1.
A Beginner’s Guide to Machine
Learning with Scikit-Learn
Sarah Guido
PyTennessee 2014

2.
All about me
• Grad student at the University of Michigan
• Data analyst for HathiTrust
• Organizer of Ann Arbor PyLadies chapter

3.
My talk
• Machine learning and scikit-learn
• Supervised and unsupervised learning
• Preprocessing, validation and testing, strategies
for machine learning

4.
What is machine learning?
• Application of algorithms that learn from
examples
• Representation and generalization

5.
Why should we care?
• Useful in every day life
• Email spam, handwriting analysis, stock market
analysis, Netflix
• Especially useful in data analysis
• Feature extraction, linear regression, classification,
clustering

6.
Machine Learning Vocab
• Instance
• Feature
• Class
• Categorical
• Nominal
• Ordinal
• Continuous

7.
Machine Learning Vocab
Feature
Class
Instance

8.
Scikit-Learn
• Machine learning module
• Open-source
• Built-in datasets
• Good resources for learning

9.
Scikit-Learn
• Model = EstimatorObject()
• Model.fit(dataset.data, dataset.target)
• dataset.data = dataset
• dataset.target = labels
• Model.predict(dataset.data)

10.
Scikit-Learn
• Supervised
• Unsupervised
• Semi-supervised
• Reinforcement learning
• Neural networks
• …and many more!

11.
Supervised learning
• Labeled data
• You know what you’re looking for
• Classification: predict categorical labels
• Regression: predict continuous target variables

12.
Classification
• Categorical variables
• Relationship between instance and feature
• Classification algorithms == classifiers

13.
Classification
• Naïve Bayes classifier
• Features are independent
• Fast performance
• Decent classifier

14.
Classification
• Car evaluation dataset-UCI
• Features: buying price, the maintenance price,
the number of doors, the number of seats, the
size of the trunk, and the safety ranking
• Labels: unacceptable, acceptable, good, or very
good

15.
Classification

16.
Classification

17.
Classification

18.
Unsupervised algorithms
• Unlabeled data
• You might have no idea what you’re looking for
• Clustering: splitting observations into groups
• Dimensionality reduction: flatten data to fewer
dimensions

19.
Clustering
• Exploring the data
• Similar objects in the same group
• Distance between data points

20.
Clustering
• K-means clustering
• Three steps
• Chooses initial cluster centers
• Assigns data instance to cluster
• Recalculates cluster center
• Efficient

21.
Clustering

22.
Clustering

23.
Clustering

24.
Data preprocessing
• Encoding categorical features

25.
Data preprocessing

26.
Data preprocessing

27.
Data preprocessing
• Split the dataset into training and test data

28.
Validation and testing
• Model evaluation
• Cross-validation

29.
Good strategies
• Avoid overfitting
• Use lots of data
• Intuition fails in high dimensions

30.
My materials
• Scikit-learn.org documentation and tutorials
• Machine learning class at U of M
• Scikit-learn talks

31.
Resources
• Scikit-learn documentation and tutorials
• scikit-learn.org/stable/documentation.html
• Other resources
• http://archive.ics.uci.edu/ml/datasets.html
• Mldata.org
• Videos
• Scikit-learn tutorial: http://vimeo.com/53062607
• Intro to scikit-learn: http://vimeo.com/72859487

32.
Contact me!
• @sarah_guido
• Linkedin.com/sarahguido
• github.com/sarguido