Machine Learning Series: The XGBoost Algorithm in Python | Technics Publications


Machine Learning Series: The XGBoost Algorithm in Python | Technics Publications
English | Size: 206.49 MB
Genre: eLearning

Dhiraj, a data scientist and machine learning evangelist, continues his teaching of machine learning algorithms by explaining through both lecture and practice the XGBoost (eXtreme Gradient Boosting) Algorithm in Python. Click here to watch all of Dhiraj Kumar’s machine learning videos. Learn all about XGBoost using Python and the Jupyter notebook in this video series covering these seven topics:

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.

nitroflare.com/view/ACC013B51B0DB77/TP.Machine.Learning.Series.The.XGBoost.Algorithm.in.Python.rar

rapidgator.net/file/d8a6da3d61e7fe4188722f24426df5f2/TP.Machine.Learning.Series.The.XGBoost.Algorithm.in.Python.rar.html

If any links die or problem unrar, send request to
goo.gl/t4uR9G

About WoW Team

I'm WoW Team , I love to share all the video tutorials. If you have a video tutorial, please send me, I'll post on my website. Because knowledge is not limited to, irrespective of qualifications, people join hands to help me.

Speak Your Mind

This site uses Akismet to reduce spam. Learn how your comment data is processed.