Data Science: Building Machine Learning Models and Recommendation Systems

Recommendation engines power the modern web, driving user engagement across major entertainment and e-commerce platforms. Understanding how to build these predictive systems is an essential skill for any aspiring data scientist. In this text-only course, you will transition from understanding raw data structures to designing, testing, and evaluating a fully functional recommendation system. What you'll learn: - Learn the core concepts of supervised and unsupervised machine learning models. - Build a collaborative filtering recommendation system using Python. - Apply modern dataframe libraries to clean, preprocess, and analyze datasets. - Understand the fundamentals of vector databases and similarity search for modern recommendations. - Evaluate model performance using precision, recall, and root mean square error (RMSE). - Practice implementing basic model deployment concepts to serve predictions. You will begin by learning fundamental data science terminology and mathematical concepts before moving step-by-step through data preprocessing, model selection, algorithm implementation, and evaluation techniques. This course is designed for beginners who want a clear, conceptual, and practical introduction to machine learning, and no prior data science experience is required. Start reading today to build your first predictive machine learning model.