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Course Repository
Introduction to AI, ML, and DL
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Introduction to Artificial Intelligence (5:52)
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Basic of ML & DL Projects (3:13)
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Step Involved in a My DL Projects (6:45)
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Different between Traditional AI vs Generative AI (4:33)
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Supervised vs Unsupervised (11:51)
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Hands-on 1 (24:01)
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Generative AI DEMO (1:46)
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Deeplearning (26:32)
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GENERATIVE AI (3:30)
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Basic math related to Linear Regression (1:49)
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Program Mindset Session (7:20)
Python for Machine Learning
Day 3: Linear Algebra and Calculus for ML
Day 4: Supervised and Unsupervised Learning
Day 5: Model Evaluation and Cross-Validation
Day 6: Introduction to Neural Networks
Day 7: Convolutional Neural Networks (CNNs)
Day 8: Recurrent Neural Networks (RNNs)
Day 9: LSTM and GRU Networks
Day 10: Autoencoders and Variational Autoencoders (VAEs)
Day 11: Introduction to GANs
Day 12: Deep Convolutional GAN (DCGAN)
Day 13: Wasserstein GAN (WGAN)
Day 14 - Conditional GANs (cGANs)
Day 15 - CycleGAN
Event Recordings
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Day 16 - Introduction to Language Models (60:44)
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Day 17 - Transformer Architecture (66:44)
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Day 18 - Hugging Face and Pre-trained Models (27:43)
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Day 19 - Text Summarization and Question Answering (58:25)
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Day 20 - Machine Translation (31:25)
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Day 21- Introduction to Retrieval-Augmented Generation (RAG) (62:39)
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Day 22 -LangChain for LLMs (45:20)
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Day 23 - Few-shot and Zero-shot Learning (26:21)
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Day 24 - Prompt Engineering (39:03)
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Day 25 - Advanced Fine Tuning Techinques (38:53)
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Day 26 - Generative Models for Healthcare Applications (42:19)
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Day 27 - Generative Models for Scientific Applications (70:27)
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Day 28 - Generative Models for Art and Design (58:14)
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Day 29 - Generative Models for Audio and Time Series (49:22)
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Day 30 - Real-world - Final Project (27:42)
