» » Machine Learning Series The XGBoost Algorithm in Python

Machine Learning Series The XGBoost Algorithm in Python

Machine Learning Series The XGBoost Algorithm in Python
Machine Learning Series: The XGBoost Algorithm in Python
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 186 MB
Genre: eLearning | Language: English


Introducing XGBoost. This first topic in the XGBoost (eXtreme Gradient Boosting) Algorithm in Python series introduces this very important machine learning algorithm. Gradient boosting is a machine learning technique for regression and classification problems. Learn about the reasons for using XGBoost, including accuracy, speed, and scale. Understand ensemble modeling and how it can improve the overall performance of a machine learning model. Apply the concepts of bagging and boosting, and learn about AdaBoost and Gradient boosting.
XGBoost Benefits. This second topic in the XGBoost Algorithm in Python series covers where XGBoost works well. XGBoost guarantees regularization (which prevents the model from overfitting), supports parallel processing, provides a built-in capacity for handling missing values, and excels at tree pruning and cross validation.
Installing XGBoost. This third topic in the XGBoost Algorithm in Python series covers how to install the XGBoost library. It is recommended to be using Python 64 bit. Become proficient in installing Anaconda and the XGBoost library on Windows, Linux, and Mac OS.
XGBoost Model Implementation in Python. This fourth topic in the XGBoost Algorithm in Python series covers how to implement the various XGBoost linear and tree learning models in Python. Practice applying the XGBoost models using a medical data set.
XGBoost Parameter Tuning in Python. This fifth topic in the XGBoost Algorithm in Python series covers how to tune the various parameters that exist in Python. Parameter tuning is the art in machine learning. Follow along and practice applying the three categories of parameter tuning: Tree Parameters, Boosting Parameters, and Other Parameters. Become proficient in a number of parameters including max_depth, min_samples_leaf, and max_features,
XGBoost Model Evaluation Method in Python. This sixth topic in the XGBoost Algorithm in Python series shows you how to evaluate an XGBoost model. Follow along and practice applying the two most important techniques of Train Test Split and Cross Validation.
XGBoost Prediction in Python. This seventh topic in the XGBoost Algorithm in Python series shows you how to perform predictions using the XGBoost algorithm.








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