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Applied Machine Learning in R

Name: Applied Machine Learning in R
Rating: 4.6 (294 reviews)

Get the essential machine learning skills and use them in real life situations

Created byBogdan Anastasiei

Last updated 12/2020

English

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

18 sections • 80 lectures • 8h 10m total length

Introduction4:05
Let's see what you are going to learn in this course, exactly.

What Is Machine Learning?3:52
A definition of machine learning and a description of the machine learning process.
Supervised vs. Unsupervised Methods2:59
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).
Prediction vs. Inference4:21
There are two purposes we can use machine learning models: prediction and inference. Here we'll discuss the difference between them.
Restrictive Models vs. Flexible Models3:32
Should we prefer a simple restrictive ML model or a more complex one? Here we discuss the pros and cons for each.
Computing Prediction Accuracy of Regression Models5:06
Here we present a couple of indicators that measure the prediction performance for the regression models.
Computing Prediction Accuracy of Classification Models7:56
The best indicators that help us estimate the predictive performance of the classification models.
Bias-Variance Tradeoff7:09
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?

What Is Cross-Validation?1:20
Machine learning models should work well on independent data sets. This is why it is absolutely necessary to validate them before actually using them.
Validation Set Approach3:05
The advantages and disadvantages of the validation set approach.
Leave-One-Out Cross-Validation Approach3:35
What is leave-one-out cross validation and what are its strengths and weaknesses.
K-Fold Cross-Validation Approach3:24
What is k-fold cross-validation, how and when we should use it.

Introduction to the OLS Regression7:42
How to run an ordinary least squares regression in R, in order to predict the values of a numeric (quantitative) variable.
Validating the OLS Regression Model (1)6:26
How to validate an OLS regression model using the validation set approach.
Validating the OLS Regression Model (2)5:55
How to validate an OLS regression model using the k-fold cross-validation method.

Best Subset Selection Regression - Introduction16:41
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.
Forward Selection Regression6:42
How to perform a forward selection regression in order to find the predictors that most influence the response variable.
Backward Selection Regression4:28
How to perform a backward forward selection regression, with the same goal: find the predictors that have the strongest effect on the response variable.
Validating the Subset Selection Regression12:57
How to validate the subset selection regression on an independent data set.

Introduction to Logistic Regression14:37
How to run the logistic regression in R and interpret the coefficients.
Computing the Prediction Accuracy5:43
How to compute the percentage of correctly classified cases for a logistic regression (in the whole sample).
Building the ROC Curve6:00
How to draw and interpret the ROC curve - one of the most important output of a logistic regression.
Validating the Logistic Regression6:05
How to test a logistic regression model in an independent data set.
Lasso Logistic Regression6:36
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.
Validating the Lasso Logistic Regression9:14
How to validate a lasso logistic regression model - and select the model with the highest prediction accuracy.

Linear Discriminant Analysis9:21
How to run a linear discriminant analysis and interpret the output.
Validating the Linear Discriminant2:32
How to test (validate) our linear discriminant analysis.
Quadratic Discriminant Analysis3:23
How to execute a quadratic discriminant analysis in R.
Validating the Quadratic Discriminant2:21
How to validate our quadratic discriminant model (check if it does a good prediction job in independent data sets).

Introduction to Naive Bayes Estimation11:32
Here we explain in detail how the naive Bayes procedure works, and give a simple illustration in a hypothetical situation.
Naive Bayes Estimation in R with the e1071 Package11:08
How to apply the naive Bayes classification in R using the e1071 package.
Validating the Naive Bayes Model2:53
How to validate a naive Bayes estimation model.
Naive Bayes Estimation in R with the naivebayes Package4:45
How to perform a naive Bayes classification in R using the naivebayes package (an how to test our model).

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

Applied Machine Learning in R

What you'll learn

Explore related topics

Course content

Getting Started1 lecture • 4min

Key Issues in Machine Learning7 lectures • 35min

Cross-Validation4 lectures • 11min

Ordinary Least Squares Regression3 lectures • 20min

Best Subset Regression4 lectures • 41min

Penalized Regression4 lectures • 29min

Partial Least Squares Regression2 lectures • 13min

Logistic Regression6 lectures • 48min

Discriminant Analysis4 lectures • 18min

Naive Bayes Estimation4 lectures • 30min

Requirements

Description

Who this course is for: