Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Updated Jan 14, 2019

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 modelsâfirstly, 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. 

Target Audience

The course is for data analysts or data scientists, software engineers, and developers interested in learning advanced Predictive Analytics with Python. Business analysts/business Intelligence experts who would like to learn how to go from basic predictive models to building advanced models to produce better predictions will also find this course indispensable. Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course. 

Business Outcomes

  • Improve the performance of Predictive Analytics models by using ensemble methods
  • Learn to use important techniques to improve the performance of your predictive modelssuch as feature selection, dimensionality reduction, and cross-validation
  • Build Neural Networks models and master the basics of the exciting field of Deep Learning