General Boosting & Bagging Training
- Course Content
- Drop us a Query
General Boosting and Bagging course provides in-depth insight on creating and implementing prediction functions, which includes a strong focus on the practical application of machine learning using boosting and bagging methods. Focusing on the path that include a range of model based and algorithmic machine learning methods such as Random Forest, Boosting & Bagging methods and Support Vector Machines. Moreover, the training is beneficial for the candidates willing to make their career flourished in the vast field of data analytics, via implementing the machine learning ethics to give their work the touch of advancement.
Here are some of the important aspects that will help the candidates in understanding the importance of General Boosting and Bagging training in their career:
- The candidates will learn about implementing the prediction functions with a strong focus on the practical application of machine learning.
- After the training they would be able to learn how to use boosting and bagging methods effectively.
- The training insight on the use of algorithmic machine learning such as: Support Vector Machines, Random Forest, Boosting and Bagging methods.
- It offers in-depth insight on the analysis of the credit data to perform well in the real time scenario
- The course modules enable insight on the Titanic Accident
- The training helps to understand the categorical variables and continuous variables
Professionals from IT field who have to deal with the logical formulas and wish to learn the ethics of Machine Learning
Prior opting General Boosting and Bagging training, the candidates should have the fundamental understanding of statistics, classification and regression techniques, Language of R, and the basics of machine learning.
- Decision Tree Ensembles: Bagging and Boosting
- The Case Study: Analysis of Credit Data
- The Case Study: The Titanic Accident
- The Case Study: Comparing Algorithms
1. Decision Tree Ensembles: Bagging and Boosting
- The Decision tree Review
- Bias Variance Tradeoffs
2. The Case Study: Analysis of Credit Data
- Analysis of credit Data: Categorical Variables and EDA_Cross_Tabs_Continuous Variables
3. The Case Study: The Titanic Accident
- About The Titanic Accident
4. The Case Study: Comparing Algorithms
- About Comparing Algorithm