Objective

Understand data preparation, model building, and foundational machine learning techniques.

Topics Covered

1. Data Collection & Preprocessing
   • Cleaning, transforming, and preparing datasets
2. Feature Engineering Concepts
   • Selecting and preparing variables for modeling
3. Core Machine Learning Algorithms
   • Regression, classification, and clustering methods
4. Model Training & Evaluation
   • Validation, testing, and performance measurement
5. Introduction to Neural Networks & Deep Learning
   • Basic architecture and concepts

Activity

Build and evaluate a simple machine learning model using sample data.