Phase 1 – Building Blocks

Subject	Core Concepts to Apply	Project/Application Use Case (E-commerce & Fintech)
Mathematical Foundations for ML (AIMLZC416)	Vector algebra, Gradient descent, & PCA.	ML System Optimization: Using PCA for dimensionality reduction to optimize the performance of the ClickHouse-powered analytics dashboard you built at xyz.
Introduction to Statistical Methods (AIMLZC418)	Bayes Theorem, Hypothesis Testing, & Time series analysis.	Inventory Forecasting: Applying ARIMA/SARIMA time series models to your SKU-level inventory API at abc to predict stock-outs with higher statistical significance.
Machine Learning (AIMLZG565)	Supervised/Unsupervised Learning & Ensemble methods	Intelligent Fraud Detection: Utilizing Random Forest or SVMs to enhance the real-time reconciliation system designed for upi payments at xyz.
Deep Neural Networks (AIMLZG511)	Backpropagation, CNNs, & Transformers.	B2B Communication Intelligence: Using Transformer-based NLP models to extract intent and automate tracking from the Order Notes API you designed.

Pages: 12345