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 (anyone who wants to discuss a machine learning project)
- Self-paced or mini study session
- Today I Learned (TIL)
- Announcement
- Note: Every other session, we will discuss one machine learning/ AI algorithm.
- Check out these useful cheatsheets!
If you have questions, please feel free to ask and participate in any of the following:
- Guide on how to open an issue in a repository.
- Create a Github account and create a repository for wwcode-ml.ai. You will upload your work there.
- Please upload or push the file to your GIT repository.
- Open an issue in this repository named [Your name] - [topic]. For example, John Doe - First ML Project.
- Share the link of your work on the issue.
- New to Git? No worries, check ot these guides.
- Anaconda Installation
- Run a basic Python program in Anaconda Prompt
- Getting started with Jupyter Notebook (Optional)
- Our First Machine Learning Project: Iris Plant Classification
- Exercise: Handwritten Digit Recognition (MNIST)
- Assignment: Digit Recognizer Kaggle Submission
- Study Group Resources
- Study Group Slides
- Exercise: Iris Plant Classification using KNN
- Assignment: KNN from Scratch
- Study Group Resources
- Study Group Slides
- Handling Missing Values Tutorial
- Exercise: Pima Indians Diabetes Prediction
- Study Group Resources
- Understand your Data with Descriptive Statistics Tutorial
- Visualize Machine Learning Data in Python With Pandas
- Exercise: Pima Indians Diabetes Prediction
- Study Group Slides
- Exercise: Wine Data Classification
- Assignment: Binarize Features in Handwritten Digit Recognition
- Study Group Resources
- Study Group Slides
- Visualizing Categorical Data Tutorial
- Handling Categorical Data Tutorial
- Exercise: Adult Census Income Dataset
- Study Group Resources