Description
Deep Natural Language Processing: From Words to Meaning
Text is the richest data we have—but it’s messy, ambiguous, and high-dimensional. This course teaches you to tame it with deep learning: from word embeddings to attention mechanisms, you’ll build systems that understand, generate, and reason about language.
Projects You’ll Build
- Spam detector using Naive Bayes and deep classifiers
- Sentiment analyzer for product reviews and social media
- Article spinner that rewrites content while preserving meaning
- Word embedding visualizer that maps semantic relationships
- Text classifier for news categories using LSTM and CNN hybrids
Key Techniques Covered
- Text preprocessing—tokenization, stemming, lemmatization
- Vectorization—TF-IDF, Word2Vec, GloVe, FastText
- Deep architectures—RNNs, LSTMs, GRUs, CNNs for text
- Attention mechanisms—the precursor to transformers
- Evaluation metrics—precision, recall, F1-score for NLP
Why Deep NLP?
- Universal applicability—every industry uses text: finance, healthcare, e-commerce, media.
- Foundation for LLMs—understanding embeddings and attention is key to working with ChatGPT-class models.
- High ROI skills—NLP engineers earn among the highest salaries in AI.
Who Is This For?
- Python developers moving into NLP
- Data scientists tired of shallow text analysis
- Students building chatbots or content tools
- Researchers needing robust NLP pipelines
From Bag-of-Words to Contextual Understanding
You’ll move beyond simple keyword matching to models that grasp nuance, sarcasm, and context—just like humans (almost!).
Language is the final frontier of AI. Start conquering it today.
