Random Forest Algorithm in Machine Learning
2.0 (46 ratings)
13,859 students enrolled

# Random Forest Algorithm in Machine Learning

mprove the model Performance using Random Forest.
2.0 (46 ratings)
13,859 students enrolled
Published 2/2019
English
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Current price: \$25.99 Original price: \$39.99 Discount: 35% off
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This course includes
• 1.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data.
• Through this training we are going to learn and apply how the random forest algorithm works and several other important things about it
Course content
Expand all 13 lectures 01:19:07
+ Getting Started
6 lectures 39:36
06:43
06:09
Evaluate an Algorithm using a Cross Validation Split
08:18
Calculate the Gini index for a Split Dataset
06:16
Select the Best Split Point for a Dataset
04:51
+ Node Value and Subsample
6 lectures 29:10
Build a Decision Tree
06:28
Create a Random Subsample
03:35
Random Forest Algorithm
03:07
Test the Random Forest Algorithm on Sonar Dataset
03:31
Evaluate Algorithm
06:02
Requirements
• Basic Machine learning concepts and Python.
Description

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.

Who this course is for:
• Aspiring Data Scientists
• Artificial Intelligence/Machine Learning/ Engineers