Description
Natural Language Processing with PyTorch: The Complete Developer’s Guide
This course bridges the gap between classical NLP and deep learning. You’ll start with tokenization and TF-IDF, then rapidly progress to word embeddings, RNNs, and transformers—all implemented cleanly in PyTorch.
Real Projects You’ll Build
- Spam detector using logistic regression and naive Bayes
- Topic modeler using LDA and NMF
- Neural text classifier with LSTM and attention
- Custom tokenizer for domain-specific text (e.g., medical, legal)
- Model deployment API using Flask and PyTorch Serve
Skills You’ll Gain
- Text preprocessing with spaCy and NLTK
- Feature engineering for NLP: n-grams, TF-IDF, POS tagging
- Deep learning for text: Embeddings, RNNs, CNNs, transformers
- Model evaluation: precision/recall, F1, perplexity, BLEU
- Production deployment: serialization, batching, REST APIs
Why This Course Stands Out
We don’t just import pipeline() and call it a day. You’ll implement every major NLP component from scratch—then compare it to optimized library versions. This builds deep intuition that employers value.
Who Is This For?
- Python developers expanding into AI
- Data scientists moving from tabular to textual data
- Graduate students needing a practical NLP reference
- Freelancers building custom NLP solutions
Your NLP Career Starts Here
Whether you’re targeting roles in chatbots, search, or content analysis—this course gives you the applied skills to deliver real value.
Don’t just process text—understand it. Enroll today.
