At a Glance

Name: Machine Learning with R
Price: Free CAD
Rating: 0.0 (0 reviews)

This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

About This Course

This course presents a holistic approach to Big Data, taking both a top-down and a bottom-up approach to questions such as: What is Big Data? How do we tackle Big Data? Why are we interested in it? What is a Big Data platform?

The course emphasizes that we study Big Data to gain insight that will be used to get people throughout the enterprise to run the business better and to provide better service to customers. Rather than a implementation of a single open-source systems such as Hadoop, the course recommends that Big Data should be processed in a platform that can handle the variety, velocity, and volume of data by using a family of components that require integration and data governance. Big Data is NoHadoop (“not only Hadoop”) as well as NoSQL (“not only SQL”).

What will I get after passing this course?

You will receive a completion certificate.
You will receive the IBM Explorer - Big Data Foundations badge.

Course Syllabus

Lesson 1 - Big Data - Beyond the Hype

Big Data Skills and Sources of Big Data
Big Data Adoption

Lesson 2 - What is Big Data?

Characteristics of Big Data - The Four V's
Understanding Big Data with Examples

Lesson 3 - The Big Data Platform

Key aspects of a Big Data Platform
Governance for Big Data

Lesson 4 - Five High Value Big Data Use Cases

Overview of High Value Big Data Use Cases
Examples of High Value Big Data Use Cases

Lesson 5 - Technical Details of Big Data Components

Text Analytics and Streams
Streams, Cloud and Big Data

General Information

This course is free.
It is self-paced.
It can be taken at any time.
It can be taken as many times as you wish.

Recommended skills prior to taking this course

None

Grading scheme

The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.
You have 1 attempt to take the exam with multiple attempts per question.

Requirements

None.

Course Staff

Glen R.J. Mules

Glen R.J. Mules is a Senior Instructor and Principal Consultant with IBM Information Management World-Wide Education and works from New Rochelle, NY. Glen joined IBM in 2001 as a result of IBM's acquisition of Informix Software. He has worked at IBM, and previously at Informix Software, as an instructor, a course developer, and in the enablement of instructors worldwide. He teaches courses in BigData (BigInsights and Streams), Optim, Guardium, and DB2, and Informix databases. He has a BSc in Mathematics from the University of Adelaide, South Australia; an MSc in Computer Science from the University of Birmingham, England; and has just completed a Ph.D. in Education (Educational Technology) at Walden University. His early work life was as a high school teacher in Australia. In the 1970s he designed, programmed, and managed banking systems in Manhattan and Boston. In the 1980's he was a VP in Electronic Payments for Bank of America in San Francisco and New York. In the early 1990's he was an EVP in Marketing for a software development company and chaired the ANSI X12C Standards Committee on Data Security for Electronic Data Interchange (EDI).

Offered By: IBM Cognitive Class

Machine Learning with R