Applied Machine Learning in R
4.5 (156 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.
13,670 students enrolled

Applied Machine Learning in R

Get the essential machine learning skills and use them in real life situations
4.5 (156 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.
13,670 students enrolled
Created by Bogdan Anastasiei
Last updated 2/2018
English
Current price: $69.99 Original price: $99.99 Discount: 30% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 8 hours on-demand video
  • 4 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Understand the essential concepts related to machine learning
  • Perform model cross-validation to assess model stability on independent data sets
  • Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
  • Perform logistic regression and discriminant analysis
  • Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
  • Use neural networks to make predictions
  • Use principal components analysis to detect patterns in variables
  • Conduct cluster analysis to group observations into homogeneous classes
Course content
Expand all 80 lectures 08:10:46
+ Getting Started
1 lecture 04:05

Let's see what you are going to learn in this course, exactly.

Preview 04:05
+ Key Issues in Machine Learning
7 lectures 34:55

A definition of machine learning and a description of the machine learning process.

Preview 03:52

Maybe the most important model categorization of machine learning models: models that have a target variable (supervised) vs. models that do not have one (unsupervised).

Preview 02:59

There are two purposes we can use machine learning models: prediction and inference. Here we'll discuss the difference between them.

Preview 04:21

Should we prefer a simple restrictive ML model or a more complex one? Here we discuss the pros and cons for each.

Preview 03:32

Here we present a couple of indicators that measure the prediction performance for the regression models.

Computing Prediction Accuracy of Regression Models
05:06

The best indicators that help us estimate the predictive performance of the classification models.

Computing Prediction Accuracy of Classification Models
07:56

This is a crucial issue in machine learning. Some models may have low bias, but high variance, other models may present high bias, but lower variance. Which is best? How to select an "optimal" model?

Bias-Variance Tradeoff
07:09
+ Cross-Validation
4 lectures 11:24

Machine learning models should work well on independent data sets. This is why it is absolutely necessary to validate them before actually using them.

What Is Cross-Validation?
01:20

The advantages and disadvantages of the validation set approach.

Validation Set Approach
03:05

What is leave-one-out cross validation and what are its strengths and weaknesses.

Leave-One-Out Cross-Validation Approach
03:35

What is k-fold cross-validation, how and when we should use it.

K-Fold Cross-Validation Approach
03:24
+ Ordinary Least Squares Regression
3 lectures 20:03

How to run an ordinary least squares regression in R, in order to predict the values of a numeric (quantitative) variable.

Introduction to the OLS Regression
07:42

How to validate an OLS regression model using the validation set approach.

Validating the OLS Regression Model (1)
06:26

How to validate an OLS regression model using the k-fold cross-validation method.

Validating the OLS Regression Model (2)
05:55
+ Best Subset Regression
4 lectures 40:48

When we have a lot of predictors in our regression model, we are often interested to identify the most relevant of them. Here we explain how we can do that using the best subset regression.

Best Subset Selection Regression - Introduction
16:41

How to perform a forward selection regression in order to find the predictors that most influence the response variable.

Forward Selection Regression
06:42

How to perform a backward forward selection regression, with the same goal: find the predictors that have the strongest effect on the response variable.

Backward Selection Regression
04:28

How to validate the subset selection regression on an independent data set.

Validating the Subset Selection Regression
12:57
+ Penalized Regression
4 lectures 28:31

How to run a ridge regression in R and interpret the output.

Ridge Regression
09:58

How to validate the ridge regression using the k-fold cross-validation approach.

Validating the Ridge Regression
06:07

How to perform a lasso regression in R and interpret the results.

Lasso Regression
07:19

How to validate the lasso regression model with the k-fold cross-validation method.

Validating the Lasso Regression
05:07
+ Partial Least Squares Regression
2 lectures 13:10

How to run a PLS regression in R and interpret the output.

Introduction to PLS Regression
10:04

How to compute the prediction accuracy of a PLS regression model in the validation set.

Validating the PLS Regression
03:06
+ Logistic Regression
6 lectures 48:15

How to run the logistic regression in R and interpret the coefficients.

Introduction to Logistic Regression
14:37

How to compute the percentage of correctly classified cases for a logistic regression (in the whole sample).

Computing the Prediction Accuracy
05:43

How to draw and interpret the ROC curve - one of the most important output of a logistic regression.

Building the ROC Curve
06:00

How to test a logistic regression model in an independent data set.

Validating the Logistic Regression
06:05

If our logistic regression model has many predictors, we can use the lasso logistic regression to select the most effective of them. In this lecture we learn how to do that.

Lasso Logistic Regression
06:36

How to validate a lasso logistic regression model - and select the model with the highest prediction accuracy.

Validating the Lasso Logistic Regression
09:14
+ Discriminant Analysis
4 lectures 17:37

How to run a linear discriminant analysis and interpret the output.

Linear Discriminant Analysis
09:21

How to test (validate) our linear discriminant analysis.

Validating the Linear Discriminant
02:32

How to execute a quadratic discriminant analysis in R.

Quadratic Discriminant Analysis
03:23

How to validate our quadratic discriminant model (check if it does a good prediction job in independent data sets).

Validating the Quadratic Discriminant
02:21
+ Naive Bayes Estimation
4 lectures 30:18

Here we explain in detail how the naive Bayes procedure works, and give a simple illustration in a hypothetical situation.

Introduction to Naive Bayes Estimation
11:32

How to apply the naive Bayes classification in R using the e1071 package.

Naive Bayes Estimation in R with the e1071 Package
11:08

How to validate a naive Bayes estimation model.

Validating the Naive Bayes Model
02:53

How to perform a naive Bayes classification in R using the naivebayes package (an how to test our model).

Naive Bayes Estimation in R with the naivebayes Package
04:45
Requirements
  • Knowledge of the R program
  • Basic knowledge of statistics and statistical analysis
Description

This course offers you practical training in machine learning, using the R program. At the end of the course you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from your data.

All the machine learning procedures are explained live, in detail, on real life data sets. So you will advance fast and be able to apply your knowledge immediately – no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time you can have a solid expertise in machine learning.

Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences today!

Let’s see what you are going to learn here.

First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we’ll talk about supervised and unsupervised machine learning techniques, about the distinctions between prediction and inference, about the regression and classification models and, above all, about the bias-variance trade-off, a crucial issue in machine learning.

Next we’ll learn about cross-validation. This is an all-important topic, because in machine learning we must be able to test and validate our model on independent data sets (also called first seen data). So we are going to present the advantages and disadvantages of three cross-validations approaches.

After the first two introductory sections, we will get to study the supervised machine learning techniques. We’ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backward), penalized regression (ridge and lasso) and partial least squares regression. And of course, we’ll demonstrate all of them in R, using actual data sets.

Afterwards we’ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we’ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), naïve Bayes technique, K nearest neighbor, support vector machine, decision trees and neural networks.

For each technique above, the presentation is structured as follows:

* a short, easy to understand theoretical introduction (without complex mathematics)

* how to train the predictive model in R

* how to test the model to make sure that it does a good prediction job on independent data sets.

In the last sections we’ll study two unsupervised machine learning techniques: principal component analysis and cluster analysis. They are powerful data mining techniques that allow you to detect patterns in your data or variables.

For each technique, a number of practical exercises are proposed. By doing these exercises you’ll actually apply in practice what you have learned.

This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it.

So click the “Enroll” button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future.

See you inside!

Who this course is for:
  • Data analysts
  • Data scientists
  • Researchers
  • Students