Course curriculum

  • 1

    Course Introduction

    • Welcome to the course!

    • About ZAKA

    • A message from your instructor

    • Best practices & tips for success

    • How to get support?
  • 2

    Module 1: Data Science Foundations

    • Introduction to AI

    • AI Components

    • DS Lifecycle

    • Basic Steps of EDA

    • Data Visualization

    • Data Preparation

    • Data Preparation

    • Feature Engineering

    • Recap
  • 3

    Module 2: Machine Learning

    • Outline

    • Classes of ML

    • Supervised Learning

    • Unsupervised Learning

    • Hands-on Part 1: Build Your First Classifier

    • Hands-on Part 2: Integrate Your Classifier

    • Evaluating ML Models

    • Neural Networks & Deep Learning

    • Applications of DL

    • Feed Forward Neural Networks

    • Hands-on: Neural Network Simulation

    • Recap
  • 4

    Module 3: Domains of AI

    • Outline

    • CV: Definition & Applications

    • What is an image to a computer?

    • CV: How does it work?

    • What is 'Convolution'?

    • Neural Networks for Images

    • Hands-on: Cats vs Dogs

    • What is NLP?

    • NLP Challenges

    • NLP Applications

    • NLP: How does it work?

    • Text Data Preparation

    • Feature Extraction

    • Hands-on: Embedding Projector

    • DL in NLP

    • Hands-on: Text Generation

    • What are Time-series?

    • Types of TS problems

    • TS Applications

    • Hands-on: Number of Passengers

    • Recap

  • 5

    Module 4: Challenges of AI

    • Outline

    • Impact of AI

    • Data Scarcity & DeepFakes

    • AI Black Box

    • Adversarial Attacks

    • Ethical Challenges

    • Hands-on: Moral Machines

    • Recap
  • 6

    Course Wrap-up

    • A goodbye message from your instructor

    • Feedback survey

    • Final words