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https://colab.research.google.com/dri... Learn how to build a Python-based Emotion-Aware AI Assistant from scratch! In this tutorial, we cover: 💬 Sentiment Analysis – Teaching AI to understand emotions from text 🧠 Text Generation – Making AI respond naturally and empathetically 🚀 Using Hugging Face Transformers & Google T5 ⚡ Setting up GPU in Colab for faster AI processing By the end, you’ll have a working AI Assistant that understands user emotions and replies accordingly — perfect for beginners to intermediate Python developers diving into AI! 0:01 – Welcome & Course Overview 0:15 – Today’s Goal: Build an Emotion-Aware AI Assistant 0:35 – Skills to Learn: Sentiment Analysis & Text Generation 1:05 – Understanding LLMs (Large Language Models) 1:57 – Hugging Face & Transformers Library 2:50 – Setting Up Google Colab & GPU Runtime 3:08 – Installing Required Libraries (Transformers, Torch, Accelerate) 4:04 – Why GPU is Important for AI Models 5:02 – Checking GPU Availability in Colab 6:36 – Sentiment Analysis Pipeline 7:27 – Loading & Using Sentiment Analysis Model 9:20 – Testing the Model with Positive Reviews 10:58 – Batch Processing Multiple Sentences 14:04 – Understanding Model Bias in Sentiment Detection 15:17 – Text Generation / Generative AI Overview 16:09 – Using Google T5 Large Model for Text Generation 16:52 – Loading the Generator Model in Colab 18:14 – Giving Instructions & Prompts to the Generator 19:18 – Controlling Output Length & Creativity (Max Tokens & Temperature) 24:10 – Planning the Emotion-Aware AI Project 24:35 – Step 1: User Input → Sentiment Classification 25:58 – Step 2: Prompt Engineering Based on Sentiment 31:00 – Step 3: Conditional Responses (Positive, Neutral, Negative) 34:36 – Step 4: Generating & Displaying AI Replies 36:42 – Wrap Up & Next Steps for Backend Development #AI #Python #GenerativeAI #SentimentAnalysis #TextGeneration #HuggingFace #T5Model #PythonProjects #MachineLearning #ColabTutorial #EmotionAwareAI #ZeroToHero #AIForBeginners