Machine learning involves using algorithms to find patterns in data to make predictions or decisions without being explicitly programmed. The document discusses that machine learning uses artificial intelligence, statistical analysis, and programming to implement algorithms using open source software projects. It then provides examples of machine learning applications like cancer prediction, tuning trading algorithms, content tailoring, and dynamically generating content for platforms.

1. About Machine Learning
🤖📓
Mario Gintili

2. What?
How?
In the wild

3. What?
40%
40%
20%
Artificial Intelligence Programming Statistical Analysis

4. How? Implementations
- The computing “muscle” required to do ML became
available for commercial use
- Algorithms - aka the though bit - became publicly
accessible through OSS projects
- The applications are endless - ML has a place in
most businesses nowadays

5. How? Applications
This is just a over simplified subset of what’s actually happening.
Done exclusively for the purposes of this presentation🌚 🌚
Age Gender Height(m) Body fat %
20 Male 1.79 19
34 Female 1.67 29
Features
Predictions

6. In the wild
- Cancer prediction & prognosis[1]
- Tuning trading algorithms
- Content tailoring
[1] http://www.sciencedirect.com/science/article/pii/S2001037014000464

7. In the wild - Content based platforms
Can be dynamically fabricated
by classifying content

8. Thank you
Mario Gintili
https://github.com/mariogintili/machine-learnist
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