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Artificial Neural Networks for Business Managers in RStudio

Make important business decisions with artificial neural network models built in Rstudio with this free online course.

Publisher: Start-Tech Academy
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.
Artificial Neural Networks for Business Managers in RStudio
  • Duration

    6-10 Hours
  • Students

  • Accreditation






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Are you keen on data science and machine learning but cannot master the concepts because of the advanced mathematical and computing knowledge required? This course offers you everything you need to know about artificial neural network (ANN) and statistical modeling without demanding prior knowledge of coding or advanced mathematics to follow the video lectures. We demonstrate the creation of predictive models to solve crucial business problems as you become proficient with ‘R’, a popular language used for data manipulation and analysis in machine learning.

We begin with a basic introduction to neural networks, covering important concepts such as the perceptron, activation functions, the sigmoid neuron and neural network architecture and logic. Once you are familiar with the basic theory of ANN, we take you through the basic operations of R and the process of installing Rstudio. We then move on to building neural network models in R. This entails the processes of data analysis, normalization and splitting, followed by model training and evaluation, error estimation and tuning - before finally using the models to make predictions. Each step is discussed in detail before it is demonstrated in the R environment. Both classification and regression models are covered in the course, along with in-depth knowledge of data preprocessing and linear regression. In data preprocessing, you start with the basic theories of decision trees and move on to data treatment practices such as outlier treatment, missing value imputation and variable transformation, all of which are crucial to preparing the data for analysis and modelling. We also show you how to interpret the results of the data analysis performed by R while you quantify a model’s accuracy. This course also explains the business scenarios wherein ANN models are applicable.

ANN is one of the most popular machine-learning algorithms used by data scientists today. It is a powerful tool used in deep learning and therefore in business decision-making. Using advanced computing power, it can be applied to large volumes of data to identify complicated patterns. R and its unique packages make it easy to employ data manipulation and analysis techniques in ANN modeling, which is why R is used by almost all big companies today to identify customer behavioral patterns, estimate the effectiveness of ad campaigns and make economic forecasts. This course can make you an expert in both neural network modeling and R language, thus giving you a competitive edge over your peers in machine learning and statistical predictions. Whether you wish to pursue a career in data science or want to apply machine-learning algorithms to solving your business problems, this course is tailor-made for you. It can make you a sought-after professional in the booming field of data science and machine learning so arm yourself with the power of predictive problem-solving.

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