Machine-Learning-Applications

An overview of various projects involving real life applications of Machine Learning!

View this repository in VS Code by clicking here

To Launch all Python Notebook files (.ipynb):

• Click here

• Choose a project and open a file

• You can now look through the code!

• To interact with and run the code, click on the 'Execute on Binder' button on the top right of the screen. (This will take a considerable amount of time to load)

• Once loaded, you can download the file to save your changes

• You can also download the .ipynb files in this repository and upload them in Google Colab here

• Alternatively, if you want to run .ipynb files on your local machine, you can download jupyter notebook by running pip install notebook and then running jupyter notebook. Read more here

To Run the Python files (.py):

• Download the code git clone https://github.com/SJUSATA/Machine-Learning-Applications.git

• Navigate to the directory cd Machine-Learning-Applications

• Follow the instructions in your specific project's folder

Additional Resources

• Complete Overview of ML Topics

• MIT Free Intro to Machine Learning Course

• MIT AI & ML Lecture

• A Tour of Machine Learning Algorithms

• Ted Talk: The Wonderful and Terrifying Implications of Computers that Can Learn

• Ted Talk: The Jobs We'll Lose to Machines -- and the Ones We Won't

• Ted Talk: Dont Fear Intelligent Machines. Work with Them

• Machine Learning for Video Games

• How to teach an AI to Play Games

• Machine Learning for Cyber Security

• Art by AI

• DeepMind:

  • AlphaGo: The World Champion in Go
  • AlphaGo Documentary
  • AlphaGo Zero: Starting from Scratch
  • MuZero: Mastering Go, Chess, Shogi and Atari without Rules
  • AlphaFold: Using AI for Scientific Discovery

• OpenAI:

  • GPT-3 Language Generator Model | GPT-3 Video Demo
  • DALL·E: Creating Images from Text | DALL·E Video Demo

• Datasets for ML Applications can be found on Kaggle and Google Dataset Search