Predictive Analytics 1 – Machine Learning Tools Using R
Expand your AI and machine learning knowledge by learning about the core model of predictive analytics: classification and prediction using the programming language, R.Enroll in Predictive Analytics 1 – Machine Learning Tools Using R
About This Online Course
Continue to expand your knowledge about artificial intelligence, machine learning and data science tools by learning about the basic concepts in predictive analytics—the most prevalent form of data mining—using R.
The R programming language was created as an alternative to an expensive statistical programming package. It was built as an open-source option for that purpose, and is very good in its domain of statistical programming, which is useful in AI, machine learning and data science.
In this online training course from statistics.com, you will be introduced to the basic concepts in predictive analytics—the most prevalent form of data mining. The course covers the two core paradigms that account for most government and business applications of predictive modeling: classification and prediction.
At the conclusion of this online course, you will be able to visualize and explore data, provide an assessment basis for predictive models, and choose appropriate performance measures. You will understand commonplace algorithms, including k-nearest-neighbor, Naive Bayes and classification and regression trees, as well as ensemble models.
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
- Visualize and explore data to better understand relationships among variables
- Organize the predictive modeling task and data flow
- Develop machine learning models with KNN, Naive Bayes and CART algorithms using R
- Assess the performance of these machine learning models with holdout data
- Apply predictive models to generate predictions for new data
- Use various R packages to implement the machine learning models in the course
Kuber Deokar is instructional operations supervisor at Statistics.com where he is responsible for coordination of online courses and seamless interactions between the management team, course instructors, teaching assistants and students. Mr. Deokar also serves as senior teaching assistant and shares instructional responsibilities for several courses. He handles consultancy assignments from the statistics.com office in Pune, India.
Mr. Deokar holds Master and Bachelor of Science degrees in Statistics from University of Pune, India, where he also taught undergraduate statistics.
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.
You should be sufficiently familiar with Python to follow and are well-versed in statistics (or have the equivalent understanding of topics covered in the statistics.com courses: Statistics 1 – Probability and Study Design course and Statistics 2 – Inference and Association.
After finishing this course, you can take a companion course from statistics.com to continue to advance your knowledge: Predictive Analytics 2 – Neural Nets and Regression with R which introduces the basic concepts in predictive analytics to visualize and explore predictive modeling.
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.
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.
$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.