AI Engineer & LLM Development Combo
Course Duration: 19–20 Weeks (Flexible) | Mode: Offline/Online
Goal: Upon completing this programme, students will be able to design, build, and deploy AI and LLM-based solutions for real-world applications. They will gain hands-on expertise in machine learning, deep learning, NLP, generative AI, and AI agents, making them job-ready for roles like AI Engineer, LLM Developer, or NLP Specialist.
MODULE 1: Python for AI & Data Handling (Week 1–2)
- Python fundamentals: variables, loops, functions
- Data structures: lists, dictionaries, tuples
- Libraries for AI: NumPy, Pandas, Matplotlib, Seaborn
- Data preprocessing, cleaning, and manipulation
MODULE 2: AI & Machine Learning Fundamentals (Week 3–5)
- AI basics: types, applications, and lifecycle
- Machine Learning: Supervised, Unsupervised learning
- Regression, classification, clustering
- Model evaluation: accuracy, precision, recall, F1-score
- Feature engineering and selection
MODULE 3: Deep Learning & Neural Networks (Week 6–8)
- Neural networks basics: perceptrons, activation functions
- Feedforward & convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs) and LSTM
- Transfer learning and pre-trained models
MODULE 4: Natural Language Processing (NLP) & Text AI (Week 9–10)
- Text preprocessing: tokenization, lemmatization, stopwords removal
- Bag of Words, TF-IDF, embeddings
- Sentiment analysis, text classification
- Transformer models: attention mechanism, BERT, GPT overview
MODULE 5: Large Language Models (LLM) Fundamentals (Week 11–12)
- Introduction to LLMs: architecture & applications
- Tokenization, embeddings, attention mechanisms
- Fine-tuning vs prompt engineering
- OpenAI GPT, LLaMA, Claude, and open-source LLMs
MODULE 6: Generative AI & Prompt Engineering (Week 13–14)
- Generative AI overview: text, image, audio generation
- Prompt engineering: structured, zero-shot, few-shot, chain-of-thought
- Tools: OpenAI API, MidJourney, DALL-E, Stable Diffusion
- Use cases: content creation, code generation, summarization
MODULE 7: Agentic AI & Intelligent Automation (Week 15–16)
- Understanding AI agents: task agents, research agents, automation agents
- Agent architecture: plan, act, observe, memory
- Multi-agent workflows and orchestration
- Tools: LangChain, Flowise, AI Studio
MODULE 8: Model Deployment & MLOps (Week 17–18)
- Introduction to MLOps & deployment
- Model serialization: Pickle, Joblib
- REST APIs with Flask / FastAPI
- Dockerizing AI models
- Deployment on cloud platforms (AWS, Azure, GCP, or Heroku)
MODULE 9: Practical & Interview Preparation (Week 19–20)
- Practical
- Interview Preparation