Regression Machine Learning with R
3.2 (35 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
356 students enrolled

Regression Machine Learning with R

Learn regression machine learning from basic to expert level through a practical course with R statistical software.
3.2 (35 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
356 students enrolled
Created by Diego Fernandez
Last updated 7/2018
English
English [Auto]
Current price: $16.99 Original price: $24.99 Discount: 32% off
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This course includes
  • 5.5 hours on-demand video
  • 10 articles
  • 10 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Read S&P 500® Index ETF prices data and perform regression machine learning operations by installing related packages and running code on RStudio IDE.
  • Create target and predictor algorithm features for supervised regression learning task.
  • Select relevant predictor features subset through Student t-test and ANOVA F-test univariate filter methods.
  • Choose relevant predictor features subset through recursive feature elimination deterministic wrapper method.
  • Designate relevant predictor features subset through least absolute shrinkage and selection operator embedded method.
  • Extract predictor features transformations through principal component analysis.
  • Train algorithm for mapping optimal relationship between target and predictor features.
  • Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics.
  • Calculate generalized linear models such as linear regression or elastic net regression and select optimal linear regression coefficients regularization parameter through time series cross-validation.
  • Compute similarity methods such as k nearest neighbors and select optimal number of nearest neighbors parameter through time series cross-validation.
  • Estimate frequency methods such as decision tree and select optimal maximum tree depth parameter through time series cross-validation.
  • Calculate ensemble methods such as random forest or extreme gradient boosting machine and select optimal number of randomly selected predictors or maximum trees depth parameters through time series cross-validation.
  • Compute maximum margin methods such as linear or non-linear support vector machines and select optimal error term penalization parameter through time series cross-validation.
  • Estimate multi-layer perceptron methods such as artificial neural network and select optimal node connection weight decay regularization parameter through time series cross-validation.
  • Compare regression machine learning algorithms training and testing.
Course content
Expand all 56 lectures 05:42:33
+ Course Overview
7 lectures 36:10

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 05:35

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (algorithm learning, generalized linear models, similarity methods, frequency methods, ensemble methods, maximum margin methods and multi-layer perceptron methods).

Preview 06:17

In this lecture you will learn regression machine learning definition, R statistical software and RStudio Integrated Development Environment (IDE) downloading websites.

Regression Machine Learning
06:44

In this lecture you will learn regression machine learning .TXT data file in .CSV format downloading, .TXT R script code file downloading, advanced forecasting models packages installation (caret, corrplot, e1071, elasticnet, forecast, kernlab, lars, nnet, party, penalized, plyr, quantmod, randomForest, rpart, xgboost) and RStudio Integrated Development Environment (IDE) project creation.

Regression Machine Learning Data
17:23

Before starting course please download .TXT data file in .CSV format as additional resources.

Course Data File
00:03

Before starting course please download .TXT R script code file as additional resources below.

Course Script Code File
00:05

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02
+ Algorithm Learning
10 lectures 01:11:12

You can download .PDF section slides file as additional resources.

Algorithm Learning Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to algorithm learning (algorithm features, features selection, features extraction, algorithm training and algorithm testing).

Preview 05:47

In this lecture you will learn algorithm features definition and main calculations (dailyReturn(), lag(), cbind(), colnames(), na.exclude(), window() functions).

Algorithm Features
05:55
Features Selection
14:29

In this lecture you will learn filter methods definition and main calculations (sbfControl(), sbf() functions).

Filter Methods
07:22

In this lecture you will learn wrapper methods definition and main calculations (rfeControl(), rfe() functions).

Wrapper Methods
06:40

In this lecture you will learn embedded methods definition and main calculations (train(), predictors() functions).

Embedded Methods
07:30

In this lecture you will learn features extraction definition and main calculations (princomp(), summary() plot() functions).

Features Extraction
09:30

In this lecture you will learn algorithm training definition and main calculations (trainControl() function).

Algorithm Training
11:00

In this lecture you will learn algorithm testing definition.

Algorithm Testing
02:57
+ Generalized Linear Models
8 lectures 43:43

You can download .PDF section slides file as additional resources.

Generalized Linear Models Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to generalized linear models (linear regression and elastic net regression).  

Generalized Linear Models Overview
05:35

In this lecture you will learn linear regression definition.  

Linear Regression
01:46

In this lecture you will learn linear regression training definition and main calculations (Sys.time(), train(), $results functions).

Linear Regression Training
05:51

In this lecture you will learn linear regression testing definition and main calculations (predict.train(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Linear Regression Testing
08:29

In this lecture you will learn elastic net regression definition.

Elastic Net Regression
03:11

In this lecture you will learn elastic net regression training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Elastic Net Regression Training
09:51

In this lecture you will learn elastic net regression testing definition and main calculations (predict.train(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Elastic Net Regression Testing
08:58
+ Similarity Methods
5 lectures 27:33

You can download .PDF section slides file as additional resources.

Similarity Methods Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to similarity methods (k nearest neighbors).

Similarity Methods Overview
05:13

In this lecture you will learn k nearest neighbors definition.

K Nearest Neighbors
04:11

In this lecture you will learn k nearest neighbors training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

K Nearest Neighbors Training
07:19

In this lecture you will learn k nearest neighbors testing definition and main calculations (predict.train(), cbind(), index(), as.data.frame(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

K Nearest Neighbors Testing
10:48
+ Frequency Methods
5 lectures 24:39

You can download .PDF section slides file as additional resources.

Frequency Methods Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to frequency methods (decision tree).

Frequency Methods Overview
05:15

In this lecture you will learn decision tree definition.

Decision Tree
02:58

In this lecture you will learn decision tree training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Decision Tree Training
07:11

In this lecture you will learn decision tree testing definition and main calculations (predict.train(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Decision Tree Testing
09:13
+ Ensemble Methods
8 lectures 48:41

You can download .PDF section slides file as additional resources.

Ensemble Methods Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to ensemble methods (random forest and gradient boosting machine).

Ensemble Methods Overview
05:48

In this lecture you will learn random forest definition.

Random Forest
01:59

In this lecture you will learn random forest training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Random Forest Training
07:10

In this lecture you will learn random forest testing definition and main calculations (predict.train(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Random Forest Testing
09:03

In this lecture you will learn extreme gradient boosting machine definition.

Extreme Gradient Boosting Machine
04:05

In this lecture you will learn extreme gradient boosting machine training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Extreme Gradient Boosting Machine Training
10:04

In this lecture you will learn extreme gradient boosting machine testing definition and main calculations (predict.train(), cbind(), index(), as.data.frame(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Extreme Gradient Boosting Machine Testing
10:30
+ Maximum Margin Methods
7 lectures 47:46

You can download .PDF section slides file as additional resources.

Maximum Margin Methods Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to maximum margin methods (linear and non-linear or radial basis function support vector machines).

Maximum Margin Methods Overview
05:41

In this lecture you will learn support vector machine definition.

Support Vector Machine
05:21

In this lecture you will learn linear support vector machine training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Linear Support Vector Machine Training
08:16

In this lecture you will learn linear support vector machine testing definition and main calculations (predict.train(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Linear Support Vector Machine Testing
09:18

In this lecture you will learn non-linear or radial basis function support vector machine training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions).

Non-Linear Support Vector Machine Training
08:02

In this lecture you will learn non-linear or radial basis function support vector machine testing definition and main calculations (predict.train(), cbind(), index(), as.data.frame(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Non-Linear Support Vector Machine Testing
11:06
+ Multi-Layer Perceptron Methods
6 lectures 42:43

You can download .PDF section slides file as additional resources.

Multi-Layer Perceptron Methods Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to multi-layer perceptron methods (artificial neural network).

Multi-Layer Perceptron Methods Overview
05:54

In this lecture you will learn artificial neural network definition.

Artificial Neural Network
05:17

In this lecture you will learn artificial neural network training definition and main calculations (Sys.time(), train(), $bestTune, plot(), $results functions)..

Artificial Neural Network Training
10:05

In this lecture you will learn linear artificial neural network testing definition and main calculations (predict.train(), cbind(), index(), as.data.frame(), xts(), as.Date(), plot(), lines(), ts(), coredata(), accuracy() functions).

Artificial Neural Network Testing
11:02

In this lecture you will learn regression machine learning comparison definition and main calculations (accuracy() function).

Regression Machine Learning Comparison
10:23
Requirements
  • R statistical software is required. Downloading instructions included.
  • RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
  • Practical example data and R script code files provided with the course.
  • Prior basic R statistical software knowledge is useful but not required.
  • Mathematical formulae kept at minimum essential level for main concepts understanding.
Description

Full Course Content Last Update 07/2018

Learn regression machine learning through a practical course with R statistical software using S&P 500® Index ETF prices historical data for algorithm learning. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.

Become a Regression Machine Learning Expert in this Practical Course with R

  • Read S&P 500® Index ETF prices data and perform regression machine learning operations by installing related packages and running code on RStudio IDE.

  • Create target and predictor algorithm features for supervised regression learning task.

  • Select relevant predictor features subset through Student t-test and ANOVA F-test univariate filter methods.

  • Choose relevant predictor features subset through recursive feature elimination deterministic wrapper method.

  • Designate relevant predictor features subset through least absolute shrinkage and selection operator embedded method.

  • Extract predictor features transformations through principal component analysis.

  • Train algorithm for mapping optimal relationship between target and predictor features.

  • Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics.

  • Calculate generalized linear models such as linear regression or elastic net regression and select optimal linear regression coefficients regularization parameter through time series cross-validation.

  • Compute similarity methods such as k nearest neighbors and select optimal number of nearest neighbors parameter through time series cross-validation.

  • Estimate frequency methods such as decision tree and select optimal maximum tree depth parameter through time series cross-validation.

  • Calculate ensemble methods such as random forest or extreme gradient boosting machine and select optimal number of randomly selected predictors or maximum trees depth parameter through time series cross-validation.

  • Compute maximum margin methods such as linear or non-linear support vector machines and select optimal error term penalization parameter through time series cross-validation.

  • Estimate multi-layer perceptron methods such as artificial neural network and select optimal node connection weight decay regularization parameter through time series cross-validation.

  • Compare regression machine learning algorithms training and testing.

Become a Regression Machine Learning Expert and Put Your Knowledge in Practice

Learning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for business forecasting research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for algorithm learning to achieve greater effectiveness.

Content and Overview

This practical course contains 56 lectures and 5.5 hours of content. It’s designed for all regression machine learning knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read S&P 500® Index ETF prices historical data to perform regression machine learning operations by installing related packages and running script code on RStudio IDE.

Then, you’ll define algorithm features by creating target and predictor variables for supervised regression learning task. Next, you’ll only include relevant predictor features subset or transformations in algorithm learning through features selection and features extraction procedures. For features selection, you’ll define univariate filter methods, deterministic wrapper methods and embedded methods. For univariate filter methods, you’ll implement Student t-test and ANOVA F-test. For deterministic wrapper methods, you’ll implement recursive feature elimination. For embedded methods, you’ll implement least absolute shrinkage and selection operator or lasso. For features extraction, you’ll implement principal component analysis. After that, you’ll define algorithm training through mapping optimal relationship between target and predictor features within training range. For algorithm training, optimal parameters selection or fine tuning, bias-variance trade-off, optimal model complexity and time series cross-validation are defined. Later, you’ll define algorithm testing through evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics within testing range. For scale-dependent metrics, you’ll define mean absolute error and root mean squared error. For scale-independent metrics, you’ll define mean absolute percentage error.

After that, you’ll define generalized linear models such as linear regression and elastic net regression. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and linear regression coefficients regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.

Then, you’ll define similarity methods such as k nearest neighbors. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and number of nearest neighbors optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.

After that, you’ll define frequency methods such as decision tree. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.

Then, you’ll define ensemble methods such as random forest and extreme gradient boosting machine. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and number of randomly selected predictors or maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.

After that, you’ll define maximum margin methods such as linear and non-linear or radial basis function support vector machines. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and error term penalization optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.

Then, you’ll define multi-layer perceptron methods such as artificial neural network. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and node connection weight decay regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range. Finally, you’ll compare regression machine learning algorithms training and testing.

Who this course is for:
  • Undergraduates or postgraduates at any knowledge level who want to learn about regression machine learning using R statistical software.
  • Academic researchers who wish to deepen their knowledge in data mining, applied statistical learning or artificial intelligence.
  • Business data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.