Day 1
Session 1: Introduction to Python for Machine Learning

• Overview of Python and its role in data science and ML
• Setting up the Python environment (Jupyter Notebook, Anaconda)
• Basic Python concepts: variables, data types, functions, and loops

Session 2: Working with Python Libraries for Data Analysis

• Introduction to NumPy for numerical operations
• Using Pandas for data manipulation and analysis
• Loading and exploring datasets with Python

Session 3: Hands-on Lab: Python Basics and Data Handling

• Writing simple Python scripts for data analysis
• Working with data frames, arrays, and basic statistical functions
• Cleaning and preparing data for machine learning models

Day 2
Session 1: Introduction to Machine Learning Concepts

• What is machine learning? Key concepts and workflow
• Supervised vs. unsupervised learning
• Overview of ML algorithms: classification and regression

Session 2: Building Machine Learning Models with Scikit-learn

• Introduction to Scikit-learn library
• Data splitting: training and testing datasets
• Implementing a simple linear regression model

Session 3: Hands-on Lab: Building Your First ML Model

• Applying linear regression to a real-world dataset
• Training, testing, and evaluating the model
• Interpreting model results and performance metrics

Day 3
Session 1: Classification Models and Evaluation Metrics

• Introduction to classification algorithms: logistic regression, decision trees
• Model evaluation techniques: accuracy, precision, recall, F1-score
• Understanding confusion matrices

Session 2: Hands-on Lab: Classification Model Development

• Building a classification model using Scikit-learn
• Applying logistic regression and decision tree algorithms
• Evaluating model performance with real-world data

Session 3: End-to-End Machine Learning Workflow

• Data preprocessing, model building, and evaluation
• Model tuning and improving performance
• Hands-on mini-project: Developing a complete ML solution