Technical Orientation (Python, Numpy, Probability, Statistics, Tensors)

- How AI Thinks in Numbers: Dot Products and Matrix Logic - NumPy Power-Tools: The Math Engine Behind Modern AI - Introduction To Machine Learning Libraries - Two and Three Dimensional Arrays - Data as Fuel: Cleaning, Structuring, and Transforming with Pandas - Normalization in Data Processing: Teaching Models to Compare Apples to Apples - Probability Foundations: How Models Reason About the Unknown - The Bell Curve in AI: Detecting Outliers and Anomalies - Evaluating Models Like a Scientist: Bootstrapping, T-Tests, Confidence Intervals - Transformers: The Architecture That Gave AI Its Brain - Diffusion Models: How AI Creates Images, Video, and Sound - Activation Functions: Teaching Models to Make Decisions - Vectors and Tensors: The Language of Deep Learning - GPUs, Cloud, and APIs: How AI Runs in the Real World