Orientation — Technical Kickoff

- Jupyter & Python Setup - Understanding why Python is used in AI (simplicity, libraries, end-to-end stack) - Exploring Jupyter Notebooks: shortcuts, code + text blocks, and cloud tools like Google Colab - Hands-On with Arrays, Vectors, and Tensors - Creating and manipulating 2D and 3D NumPy arrays (reshaping, indexing, slicing) - Performing matrix operations: element-wise math and dot products - Visualizing vectors and tensors in 2D and 3D space using matplotlib - Mathematical Foundations in Practice - Exponentiation and logarithms: visual intuition and matrix operations - Normalization techniques and why they matter in ML workflows - Activation functions: sigmoid and softmax with coding from scratch - Statistics and Real Data Practice - Exploring core stats: mean, standard deviation, normal distributions - Working with real datasets (Titanic) using Pandas: filtering, grouping, feature engineering, visualization - Preprocessing tabular data for ML: encoding, scaling, train/test split - Bonus Topics - Intro to probability, distributions, classification vs regression - Tensor intuition and compute providers (GPU, Colab, cloud vs local)