Description

What will you learn?

✅ Day 1: Introduction to AI, ML, and DL
✅ Day 2: Linear Algebra and Calculus for ML
✅ Day 3: Supervised and Unsupervised Learning
✅ Day 4: Model Evaluation and Cross-Validation
✅ Day 5: Introduction to Neural Networks
✅ Day 6: Convolutional Neural Networks (CNNs)
✅ Day 7: Recurrent Neural Networks (RNNs)
✅ Day 8: LSTM and GRU Networks
✅ Day 9: Autoencoders and Variational Autoencoders (VAEs)
✅ Day 10: Introduction to GANs
✅ Day 11: Deep Convolutional GAN (DCGAN)
✅ Day 12: Wasserstein GAN (WGAN)
✅ Day 13: Conditional GANs (cGANs)
✅ Day 14: CycleGAN
✅ Day 15: Introduction to Language Models
✅ Day 16: Transformer Architecture
✅ Day 17: Hugging Face and Pre-trained Models
✅ Day 18: Text Summarization and Question Answering
✅ Day 19: Machine Translation
✅ Day 20: Introduction to Retrieval-Augmented Generation (RAG)
✅ Day 21: LangChain for LLMs
✅ Day 22: Few-shot and Zero-shot Learning
✅ Day 23: Prompt Engineering
✅ Day 24: Advanced Fine-Tuning Techniques
✅ Day 25: Generative Models for Healthcare Applications
✅ Day 26: Generative Models for Scientific Applications
✅ Day 27: Generative Models for Art and Design
✅ Day 28: Generative Models for Audio and Time Series
✅ Day 29: Real-world – Final Project
✅ Day 30: Real-world – Final Project