Random Forest Algorithm in Machine Learning

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction–making. Through this training, we are going to learn and apply how the random forest algorithm works and several other important things about it. The course includes the following;

1) Extract the Data to the platform.

2) Apply data Transformation.

3) Bifurcate Data into Training and Testing Data set.

4) Built Random Forest Model on Training Data set.

5) Predict using Testing Data set.

6) Validate the Model Performance.

7) Improve the model Performance using Random Forest.

8) Predict and Validate Performance of Model.

In a world where we generate 2.5 quintillion bytes (1 quintillion bytes 1018 bytes!) every day, it becomes important for people who can read and derive meaning from that data. This course helps you be that person who can derive meaning out of this huge data with organizations. During this course, we would take you through different concepts. One can master the concepts taught during this course so that it runs in the blood of the programmer and gets easygoing for him to apply them in real–life situations. These topics when taught will boost up the confidence and the projects along with the course will add to push that confidence beyond 100%.