p-workshops-2021

P-ai's AI/ML workshops, revamped for the 2021-2022 school year

Dates and times TBD

Workshop session 1

• Introductions & setup
  • Git / GitHub
  • Jupyter Notebook / Google Colab
• Demystifying Machine Learning
  • GI vs. AI vs. ML vs. DL
  • Linear regression as a machine learning algorithm
  • What it means to train a machine learning model
• Coding environments
  • Python libraries
  • Package management
  • Virtual environments
• Python crash course

Workshop session 2

• Domains of machine learning
• Supervised learning in more detail
• Linear regression and Logistic regression
• Case study with house prices

Workshop session 3

• Gradient descent
  • What is gradient descent?
  • How do machines use GD to learn?
• Machine learning model crash course
  • K-Nearest Neighbors
  • Naive Bayes
  • Decision Trees
  • Random Forest
  • Support Vector Machines
• Case study applying the skills we learned
• How to use git & GitHub

Workshop session 4

• Deep learning
  • Intuition behind neural nets
  • How to build and train a neural net with Tensorflow and Keras
  • Types of neural nets
• Case study applying deep learning