Intermediate Data Manipulation and Machine Learning

In this comprehensive course, you will explore artificial intelligence (AI) and its core concepts, forming a solid foundation for machine learning. You will delve into regression analysis, applying univariate, polynomial, and multivariate regression techniques to real-world problems through interactive labs. Next, you will learn model preparation and evaluation, focusing on underfitting, overfitting, data splitting, and resampling methods, alongside regularization techniques to enhance model performance. The course covers classification methods, including confusion matrices, ROC curves, decision trees, random forests, logistic regression, and support vector machines, all paired with practical labs. You will also explore ensemble models and association rules, like the Apriori algorithm, to uncover hidden data patterns. Designed for data scientists, machine learning enthusiasts, and technical professionals, this course requires a basic understanding of machine learning concepts and Python programming. Learning outcomes include grasping AI and machine learning fundamentals, applying regression analysis, building and evaluating models, implementing classification techniques, performing clustering and dimensionality reduction, uncovering patterns with association rules, and applying reinforcement learning principles.