R Programming for Data Science & Machine Learning Course Overview

The R Programming for Data Science & Machine Learning course offers a comprehensive guide for learners to master data analysis, statistical modeling, and machine learning using the R language. This course begins with Module 1, laying the groundwork with R Programming basics, including arithmetic operations, variables, vectors, and essential data types. It then progresses through modules that cover complex data structures like matrices, data frames, and lists, as well as Data Processing techniques including file handling and web scraping.

Each module builds upon the previous, integrating R programming concepts with practical applications, such as logical operators, loops, and functions. As participants delve into Module 6, they focus on data manipulation and visualization using powerful libraries like dplyr, ggplot2, and Plotly, essential for insightful data analysis. Module 7 transitions into supervised machine learning, exploring algorithms for both regression and classification tasks. The course culminates with Module 8's exploration of unsupervised machine learning and Deep Learning, empowering learners to tackle complex data science challenges with confidence. By the end of this course, students will be well-equipped with the skills needed for R programming for data science and machine learning.