Advanced Predictive Techniques with Scikit-Learn and TensorFlow [Video]

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Advanced Predictive Techniques with Scikit-Learn and TensorFlow [Video]

Alvaro Fuentes

Improve the performance predictive models, build more complex models and use techniques to improve quality of your predictive models.
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Video Details

ISBN 139781788295321
Course Length3 hours 44 minutes

Video 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.

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.

Table of Contents

Ensemble Methods for Regression and Classification
The Course Overview
How Ensemble Methods Work?
Bagging, Random Forests, and Boosting for Regression
Bagging, Random Forests, and Boosting for Classification
Cross-Validation and Parameter Tuning
K-fold Cross-Validation
Comparing Models with K-fold Cross-Validation
Hyper-Parameter Tuning in scikit-learn
Working with Features
Feature Selection Methods
Dimensionality Reduction and PCA
Creating New Features
Improving Models with Feature Engineering
Introduction to Artificial Neural Networks and TensorFlow
Introduction to Artificial Neural Networks
Elements of a Deep Neural Network Model
Installation and Introduction to TensorFlow
Core Concepts in TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
Predictions with TensorFlow - Introductory Example
Regression Using Deep Neural Networks
Classification with Deep Neural Networks

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

Authors

Table of Contents

Ensemble Methods for Regression and Classification
The Course Overview
How Ensemble Methods Work?
Bagging, Random Forests, and Boosting for Regression
Bagging, Random Forests, and Boosting for Classification
Cross-Validation and Parameter Tuning
K-fold Cross-Validation
Comparing Models with K-fold Cross-Validation
Hyper-Parameter Tuning in scikit-learn
Working with Features
Feature Selection Methods
Dimensionality Reduction and PCA
Creating New Features
Improving Models with Feature Engineering
Introduction to Artificial Neural Networks and TensorFlow
Introduction to Artificial Neural Networks
Elements of a Deep Neural Network Model
Installation and Introduction to TensorFlow
Core Concepts in TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
Predictions with TensorFlow - Introductory Example
Regression Using Deep Neural Networks
Classification with Deep Neural Networks

Video Details

ISBN 139781788295321
Course Length3 hours 44 minutes
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