34 Introduction To Machine Learning

These tools are at the center of the Data Science revolution. Many researchers, companies, and governmental organizations would like to use the cheap and abundant data they are collecting to predict what customers will like, what services to offer, or how to improve people’s lives.

The emphasis of this course is hands on learning and implementation. If you like what you learn in this class, there are a large number of other Machine and Statistical Learning MOOCs that you can use to deepen your knowledge of the technical and mathematical details.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

List of Packages used: psych, ISLR, kernlab, caret, RANN, Hmisc, splines, rattle, ElemStatLearn, tidyverse