This course guides you through the core concepts behind neural language models and machine translation, focusing on how RNNs, attention, and transformers enable powerful NLP applications used in today’s AI systems.

Through hands-on exercises, you’ll learn to build, fine-tune, and evaluate neural models for contextual language understanding, sentiment classification, and multilingual translation across various domains. By the end of this course, you will be able to: - Explain and implement core neural architectures, including RNNs, LSTMs, GRUs, and Transformers - Apply encoder-decoder frameworks and attention mechanisms to build translation systems - Fine-tune pretrained models like BERT, RoBERTa, and MarianMT for contextual NLP tasks - Address challenges such as domain adaptation, low-resource translation, and error correction - Evaluate model performance using BLEU, ROUGE, and semantic similarity metrics This course is ideal for NLP practitioners, machine learning engineers, and researchers aiming to build high-performing neural NLP systems for translation, classification, and conversational AI. A working knowledge of Python, NLP concepts, and machine learning is recommended. Join us to master the neural foundations driving next-generation language understanding and generation.