Natural Language Processing with Deep Learning

Text data is everywhere, but teaching machines to understand human language requires powerful computational models. This text-based course guides you through the foundational concepts of processing text using modern deep learning techniques. You will transition from understanding basic text preprocessing to designing, training, and evaluating neural models for tasks like sentiment analysis, text generation, and translation. What you'll learn: Understand foundational NLP concepts, text preprocessing, and tokenization techniques; Map words to high-dimensional spaces using word embeddings and vector representations; Build and train recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data; Explore the mechanics of attention mechanisms and transformer-based architectures; Apply modern transfer learning techniques using pre-trained language models for text classification; Implement evaluation metrics to measure model performance on real-world text datasets. The course begins with core linguistic concepts and mathematical foundations before advancing to neural network architectures and modern transformer models. You will read clear, step-by-step explanations and analyze code snippets designed to build your practical intuition. This course is designed for aspiring data scientists, software developers, and tech enthusiasts who want to learn deep learning for NLP from scratch. No prior experience with neural networks is required, though basic Python familiarity is helpful. Start your journey into the world of intelligent text processing today.