Last Updated on February 3, 2024 by GeeksGod
Course : Random Forest Algorithm & Supervised Learning using Python
Free Udemy Coupon: Random Forest Algorithm
What is 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.
Introduction to Random Forest Algorithm
Through this training, we are going to learn and apply the random forest algorithm and several other important things about it. The random forest algorithm is a supervised learning method that offers an accurate way of predicting results using subsets of data, split from the global dataset. It uses multi-various conditions and flows through numerous decision trees, providing a perfect unsupervised data model platform for both classification or regression cases as applicable.
Course Outline
The course includes the following:
- Extract the Data to the platform
- Apply Data Transformation
- Bifurcate Data into Training and Testing Data set
- Built Random Forest Model on Training Data set
- Predict using Testing Data set
- Validate the Model Performance
- Improve the Model Performance using Random Forest
- Predict and Validate Performance of Model
Benefits of Random Forest Algorithm
Random forest in Python offers several benefits:
- Accurate method of predicting results
- Uses subsets of data split from the global dataset
- Handles high dimensional data without the need for pre-processing or transformation of the initial data
- Allows parallel processing for quicker results
How Does Random Forest Algorithm Work?
The unique feature of the random forest algorithm is supervised learning. What it means is that data is segregated into multiple units based on conditions and formed as multiple decision trees. These decision trees have minimal randomness (low entropy), are neatly classified and labeled for structured data searches and validations. Little training is needed to make the data models active in various decision trees. This makes the random forest algorithm a powerful tool for data analysis and prediction.
Enroll in the Course Today!
Don’t miss the opportunity to learn about the random forest algorithm for free! Enroll in this Udemy course today and enhance your machine learning skills.
