€77.87

**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!**

- 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

76 Lessons

9 Courses

1 students