Data Science: Machine Learning and Statistical Modeling in R

Master machine learning techniques with R to solve Real-World problems and gain valuable insights from your data.

What you'll learn

Master prediction and model assessment
Work with R, the language of data science
Gain deep insights into the application of machine learning tools in the industry
Understand and apply machine learning methods using an extensive set of R packages
Implement advanced concepts in machine learning
Understand how working with complex data is different to standard numerical work
Manipulate data in R efficiently to prepare it for analysis
Master the skill of recognizing techniques for effective visualization of data
Understand why and how to create test and training data sets for analysis

Requirements

No prerequisites, knowledge of some undergraduate level mathematics would be an added advantage

Description

In this course, we will teach you advanced techniques in machine learning with the latest code in R. Now is the time to take control of your data and start producing superior statistical analysis with R. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning and more.

This course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. This course aims to excite you with awesome projects focused on analysis, visualization, and machine learning. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, and more. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.

This course supplies in-depth content that put the theory into practice. You know you need to upgrade your skills to stay relevant. Don’t wait. Enroll in this course today.

Who this course is for:

The course is intended for both students and professionals. Specifically anyone with none or minimal prior experience with programming.