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)