Start Step-1
Covers statistics
Linear algebra
Calculus
Optimization
Step-2
Strong math skills in statistics, linear algebra, and calculus are key to understanding AI models and optimization.
Step-3
Master Python, TensorFlow, and PyTorch for AI development, focusing on data manipulation and algorithms.
Learning Python
TensorFlow
PyTorch
Data manipulation
Step-4
Clean, transform, and optimize data for AI by mastering feature engineering and handling large datasets.
Data cleaning
Transformation
Handling large datasets
Step-5
Learn feedforward, convolutional, and recurrent neural networks to build deep learning models for AI tasks.
Covers feedforward
Convolutional
Recurrent networks
Step-6
Explore VAEs, GANs, and diffusion models to generate realistic images, text, and synthetic data in AI.
Covers VAEs
GANs
Diffusion models
Step-7
Understand GPT, BERT, and Transformer models to build powerful AI for text generation, NLP, and automation.
Architectures of BERT
Transformer models
Architectures of GPT
Step-8
Master tokenization, embeddings, and conversational AI to build chatbots, text summarization, and NLP models.
Tokenization
Embeddings
Conversational AI
Step-9
Learn GANs, stable diffusion, and video synthesis to create AI-generated images, videos, and 3D models.
Covers GANs
Stable diffusion
Video synthesis
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Step-10
Ensure fair AI by understanding bias, ethical considerations, and regulations for responsible AI development.
Step-11
End