Foundations of Machine Learning

Welcome to the Foundations of Machine Learning, your practical guide to fundamental techniques powering data-driven solutions. Master key ML domains—supervised learning (prediction), unsupervised learning (pattern discovery), data preprocessing & feature engineering, and time series forecasting—using Pandas, Scikit-learn, Statsmodels, and Prophet to tackle real-world challenges. By the end of this course, you'll be able to: - Implement and evaluate key supervised models (e.g., regression, classification, Tree-based models & SVMs) for prediction. - Apply unsupervised methods (e.g., K-Means, Isolation Forest) for segmentation and anomaly detection. - Perform robust data preprocessing: handle missing data, encode categoricals, scale features, and apply dimensionality reduction (PCA). - Build and analyze time series forecasts with ARIMA, Exponential Smoothing, Holt-Winters and Prophet. Through hands-on exercises and a capstone customer purchase prediction project, you'll develop versatile skills to confidently address common machine learning challenges

• Master supervised and unsupervised learning, data preprocessing, and feature engineering
• Apply unsupervised methods for segmentation and anomaly detection
• Perform robust data preprocessing including handling missing data and feature scaling
• Build and analyze time series forecasts with advanced models

• Basic familiarity with Python syntax and linear algebra concepts