Certificate of Completion

What You Learned

• Explain how NLP systems process raw text through tokenization, stemming, and lemmatization
• Compare text representation methods from bag-of-words and TF-IDF to Word2Vec and transformer embeddings
• Build text classification pipelines for spam detection, topic labeling, and document categorization
• Apply named entity recognition to extract people, organizations, dates, and locations from unstructured text
• Implement sentiment analysis systems that classify opinions at document and aspect level
• Evaluate when to use BERT, GPT, or T5 architectures for different NLP tasks