Course curriculum

    1. Welcome to the course!

    2. About ZAKA

    3. A message from your instructor

    4. Best practices & tips for success

    5. How to get support?

    1. Introduction to AI

    2. AI Components

    3. DS Lifecycle

    4. Basic Steps of EDA

    5. Data Visualization

    6. Data Preparation

    7. Data Preparation

    8. Feature Engineering

    9. Recap

    1. Outline

    2. Classes of ML

    3. Supervised Learning

    4. Unsupervised Learning

    5. Hands-on Part 1: Build Your First Classifier

    6. Hands-on Part 2: Integrate Your Classifier

    7. Evaluating ML Models

    8. Neural Networks & Deep Learning

    9. Applications of DL

    10. Feed Forward Neural Networks

    11. Hands-on: Neural Network Simulation

    12. Recap

    1. Outline

    2. CV: Definition & Applications

    3. What is an image to a computer?

    4. CV: How does it work?

    5. What is 'Convolution'?

    6. Neural Networks for Images

    7. Hands-on: Cats vs Dogs

    8. What is NLP?

    9. NLP Challenges

    10. NLP Applications

    11. NLP: How does it work?

    12. Text Data Preparation

    13. Feature Extraction

    14. Hands-on: Embedding Projector

    15. DL in NLP

    16. Hands-on: Text Generation

    17. What are Time-series?

    18. Types of TS problems

    19. TS Applications

    20. Hands-on: Number of Passengers

    21. Recap

    1. Outline

    2. Impact of AI

    3. Data Scarcity & DeepFakes

    4. AI Black Box

    5. Adversarial Attacks

    6. Ethical Challenges

    7. Hands-on: Moral Machines

    8. Recap

    1. A goodbye message from your instructor

    2. Feedback survey

    3. Final words

About this course

  • Free
  • 58 lessons
  • 5 hours of video content