Day 1: Foundations of Machine Learning

Session 1: Introduction to Machine Learning

• What is machine learning? Key concepts and terminology
• Overview of machine learning types: Supervised, unsupervised, and reinforcement learning

Session 2: The Machine Learning Workflow

• Steps in a typical machine learning project
• Practical demonstration: Understanding data preprocessing and feature engineering

Session 3: Introduction to Supervised Learning

• Overview of algorithms: Linear regression and decision trees
• Hands-on lab: Implementing a simple supervised learning model

Day 2: Unsupervised Learning and Algorithms

Session 1: Introduction to Unsupervised Learning

• Key concepts and applications of clustering and dimensionality reduction
• Practical demonstration: Visualizing clustering results

Session 2: Key Algorithms in Machine Learning

• Understanding k-Means, PCA, and support vector machines
• Hands-on lab: Applying k-Means for clustering tasks

Session 3: Evaluating Model Performance

• Introduction to evaluation metrics: Accuracy, precision, recall, and F1 score
• Hands-on lab: Evaluating a machine learning model using basic metrics

Day 3: Applications and Industry Use Cases

Session 1: Applications of Machine Learning

• Real-world examples in finance, healthcare, retail, and more
• Group discussion: Identifying potential ML applications in your organization

Session 2: Building a Simple Machine Learning Workflow

• End-to-end implementation of a machine learning project
• Hands-on lab: Building a workflow from data preprocessing to model evaluation

Session 3: Next Steps in Machine Learning

• Overview of advanced topics: Deep learning, NLP, and reinforcement learning
• Discussion: Resources and tools for continued learning in machine learning