NLP-LOVE/ML-NLP

Combines theoretical explanations with hands-on implementations across classical ML algorithms (linear/logistic regression, decision trees, SVM, clustering) through modern deep learning architectures (CNNs, RNNs, Transformers, BERT) to NLP-specific applications. Organized as modular chapters with working code examples and interview-focused problem sets, covering feature engineering, optimization techniques, and end-to-end projects like recommendation systems and intelligent chatbots. Targets algorithm engineers preparing for technical interviews with a structured knowledge progression from foundational concepts to state-of-the-art transformer-based models.

17,549 stars.