| 1 | Review of Machine Learning Fundamentals | |
| 2 | Python Basics | |
| 3 | Basic Text Processing 1 – Preprocessing, Exploratory Data Analysis | |
| 4 | Basic Text Processing 2 – Feature Extraction / Discriminative Features Text Classification – N-gram Language Models | |
| 5 | Review of Artificial Neural Networks Fundamentals Word Embeddings – Word2vec, Gensim, Glove | |
| 6 | Text Classification – Sequence Models RNN, LSTM, Perplexity Score | |
| 7 | Text Classification – Sequential Models -2 Seq2seq, Encoder-Decoders, Attention Mechanism, Bleu Score | |
| 8 | Advanced NLP (Natural Language Processing) 1 – Transformers, Bert, GPT | |
| 9 | dvanced NLP 2 – Implementing Basic NLP Tasks with Huggingface | |
| 10 | Advanced NLP 3 – Other NLP Tasks NER, QA, Summarization, Text Generation | |
| 11 | Product Ready Application Development – ??Search Engine Application Huggingface-transformers, Elasticsearch, Fastapi | |
| 12 | Tools of the Trade | |
| 13 | Human-Sourced Learning, Human-Sourced Reinforcement Learning (Prompting, Reinforcement Learning from Human) | |
| 14 | Ethics, Privacy, Deception, Fraud, Reliability, Explainability in Natural Language Processing | |