Predictive Analytics 2 – Neural Nets and Regression with R

Expand your AI and machine learning knowledge by learning about basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling

Enroll in Predictive Analytics 2 – Neural Nets and Regression with R

About This Online Course

Advance your knowledge of artificial intelligence, machine learning and data science techniques by learning about predictive modeling, the most prevalent form of data mining. Enroll in this online training course from statistics.com that is a follow up to Predictive Analytics 1 – Machine Learning Tools Using R (also from statistics.com).

In this online course, you will continue work from Predictive Analytics 1 and be introduced to additional techniques in predictive analytics (also called predictive modeling), the most prevalent form of data mining. The course includes hands-on work with R.

Upon completing this course, you will be able to distinguish between profiling and prediction tasks for linear and logistic regression. You also will be able to specify and interpret linear and logistics regression models, use various analytical tools for prediction and classification and preprocess text for text mining.

This online course includes hands-on work with the programming language, R.

This online course is perfect if you wish to understand what predictive modeling can offer to your organization, undertake pilots with minimum setup costs, manage predictive modeling projects or work with consultants or technical experts involved in ongoing predictive modeling deployments.

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 5th edition (Wiley 2017), Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., and Lichtendal, K. (available on the Wiley website). Learners must purchase the book before starting the course.

What You Will Learn

  • Distinguish between profiling (explanation) tasks and prediction tasks for linear and logistic regression
  • Specify and interpret linear regression models to predict continuous outcomes
  • Specify and interpret logistic regression models for classification
  • Use discriminant analysis for classification
  • Use neural nets for prediction and classification
  • Preprocess text for text mining and use a predictive model with the resulting matrix

Your Instructor

Dr. Peter Gedeck is at the forefront of the use of data science in drug discovery. He is a senior data scientist with Collaborative Drug Discovery, which offers the pharmaceutical industry cloud-based software to manage the huge amount of data involved in the drug discovery process Dr. Gedeck’s specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Prior to his current position, he was a computational chemist in drug discovery at Novartis in the United Kingdom, Switzerland, and Singapore for two decades.

Dr. Gedeck’s research interests include the application of statistical and machine learning methods to problems in drug discovery. clinical research and meta-analysis. He is a co-author of Data Mining for Business Analytics – Using Python, which serves as the foundation for statistics.com’s predictive analytics series of courses. Dr. Gedeck received his Doctorate and Master of Science in Chemistry from the University of Erlangen-Nürnberg, Germany and his Bachelor of Science in Mathematics from Fernuniversität Hagen, Germany.

Who Should Take This Course

This course is designed for marketing and information technology managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.

Prerequisites

Predictive Analytics 1 – Machine Learning Tools Using R, from statistics.com, is the prerequisite for this course.

After finishing this course, you can take the statistics.com companion course, Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R, to continue your professional education in AI, machine learning and data science. The course covers the key unsupervised learning techniques of association rules—principal components analysis, and clustering—and includes an integration of supervised and unsupervised learning techniques using R.

Course Certificate

A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.

In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.

Course Format

This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. Participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Course Pricing

$599 (per person)

Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.

Continuing Education Unit Credits

This online course provides 5.0 CEUs upon 50-percent completion.

This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.