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5. Module 0 - What is Machine Learning? - Part One

# Introduction to Data

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## Contents

###### Practical Machine Learning
1
Introduction to AI
PREVIEW3m 41s
2
The history of AI
PREVIEW3m 54s

## The course is part of these learning paths

Practical Machine Learning
11
6
3
AWS Machine Learning – Specialty Certification Preparation
39
14
15
Introduction to Machine Learning on AWS
6
1
2
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Overview
Difficulty
Beginner
Duration
1h 23m
Students
962
Ratings
3.9/5
Description

Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. This course is part one of the module on machine learning. It starts with the basics, introducing you to AI and its history. We’ll discuss the ethics of it, and talk about examples of currently existing AI. We’ll cover data, statistics and variables, before moving onto notation and supervised learning.

Part two of this two-part series can be found here, and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.

Transcript

Let's talk about data. What does this word mean? Well, it's used in all kinds of contexts today, but I think in a technical or practitioner context we mean specifically information which can be used by computers. Now in the context of machine learning, we are interested in understanding data as composed of columns, or what might be called variables. So, data here is always tabular in our model, in our thinking about the problem. So we have some variables that say X1, X2, X3, and we have a Y, and we are thinking in terms of a tabular layout, and what we mean by a variable then is such a column, or variable, and here we use the letter X to denote a feature or what you might call an observation, an example, a trait or characteristic, something else. So here Y we call the target and that is the thing that we are trying to predict. So prediction target. Now there are different kinds of connection that Y may have to X. In general, we call such connections functions or relationships and the machine using statistics is able to determine from a historical data set, and then by using such a formula able to, in the future, estimate or predict something for Y given what it can see for X.