XGBoost is the most winning supervised machine learning approach in competitive modeling on structured datasets. This course will teach you the basics of XGBoost, including basic syntax, functions, and implementing the model in the real world.
At the core of applied machine learning is supervised machine learning. In this course, Machine Learning with XGBoost Using scikit-learn in Python, you will learn how to build supervised learning models using one of the most accurate algorithms in existence. First, you will discover what XGBoost is and why it’s revolutionized competitive modeling. Next, you will explore the importance of data wrangling and see how clean data affects XGBoost’s performance. Finally, you will learn how to build, train, and score XGBoost models for real-world performance. When you are finished with this course, you will have a foundational knowledge of XGBoost that will help you as you move forward to becoming a machine learning engineer.
Course Overview [Autogenerated] Hello. My name is Mike West and welcome to my course machine learning with extra boost using second Learning Python Artificial neural networks are getting all the attention. A class of models known as Grady and boosters are doing all the winning in that competitive modeling space. The most famous Grady in booster ____ boost extra boost its implementation of Grady and boosted decision trees designed for speed and performance. Extra boost stands for extreme Grady and boosting. Additionally, because so much of applied machine learning is supervised, extra boost is being widely adopted as the model of choice for highly structured data sets in the real world. This course will provide you with the foundation you'll need to build holly performance models Using actually boost. This course will introduce you to decision trees Decision Trees Air used as the base model next to boost Decision Trees building ensemble model that offers better predictability than the base model. You learn how machine learn engineers massage their data and the highly structured, highly cleansed to raise that machine learning models understand. You also learn how data segmented into training and test sets separating your data is critical to avoid over fitting boosting algorithms like extra boost are prone to over fitting over. Fitting happens when the model Lauren's the data to well, once your data has been cleansed, the actually boost model train and tested on fresh data, you'll learn how to persist or save those models to disk. The gold standard for saving models and python is called pickle. Every step in the machine learning process is critical to building highly accurate models an extra boost. I hope you'll join me on this journey to learn more about extra boost in Python plural site.