Practical Machine Learning with Real Data

Introduction

This course immerses participants in practical machine learning techniques, using real-world datasets to build, evaluate, and deploy effective models.

Objectives

• Understand key machine learning concepts and workflows
• Explore and preprocess real-world datasets for analysis
• Build, train, and tune machine learning models
• Evaluate model performance using appropriate metrics
• Deploy and monitor machine learning solutions in practical scenarios

Course Outlines

Day 1: Foundations of Machine Learning and Data Exploration

• Introduction to machine learning paradigms
• Understanding supervised vs. unsupervised learning
• Overview of real-world datasets and data gathering
• Exploratory data analysis techniques
• Data visualization for insights

Day 2: Data Preparation and Feature Engineering

• Data cleaning and handling missing values
• Encoding categorical variables
• Feature scaling and normalization
• Generating new features for improved models
• Practical exercises with real datasets

Day 3: Building and Training Machine Learning Models

• Overview of common algorithms (regression, classification)
• Splitting data: training, validation, and test sets
• Training models using scikit-learn
• Hyperparameter tuning and cross-validation
• Hands-on model implementation

Day 4: Model Evaluation and Interpretation

• Choosing the right evaluation metrics
• Confusion matrix, ROC curve, and precision-recall
• Model interpretability techniques
• Addressing overfitting and underfitting
• Improving model performance with real data

Day 5: Model Deployment and Real-World Applications

• Introduction to model deployment workflows
• Exporting and saving models
• Building simple APIs for model serving
• Monitoring and updating deployed models
• Case studies and best practices in real-world ML projects