Day 1
Session 1: Introduction to Machine Learning and Python

• What is machine learning? Key concepts and types
• Overview of Python’s ecosystem for ML (NumPy, Pandas, Scikit-learn)
• Hands-on lab: Setting up Python and exploring libraries

Session 2: Data Preprocessing for Machine Learning

• Cleaning and preparing data with Pandas
• Handling missing values and categorical variables
• Hands-on lab: Preprocessing a dataset for analysis

Session 3: Building Your First Machine Learning Model

• Introduction to Scikit-learn and its API
• Training and testing a simple regression model
• Hands-on lab: Building a linear regression model

Day 2
Session 1: Classification Models in Machine Learning

• Overview of classification algorithms (e.g., Logistic Regression, Decision Trees)
• Evaluating classification models with metrics (e.g., accuracy, precision, recall)
• Hands-on lab: Training and evaluating a classification model

Session 2: Model Evaluation and Tuning

• Techniques for splitting datasets: Train-test and cross-validation
• Improving model performance through hyperparameter tuning
• Hands-on lab: Evaluating and tuning a machine learning model

Session 3: Applications and Next Steps

• Real-world applications of machine learning
• Exploring advanced topics and resources for further learning
• Group discussion: Identifying machine learning opportunities in your field