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Machine Learning – Bagging and Random Forest
This course introduces Ensemble techniques and apply the concepts of Bagging and Random Forest to find solutions to real-world problems. and predictability of the machine learning model.
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1 Video
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2 Data Cases
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1 Reading
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1 Quiz
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Difficulty: Advanced
Prior education or professional experience required
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Duration: Approximately 5 hours
Course Overview
- The course familiarizes the learner to understand the working principle of Bagging ensemble technique
- The course deep dive into the concept of Random Forest, an algorithm built on the Bagging ensemble technique.
- Learn the concept of variance tradeoff in Bagging algorithms.
- Gain in-depth information on pros and cons of Bagging algorithms.
What’s included
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills you will learn
Associated Learning Tracks
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
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Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
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