Everything about Random Forest Model
Instructors: AI Monks
Validity Period: 365 days
40% Cashback as Credits
Why this course?
Do you know that the Random Forest model is the most commonly applied ensemble machine learning technique for classification and regression? This is also one of the most popular techniques to be asked in data analytics and data scientist job interviews.
In this course on the Random Forest model, we have covered the fundamental concept of Random Forest and tried to understand the implementation and usage in the real world.
Who should opt for this course?
Comprehensive Course Coverage
This course covers Random Forest in great depth and covers the following aspects:
In case of any query, please reach out to us at firstname.lastname@example.org
|Introduction to Random Forest Model|
|Introduction to Random Forest Model (3 pages)|
|Understanding the Random Forest Model|
|Decision Trees (3 pages)|
|Information Gain and Gini Index (4 pages)|
|Random Forest (3 pages)|
|Interpreting and Tuning the Random Forest Model|
|Variable Importance (3 pages)|
|Model Checks (2 pages)|
|Tuning the Model (2 pages)|
|Building Random Forest Model in Python|
|Data Preparation (3 pages)|
|Extracting the Variables (5 pages)|
|Training and Evaluating the Model (3 pages)|
|Tuning and Improving the Model (5 pages)|
|Learning Random Forest Through Case Study|
|Learning Random Forest Through Case Study (5 pages)|
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