NLP & Speech Processing
From word embeddings to voice AI — build models that understand language
4 phases. 16 lessons. 16 labs. 1 capstone. NLP and speech from foundations to production — text processing & embeddings, sequence models (RNNs, LSTMs, transformers for NLP), speech processing (audio fundamentals, ASR with Whisper, TTS), and production NLP (RAG, agents, evaluation). You build a voice assistant that transcribes speech, understands intent, and responds with synthesized voice.
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- NLP Foundations — Text processing, tokenization, and word embeddings from scratch.
- Text Representation — Word2Vec, GloVe, and contextual embeddings from scratch.
- Sequence Models — RNNs, LSTMs, and sequence-to-sequence models from scratch.
- Transformers for NLP — BERT, GPT, and transformer-based NLP from scratch.
- NLP Applications — Text classification, NER, summarization, and translation.
- Speech Foundations — Audio processing, FFT, and spectrograms from scratch.
- Automatic Speech Recognition — ASR systems, CTC, and Whisper architecture from scratch.
- Speech Synthesis & Applications — TTS, voice cloning, and speech applications.
Skills You Will Learn
- Text Preprocessing & Tokenization
- Word & Sentence Embeddings
- Vector Databases & Semantic Search
- Transformer Fine-tuning for NLP
- Named Entity Recognition
- Whisper ASR Pipeline
- Text-to-Speech Synthesis
- RAG Systems
- NLP Agents & Tool Use
- NLP Evaluation (BLEU, ROUGE, BERTScore)
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