Power Electronics

Artificial Intelligence using R

Course Overview

  1. Why This Course?

    This is an instructor led course provides lecture topics and the practical application of Machine Learning and the underlying technologies. It pictorially presents most concepts and there is a detailed case study that strings together the technologies, patterns and design. Measuring and Tuning performance of ML algorithms You'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems Most effective machine learning techniques You will learn how to Prototype and then productionize Best practices in innovation as it pertains to machine learning and AI Prerequisite: Experience in Programming An understanding of Intro to Statistics would be helpful. A familiarity with Probability Theory, Calculus, Linear Algebra and Statistics is required
  2. What you will learn?

    1. Fundamentals of R Programming Capabilities of R Datatypes in R Data Structures in R Data Inputting in R Data Manipulation in R Functions and Programming in R Data Visualization in R 2. Introduction to Machine Learning 3. Introduction to Basic Statistics 4. Exploratory Data Analysis using R 5. Modelling Concepts and Notations 6. Regression I. Simple Regression II. Multilinear Regression III. Polynomial Regression 7. Classification kNN LDA QDA 8. Logistic Regression Support Vector Machines Model Assessment and Selection 9. Trees & Ensemble Models Bagging Boosting Random Forest 10. Clustering Techniques k-Means Hierarchical Clustering PCA 11. Capstone Project
  3. Venue and Schedule

    Schedule : To be notified