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PT24914
Summary
Applied Supervised Learning with R perfectly balances theory and exercises. Each module is designed to build on the learnings of the previous module. The course contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best-supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.
Prerequisites
Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this course.
Duration
3 Days/Lecture & Lab
Audience
This course is specially designed for novice and intermediate data analysts, data scientists, and data engineers who want to explore various methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this course.
Topics
- R for Advanced Analytics
- Exploratory Analysis of Data
- Introduction to Supervised Learning
- Regression
- Classification
- Feature Selection and Dimensionality Reduction
- Model Improvements
- Model Deployment
- Capstone Project - Based on Research Papers