Length 3h 44m With Project Files MP4
Improve the performance predictive models, build more complex models and use techniques to improve quality of your predictive models.
About This Video
Improve the performance of Predictive Analytics models by using ensemble methods
Learn to use important techniques to improve the performance of your predictive models—such as feature selection, dimensionality reduction, and cross-validation
Build Neural Networks models and master the basics of the exciting field of Deep Learning
Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems.
When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions.
Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library.
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