Machine Learning Foundations is a beginner-friendly course designed to help you understand how computers learn from data, identify patterns, make predictions, and support automated decision-making. Whether you are exploring a career in artificial intelligence, data science, analytics, or software development, this course provides the essential knowledge needed to understand the complete machine learning workflow.
You will begin by learning what machine learning is, how it differs from traditional programming, and why it has become important across modern industries. You will explore the major types of machine learning, including supervised and unsupervised learning, and examine common applications such as fraud detection, customer segmentation, recommendation systems, forecasting, image recognition, and predictive maintenance.
The course then focuses on data preparation and feature engineering. You will learn why data quality has a major impact on model performance and how datasets are cleaned, transformed, and organized before training. You will explore missing values, categorical variables, numerical features, scaling, and feature selection. You will also understand the purpose of training, validation, and testing datasets and how proper data splitting helps prevent misleading results.
