Machine Learning Fundamentals in R

This introduction to machine-learning is intended for beginners. It covers four of the most well-known classification algorithms. This course will provide a solid understanding of the approach each algorithm takes to learning tasks and the R functions that are required to apply these tools to your own work.

Regression is a method that uses inputs to predict numerical outcomes. This is machine learning. This course will help you learn about regression models, how to train them in R, and how to make predictions with them.

Machine learning is often used to find patterns in data. It is impossible to predict the future. This is unsupervised learning. This can be used to identify the unsupervised learning that is being done to target marketing campaigns by grouping consumers based on their buying history and demographics. Another example is to determine the unmeasured factors that affect differences in crime rates between cities. This course will give you a general introduction to clustering and dimension in R from a machine learning perspective. This course will allow you to quickly get data into insight.

Machine learning is the study and application of algorithms that can learn data and make predictions. It is used in all aspects of our lives, including search results and self-driving cars. This is also one the fastest-growing areas of data science research. This course covers the basics of machine learning. This course teaches you how to create, evaluate, tune, and improve predictive models. This course uses the popular R package, which provides a consistent interface to all R's most powerful machine learning tools.

Tidymodels is a powerful suite of R packages that simplifies machine-learning workflows. Split data for cross validation, preprocess data with tidymodels' recipe pack and fine-tune machine learning algorithms. You will learn key concepts such as how to create modeling workflows and define model objects. Next, use your knowledge to predict home prices and classify employees according to their likelihood of leaving the company.

Tree-based machine learning models can reveal complex, nonlinear data relationships. They often win machine-learning competitions. This course will show you how to use tidymodels in order to create different tree-based models. These can range from simple decision trees to complex random forests. You will also learn how to use the powerful machine-learning technique of boosted tree that uses ensemble learning to create highly-performing predictive models. Learn how credit and health data can be used to predict customer churn or diabetes.

Machine learning is a method that predicts the future. This track covers the prediction of categorical and numerical responses through regression and classification, as well as uncovering the hidden structure in datasets (unsupervised Learning). You will learn how to model data, how you can train models, how your models are visualized and evaluated, and how you can tune their parameters to improve performance.