Advanced Predictive Techniques with Scikit-Learn and TensorFlow: Use advanced techniques to produce more powerful models

Alvaro Fuentes

$130.99

Video Nov 2017 3hrs 44mins 1st Edition

Alvaro Fuentes

$130.99

Video Nov 2017 3hrs 44mins 1st Edition

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Key benefits

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

Description

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.

Who is this book for?

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.

What you will learn

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

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