1) What is AI, ML, DL? 2) Types of Learning (Supervised, Unsupervised, RL) 3) Use Cases of AI 4) Python for AI: NumPy, Pandas 5) Jupyter Notebook hands-on
1) Neural Networks, Layers, Activations 2) Forward/Backward Propagation 3) Loss Functions, Optimizers 4) TensorFlow or PyTorch Setup 5) Build a Simple Neural Net Model
1) What are LLMs? (GPT, BERT, T5) 2) Tokenization, Embeddings 3) How LLMs generate text 4) OpenAI API Setup + Hands on Calls Parameters 5) Cost management
1) Zero shot, Few shot 2) Role based prompting 3) Prompt templates & design tips 4) Prompt injection protection 5) Context handling, system messages 6) Testing, iteration, real world ex
1) GenAI Chatbots with Web UI 2) Building SaaS Tools (Excel Copilot, Email Generator) 3) DALL·E / SD for image generation 4) OCR, Text-Image applications 5) Text to Speech / Speech to Text
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