Machine Learning Overview

Course Objectives

How to approach ML problems

Types of common ML problems and gotchas

Understand differences between single node and distributed ML

Identify new ML use cases

What is ML?

Learning patterns in your data without being explicitly programmed

Function that maps features to an output

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Supervised Machine Learning

Classification

Regression

Unsupervised Machine Learning

Learn structure of the unlabeled data

Reinforcement Learning

Learning what to do to maximize reward

Explore and exploit

Business Use Cases?

Defining Success

What if I told you I had a model that was 99% accurate in predicting brain cancer?

Which Metric is Important?

Baseline Model

You ALWAYS want to have a baseline model to compare to

This should be a "dummy" model, i.e. a coin flip

All models are wrong; some models are useful.

Model Selection?

Underlying data distribution

Deployment considerations

Some models are more costly to train

Need for interpretability?

Right to Explanation


General Data Protection Regulation

But first, Data Cleansing...

The Dirty Work of a Data Scientist

Data Cleansing Demo

Why visualize your data?

Anscombe's Quartet

Dataset #0 Dataset #1 Dataset #2 Dataset #3
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
Regression
Dataset #1 Dataset #2 Dataset #3 Dataset #4
Mean 9 7.5 9 7.5 9 7.5 9 7.5
Variance 11 4.1 11 4.1 11 4.1 11 4.1
Correlation 0.86 0.86 0.86 0.86
Regression
line
y = 3 + 0.5x y = 3 + 0.5x y = 3 + 0.5x y = 3 + 0.5x

Why Visualize Your Data?

Data Exploration Lab