Predictive Analytics With Neural Networks in R

Neural networks are powerful predictive tools that can be used for almost any machine learning problem with very good results. If you want to break into deep learning and artificial intelligence, learning neural networks is the first crucial step.

This is why I’m inviting you to get into the fascinating world of neural networks. In this course you will develop a strong understanding of one the most utilised network, multilayer perceptron, suitable for both classification and regression problems.

The mathematics behind neural networks is particularly complex, but you don’t need to be a mathematician to take this course and fully benefit from it. Our emphasis here is on practice. You will learn how to operate multilayer perceptrons using the R program, how to build and train models and how to make predictions on new data.

All the procedures are explained live, on real life data sets. So you will advance fast and be able to apply your knowledge immediately.

This course contains three sections.

The first section is dedicated to the basic concepts related to neural networks and predictive analytics. You will find out what multilayer perceptrons are how they learn, what procedure they employ to make predictions. Also, you’ll learn the main prediction accuracy metrics for both numeric and categorical response variables.

In the second section we’ll build and train a multilayer perceptron to predict a bank customers’ default. In other words, our response is categorical in this case. After training the network, we’ll use it to measure prediction accuracy in the test set. But that’s not all. We will also try to improve our model by manipulating various parameters of the network and test our model accuracy using the k-fold cross-validation technique.

In the third section we build and train a model with a numeric response variable. More exactly, we’ll predict car prices depending on their technical features, using a multilayer perceptron, of course. After building the model we’ll measure its accuracy on the test set, try to improve it by modifying he network parameters and, finally, validate our model using the k-fold cross-validation method. So, the same steps as in the previous section, but this time for the particular case of a numeric dependent variable.

A number of practical exercises are proposed at the end of the course. By doing these exercises you’ll actually apply in practice what you have learned.

This course is your opportunity to become familiar with neural networks very fast. With my video lectures, you will find it easy to master these major neural networks and build them in R. 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 course. It will surely provide you with new priceless skills. And, who knows, it could enhance your future career.

See you inside!