictly on sparse frequency-based representations like Bag-of-Words or TF-IDF, participants will explore the usage of dense vector representations to capture deeper semantic relationships. By integrating fixed, pretrained word vectors with downstream classifiers or custom aggregation strategies, this challenge highlights how semantic feature transfer can improve natural language understanding under rigid computational constraints.

NLP_Classification, SpaCy, Gensim_GloVe

10.7748Solve →