This document discusses machine learning and data science. It explains that machine learning can be used to analyze large datasets with many variables, unlike traditional statistics which is better for smaller datasets with fewer variables. The document then provides examples of different types of data and how machine learning can be applied to images, text, time series data and more. It also defines the roles of data scientists and data engineers. Finally, it provides recommendations for learning machine learning including starting with basic analytics, understanding key concepts and using tools like scikit-learn, Spark and Hadoop.

1. Machine Learning for
Hackers
is how we make sense of big data.
Adam Gibson
2-27-2014 SFHN

2. BIG DATA & STATISTICS
• Statistics – Group by, aggregate, count,average, mean,p
values,mode,correlations, exploring, < 100 variables
• Machine Learning – Label this image, Predict the next
event, Pick out the anomalies – aka learn from data not
count it, group data by similarities, > 100 variables.

3. What is data?!

4. Many kinds of data Wow.
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Unstructured
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Text
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SQL
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Video
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XML

Images
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JSON

Time Series

CSV
Data Scientists
Structured
We know this, and just process it.

5. WHAT do machines learn?
• Machine learning is a general tool that can work with
•
•
•
•
•
•
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various data types.
Images = Machine vision
Text = Natural-language processing
Time-series = Prediction
Facial recognition => Security
Text => Customer profiles/Recommendation engines
Time-series => stock-market trading platforms
NLP => Customer service

6. WHAT IS A DATA SCIENTIST?
Analyst
Exploratory analysis of data,
typically on smaller data sets.
Understands the algorithms
and interprets data.
Distributed Systems Engineer
Implements production data crunching,
also known as the nosql person.
They handle distributed systems
and workloads,
APIs, perhaps
even data collection and storage

7. What kinds of Machine Learning Are there?
Unsupervised – Clustering (group things that are similar,
regression (correlation != causation ring a bell?)
Supervised – Label all the things!
Predict the future!

8. How does this affect me?

9. I will leave who does this to your imagination

Ad Targeting
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Recommends you Movies

Brings you search results

Recognizes your face in the camera

Drives your car

Automatically disables your credit card when you leave the country

10. Can I do this?
The shortcut here is to start with basics –
for example google analytics, understanding churn rate.
Pick up a more advanced understanding
after that if it still seems interesting.
If you are in to backends start with distributed systems,
get your math basics up enough to understand
what the guy on the other side of the table
who's asking you to put the algorithm in to production is saying

11. Resources
Coursera Machine Learning
Reddit Machine Learning
DataTau (hacker news for data scientists)
More mathy Stanford Machine Learning

12. Tools

13. Analysts
Data Engineers
Spark
http://scikit-learn.org/stable/
Julia Lang
R Lang
Hadoop
Storm
Hadoop QuickStart VM