Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
(Definition taken from Andrew Ng's Machine Learning Course)
- Resources such as notes, exercises and samples will be uploaded here.
- Our 1.5-2 hours will be divided into:
- Introduction/Getting to know first-timers
- Discuss questions/issues raised on discussion board
- Lightning Talk
- Self-paced or mini study session
- Today I Learned (TIL)
- Announcement
- Check out these useful cheatsheets.
- If you have questions, please feel free to ask and participate in our Official ML/AI Gitter.
We based our materials and exercises, with permission, on Professor Jennifer Widom's short course on Big Data.
- Jupyter/Anaconda Installation
- Or, work in the cloud: https://colab.research.google.com
- Python Basics Tutorial
- Exercise 1: Datatypes: Numbers, Strings, Booleans
- Exercise 2: Containers: Lists and Dictionaries
- Exercise 3: Functions
- Python Advanced for Data
- Pandas Basics Tutorial
- Exercise Option 1: Learning Pandas with Pokemon
- Exercise Option 2: Learning Pandas with McDonalds
- Your First Machine Learning Project: Iris Plant Classification
- Exercise: Handwritten Digit Recognition (MNIST)
In-depth discussions of the different machine learning algorithms.
| Topic | Slide Set | Samples | Exercise | Resources |
|---|---|---|---|---|
| Decision Trees | DT Slide Set, Gini Index | DT Example | DT Exercise | DT Video |
| Naive Bayes | NB Slide Set | NB Example | NB Exercise | NB Video |