Understand the basics of Natural Language Processing including tokenization, POS tagging, word embeddings, and text normalization. Get hands-on with core NLP libraries like NLTK and TextBlob to perform essential text preprocessing.

Learn to build classification models using Naïve Bayes and Logistic Regression for tasks like sentiment and emotion detection. Implement text classification projects using Python and real-world datasets.

Explore topic modeling with LDA and BERTopic, and understand semantic similarity through word vectors. Apply these techniques through guided exercises on real-world unstructured data.

Study probabilistic models like n-grams and their role in language generation and spell correction. Build and understand the logic behind chatbot and dialog systems through hands-on coding.

 Learn deep learning techniques using RNNs, LSTMs, and attention mechanisms to process sequence data. Get introduced to GPT architectures, encoder-decoder models, and key LLM concepts.

Use PyTorch to explore tensors, attention mechanisms, and transformer architectures. Code your own GPT-style model and train it on unlabeled data to understand model internals.

 Fine-tune large and small LLMs using LoRA and integrate them into real-world applications.
Build AI agents using LangChain, AutoGPT, and implement retrieval-augmented generation (RAG) workflows.

Apply everything you’ve learned in a guided capstone project focusing on LLM-powered tools and AI applications. Get mentorship and feedback as you build, test, and present your end-to-end solution.