ML Foundations I - ML Basics

Learn the fundamental concepts of machine learning through this comprehensive tutorial covering the essential building blocks of ML theory and practice. Explore the key distinctions between artificial intelligence and machine learning, then dive into the various types of machine learning approaches. Master the critical skills of data understanding, cleaning, and preparation, including techniques for handling ordinal and nominal data types and data clipping methods. Discover supervised learning principles and gain hands-on experience with decision trees as a foundational algorithm. Get introduced to MLOps practices for managing machine learning workflows in production environments. Develop expertise in performance measurement techniques to evaluate machine learning models effectively, learn strategies for addressing data imbalances that can skew results, and understand cross-validation and bootstrapped confidence intervals for robust model assessment.