Decision Trees Modeling & Supervised Learning using R
- No prior knowledge of machine learning required
- Basics of R
The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. This course includes learning decision tree modeling which are used by data scientists or people who inspire to be the data scientist. The tutorials will include the following;
Decision Tree Theory
Implementation using R Decision Tree Classification
Decision Tree Regression
Decision Tree in R is a machine-learning algorithm that can be a classification or regression tree analysis. The decision tree can be represented by graphical representation as a tree with leaves and branches structure. The leaves are generally the data points and branches are the condition to make decisions for the class of data set. Decision trees in R are considered as supervised Machine learning models as possible outcomes of the decision points are well defined for the data set. It is also known as the CART model or Classification and Regression Trees. There is a popular R package known as rpart which is used to create the decision trees in R.
Who this course is for:
- Anyone who wants to learn about data and analytics
- Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
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