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  • Use ensemble algorithms to combine many individual predictors to produce better predictions.
  • Apply advanced techniques such as dimensionality reduction to combine features and build better models.
  • Evaluate models and choose the optimal hyper-parameters using cross-validation.
  • Learn the foundations for working and building models using Neural Networks.
  • Learn different techniques to solve problems that arise when doing Predictive Analytics in the real world

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.

  • 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
Course Length 3 hours 44 minutes
ISBN 9781788295321
Date Of Publication 30 Nov 2017


Alvaro Fuentes

Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an M.S. in quantitative economics. He worked for many years in the Central Bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as business, education, medicine, and mass media, among others.

He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.