Artificial Neural Networks for Business Managers in RStudio
Learn where and how to build ANN models in R to create predictive models that can guide crucial business decisions with this free online course.
This free online course provides a solid foundation for a career in artificial neural networks (ANN) and deep learning. We explain how ANN can be applied to create predictive models capable of informing financial decisions. We take you step by step through the process of building ANN models in ?R? with RStudio until you can create your own machine learning algorithms. This course can help you to predict financial shifts in time to plan for them.
What You Will Learn In This Free Course
ANN Overview
This module introduces you to Artificial Neural Networks (ANN), starting with the concept of perception, through activation functions, to complex networks. You will also gain a basic understanding of how neural networks are formed, their architecture, and hyperparameters.
Setting up RStudio and R Crash Course
In this module, you will earn the basics of R and RStudio, starting from the installation processes. You will get an overview of the RStudio environment, the R commands you can use to input data, and the packages and data available in R. You will also learn to create graphs, change colours, and save the graphs.
Neural Network Models
This module demonstrates how to build neural network models using Keras and Neural nets. You will also learn about building regression models and using the Functional API. Finally, you will see how you can save your model in a separate file and restore or share it, when required.
Data Preprocessing
In this module, you will learn how to prepare the data for analysis. It starts with the basic theory of decision tree, where you will learn concepts such as Data Dictionary and Univariate Analysis. It then covers data pre-processing topics like outlier treatment, missing value imputation, variable transformation, and Test-Train split.
Linear Regression
This module starts with simple linear regression and then covers multiple linear regression in R. You will begin by understanding the basic concepts and then learn to quantify a model’s accuracy and the meaning of F statistic. You will also learn to interpret the variables in the dataset, and the result of the regression problem.
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