Machine Learning (ML) is a form of Artificial Intelligence (AI) that enables computers to automatically learn and adapt using large volumes of quality data sets. The main goal of ML is to learn from the previous data patterns and make future predictions. Today, ML powers technologies such as facial recognition, fraud prevention, consumer shopping patterns, equipment failure, and even self-driving cars. However, the field of ML is vast and complex and this 3-hour tutorial with three basic modules is your entry into the world of ML. This training is designed for analysts, managers, and executives across any industry sector who want to get a broad overview of key ML concepts and techniques . 

The first module provides an overview of the basic concepts and building blocks of ML.  This module covers the 5 main types of Analytics and the 3 types of ML. Given that ML heavily involves techniques from both statistics and computer science, the next two modules cover these two areas. Module 2 provides an overview of statistical concepts such as data distributions, sampling, correlation, statistical tests (such as T-Tests and ANOVA) and more. The third module is on the computer science aspects and covers ML algorithms such as Decision Trees, Support Vector Machines (SVM), Logistic Regression, Linear Regression, and Clustering. In all the three modules the emphasis is on the ML concepts and their relation to the real and enterprise/business world in plain simple English. This is not a ML programming tutorial.