This document provides an overview of machine learning, including its applications at companies like Yelp, Airbnb, Spotify, and Netflix. It discusses the five tribes of machine learning approaches - symbolists, connectionists, evolutionaries, Bayesians, and analogizers. It also covers getting started with machine learning using Python libraries like Scikit-learn within a Docker environment and exploring algorithms for classification and regression problems. Resources are provided for learning more about machine learning approaches, Scikit-learn algorithms, using Docker Notebook, and taking online courses.

1. PART 1: OVERVIEW & INTRO
MACHINE LEARNING

2. WHY MACHINE LEARNING
BACKGROUND, PYTHON AND ML
▸ It’s about building things. Ability to “know” user.
Back-end web development and Data Science
▸ Mathematics and APIs
▸ Scikit-learn, environment and dependencies

3. APPLICATIONS OF MACHINE LEARNING
RECENT EXAMPLES
▸ Yelp:
http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-
to-classify-business-photos-at-yelp.html
▸ Airbnb:
http://nerds.airbnb.com/aerosolve/
▸ Spotify:
http://benanne.github.io/2014/08/05/spotify-cnns.html
▸ Netflix:
http://techblog.netflix.com/2015/07/tracking-down-villains-outlier.html
▸ Spam, Medical, Finance

4. WHAT IS MACHINE LEARNING?
5 TRIBES OF MACHINE LEARNING
Tribe Origins Master Algorithm
Symbolists Logic, philosophy Inverse Deduction
Connectionists Neuroscience Back propagation
Evolutionaries Evolutionary Biology Genetic Programming
Bayesian Statistics Probabilistic Inference
Analogizes Psychology Kernel Machines
Source: Ex-1

5. GETTING STARTED
ENVIRONMENT AND SCIKIT-LEARN
▸ Build and run the Docker Notebook for dependencies:
https://github.com/ipython/docker-notebook/tree/master/
scipyserver
▸ Clone Scipy 2013 Scikit-learn Tutorial into /notebooks
https://github.com/jakevdp/sklearn_scipy2013
▸ Working with Scipy Stack:
Ipython notebook, Matplotlib, Numpy
▸ Scikitlearn API: Basic Classification and Regression

6. WHAT IS MACHINE LEARNING?
CHOOSING AN ALGORITHM
Source: Ex-2

7. MOVING FORWARD
BUILD AND LEARN
▸ Kaggle: https://www.kaggle.com/
▸ Yelp Dataset Challenge
http://www.yelp.com/dataset_challenge
▸ Machine Learning Coursera with Prof Domingos
https://www.coursera.org/course/machlearning

8. RESOURCES
RESOURCES
▸ 1: Professor Domingos Five Tribes of Machine Learning
http://www.slideshare.net/SessionsEvents/pedro-domingos-professor-
university-of-washington-at-mlconf-atl-91815
▸ 2: Scikit-learn Algorithm Cheat Sheet
http://scikit-learn.org/stable/themes/scikit-learn/static/
ML_MAPS_README.html
▸ 3: Docker Notebook:
https://github.com/ipython/docker-notebook/tree/master/scipyserver
▸ 4: Scipy 2013 Scikit-learn Tutorial
https://github.com/jakevdp/sklearn_scipy2013