Free Certification Course Title: Decision Trees, Random Forests, AdaBoost & XGBoost in Python
Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python
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What you’ll learn:
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Get a solid understanding of decision tree
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Understand the business scenarios where decision tree is applicable
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Tune a machine learning model’s hyperparameters and evaluate its performance.
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Use Pandas DataFrames to manipulate data and make statistical computations.
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Use decision trees to make predictions
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Learn the advantage and disadvantages of the different algorithms
Requirements:
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Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Who this course is for:
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience
- Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time
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After completing this course you will be able to:
- Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.
- Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost
- Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.
- Confidently practice, discuss and understand Machine Learning concepts
This course includes:
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7 hours on-demand video
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3 articles
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18 downloadable resources
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Full lifetime access
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Access on mobile and TV
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How to Subscribe for Decision Trees, Random Forests, AdaBoost & XGBoost in Python?
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