A simple approach aimed at introducing Extreme Gradient boosting to anyone interested in learning the concepts behind the model rather than just using it as a black box.

XGBoost is an implementation of gradient boosting that is being used to win Machine Learning competitions. It is powerful but could be hard to get started. In this tutorial you will discover 7 keys to scaling with XGBoost in Python.

This short tutorial is prepared for Python Machine Learning Beginners that look forward to improving their Models’ handling and implementation. The kernel comes with a detailed Python implementation of XGBoost using UC Irvine publicly available dataset on Pima Indians onset of Diabetes dataset.

Kindly recommend it to beginners in Data Science. They will enjoy the time spent learning the concepts behind XGBoost. Do well to check it out too. I will appreciate your comment and contribution to improving the kernel. I look forward to receiving insights from Leaders in Data Science that would be assessing it. I believe you will find the Kernel useful, therefore, do not forget to Upvote.

Check the Kernel in the link below.

Python has been the go-to choice for Machine Learning, Data Science and Artificial Intelligence developers for a long time. Python libraries for modern machine learning models & projects: TensorFlow; Numpy; Scipy; Scikit-learn; Theano; Keras; PyTorch; Pandas; Matplotlib; ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’d like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data.

Python For Data Analysis - Build a Data Analysis Library from Scratch - Learn Python in 2019