Machine learning education

Vik Paruchuri

DataQuest (www.dataquest.io)

vik.paruchuri@gmail.com

How I got started with ML

• BA in American History
• Learned using online resources

Quick survey

The problem

• Learning about machine learning is a nebulous process that isn't aligned with application

The result

• Many turned away
• Important skills not emphasized

Questions to answer in this talk

• What do I tell someone who wants to learn?
• How do I improve my skills?

Getting more people in

• Huge strides in past few years
• > 100k students in Andrew Ng's first MOOC

MOOCs

• Great material
• Good for deepening learning
• Hard to filter
• Not always application focused

Kaggle

• Try lots of techniques
• Good discussion forum
• Competition
• Appeals to a certain personality

How does someone learn machine learning?

• Standard answer: stats, linear algebra, read books, take moocs

Many different roles

• Researchers
• Framework developers
• Appliers
• Interpreters
• Consumers

Interpreters/consumers important

• Two "AI Winters"
  • Hype
  • Few consumer products
• Elon Musk (and others) -- "AI is an existential threat"

edX automated essay scoring

• Hype
• Negative reaction

Learning applied ML

ML is about solving problems

Entry level job/project-readiness

• Basic algo skills
• "The rest"

Application

• Data cleaning 90% of work
• Tradeoffs when applying algorithms

Communication

• Explaining results
• Working with other teams

Systems

• Writing faster-running code
• Scaling to multiple machines

Programming

• Writing well-structured code
• Testing

Why aren't we teaching this stuff?

• Harder + less interesting to teach
• Industry problem

Bias

• Advice-givers too far from beginning
• Not everyone wants to get to the same place

Quickly evolving field

• Roles ill-defined
• ML a "destination"

Perpetuates a myth

• Either a beginner or at the top
• Have to know everything about everything

Result

• Many turned off of ML
• Kaggle + courses are a personality filter
  • Reduces diversity
• ML on resume != Can do ML

How to motivate learning?

• Everyone has different on and off ramps
• Better tools
  • Educational
  • Applied

ML has intrinsic motivation

• First steps are semi-magical

You can even motivate high schoolers!

• 2 high schools on dataquest
• Good results
• Next step: teach ML

Groups increase motivation

• Blended learning effective
• Correlates with high course completion

How should you learn or improve?

Step 1: Learn to code

• If you don't know a language yet, try Python
• Go to programming meetups
• Work on projects
• Go to hackathons

Step 2: Learn to work with data

• Find datasets.
  • UCI machine learning repository
  • Census.gov
  • Reddit /r/datasets
• Answer questions with the data
• Think about what/how you might predict
• Go to data science meetups

Step 3: Start with applied ML

• Get familiar with implementing linear regression
  • Scikit-learn docs
• Go to machine learning meetups
• Work on a project
• Progressively add pieces (error measurement, cross validation, etc)

Step 4: More application, and some theory

• Write blog posts, speak at meetups, and put projects on github
• Consult
• Learn statistics
• Learn linear algebra
• Learn more algorithms (knn/k-means/logistic regression)

Step 5: Get better at coding

• Unit test your algorithms
• Learn some basic computer science (sorting/search/etc)
• Refactor old code
• Get better at bash scripting

Step 6: Broaden the scope

• Keep working on projects
• Learn a couple more algorithms
• Work on larger datasets
• Learn about hardware, storage, and networking

Quick weekend hack

• Unite -- cooperative machine learning
• https://github.com/dataquestio/unite

How long will this take?

• Ongoing process
• 6 months to a couple of years upfront
• Things change quickly

Contributing

• We can all make better tools
• No better time to learn or teach ML

Done!