Boosting Machine Learning Models in Python [Video]
Machine learning ensembles are models composed of a few other models that are trained separately and then combined in some way to make an overall prediction. These powerful techniques are often used in applied machine learning to achieve the best overall performance.
In this unique course, after installing the necessary tools you will jump straight into the bagging method so as to get the best results from algorithms that are highly sensitive to specific data—for example, algorithms based on decision trees. Next, you will discover another powerful and popular class of ensemble methods called boosting. Here you'll achieve maximal algorithm performance by training a sequence of models, where each given model improves the results of the previous one. You will then explore a much simpler technique called voting, where results from multiple models are achieved using simple statistics such as the mean average. You will also work hands-on with algorithms such as stacking and XGBoost to improve performance.
By the end of this course, you will know how to use a variety of ensemble algorithms in the real world to boost your machine learning models.
Please note that a working knowledge of Python 3; the ability to run simple commands in Shell (Terminal); and also some basic machine learning experience are core prerequisites for taking and getting the best out of this course.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Boosting-Machine-Learning-Models-in-Python
|Course Length||3 hours 7 minutes|
|Date Of Publication||19 Dec 2019|
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