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