Artificial Intelligence Fundamentals with R

Curious about artificial intelligence but not sure where to start? Discover how the R programming language, renowned for its statistical power, provides a clear and accessible path into the world of machine learning. This course demystifies AI, guiding you through essential theory and practical application. You will learn to prepare data, train predictive models for classification and regression, and evaluate their performance, all within a modern R workflow. You'll finish with the confidence to build your own models and interpret their results. What you'll learn: - Understand the core concepts of Artificial Intelligence, Machine Learning, and the different types of learning algorithms. - Learn to clean, preprocess, and prepare datasets for modeling using key R packages. - Build practical supervised learning models for tasks like prediction (regression) and classification. - Apply unsupervised learning techniques like k-means clustering to discover hidden patterns in your data. - Master the tidymodels framework for a modern, unified, and tidy approach to the entire modeling process. - Practice evaluating your model's performance and interpreting its results to understand what drives its predictions. The course starts with fundamental AI terminology and a review of R data structures. From there, you'll progress through hands-on exercises covering data preparation, model training, and evaluation. This course is designed for absolute beginners. No prior experience in artificial intelligence or machine learning is required, though a basic familiarity with R syntax is beneficial. Begin your journey into the world of AI with R today.