This course introduces the concepts and algorithms that enable computers to learn from and make predictions based on data. The curriculum covers the three primary paradigms: supervised, unsupervised, and reinforcement learning. Students explore fundamental algorithms such as linear regression, decision trees, support vector machines, and neural networks. The course emphasizes feature engineering, model evaluation metrics like accuracy and precision, and the challenges of overfitting and underfitting. By the end of the semester, students will be capable of developing predictive models and implementing data-driven solutions for complex problems such as pattern recognition, autonomous navigation, and predictive maintenance, providing a critical skill set for the era of big data and artificial intelligence.