Advanced Predictive Techniques with Scikit-Learn and TensorFlow [Video]
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.Style and Approach
This course presents some of the most advanced Predictive Analytics tools, models, and techniques currently having a big impact on every industry. The main goal is to show the viewer how to improve the performance of predictive models—firstly, by showing how to build more complex models and secondly, by showing how to use related techniques that dramatically improve the quality of predictive models.
|Course Length||3 hours 44 minutes|
|Date Of Publication||30 Nov 2017|