👋 Hi, This is @SANTONLAR
👀This is a repository with the exercises that appear on the book "Introduction to machine learning with R". It will help you gain a solid foundation in machine learning principles. Using the R programming and then move into more advanced topics such as neural networks and tree-based methods.
Once you develop a familiarity with topics such a the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. burger provides several examples to help you build a working knowledge of machine learning.
We will learn:
- To explore the machine learning algorithms for supervised and unsupervised cases.
- Understand machine learning algorithms for supervised and unsupervised cases.
- Examine statistical concepts for designing data for use in models.
- Dive into linear regression models used in business and science.
- Use single-layer and multilayer neural networks for calculating outcomes.
- Look at how tree-based models work, including popular decision trees.
- Get a comprehensive view of the machine learning ecosystem in R.
- Explore the powerhouse of tools available in R's caret package.