Objective

Understand foundational machine learning concepts and introductory predictive modeling techniques.

Topics Covered

1. Introduction to Machine Learning
   • Supervised vs unsupervised learning concepts
2. Common Machine Learning Algorithms
   • Regression, classification, and clustering basics
3. Model Training & Evaluation
   • Testing, validation, and performance measurement
4. Model Optimization Concepts
   • Overfitting, underfitting, and tuning basics
5. Python for Data Science
   • Introduction to Pandas, NumPy, and Scikit-learn

Activity

Develop and evaluate a basic predictive model using Python tools.