Given at the PyData NYC 2013 conference (http://vimeo.com/79517341), and will be given at PyTennessee 2014. Scikit-learn is one of the most well-known machine learning Python modules in existence. But how does it work, and what, for that matter, is machine learning? For those with programming experience but who are new to machine learning, this talk gives a beginner-level overview of how machine learning can be useful, important machine learning concepts, and how to implement them with scikit-learn. We’ll use real world data to look at supervised and unsupervised machine learning algorithms and why scikit-learn is useful for performing these tasks.

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