Key Objectives

  • Understand the fundamental concepts of computer vision

    Gain a solid understanding of the fundamental concepts that power computer vision systems.

  • Learn basic image processing techniques and their applications

    Discover the key image processing methods and how they are applied across various industries.

  • Explore the use of Convolutional Neural Networks (CNNs) in computer vision

    Dive into CNNs and their crucial role in enhancing computer vision technology.

This course includes

This course is backed by Cleo's guarantee.

  • 4 interactive sessions

  • 40 in-depth lessons

  • Private community of peers

  • Lifetime access to course materials

  • Course certificate upon completion

  • Direct access to instructor

Course curriculum

    1. ⭐️ Meet the expert: Hanna Mergui

      FREE PREVIEW

    2. 🚦 Course objectives

      FREE PREVIEW

    1. πŸ”‘ Introduction

    2. πŸŽ₯ - Introduction to Computer Vision

    3. 1️⃣ Part 1 - What is computer vision? πŸ‘οΈ

    4. 2️⃣ Part 2 - History and Evolution of Computer Vision πŸ“œ

    5. 3️⃣ Part 3 - The Main Categories of Computer Vision πŸ—‚οΈ

    6. 🧩 Conclusion: Chapter highlights

    7. πŸ“₯ Download Resources for the Case Study

    8. πŸ” Case Study - Computer Vision Basics: Detection, Segmentation, and Classification with OpenCV on Colab

    9. πŸ“₯ Download the Answers for the Case Study

    10. 🧐 Quiz - Introduction to Computer Vision

    1. πŸ”‘ Introduction

    2. πŸŽ₯ - Basic Concepts in Computer Vision

    3. 1️⃣ Part 1 - Digital Images: Pixels and Formats πŸ–ΌοΈ

    4. 2️⃣ Part 2 : Basic Image Processing πŸ–₯️

    5. 3️⃣ Part 3 : Introduction to Image Filters πŸ”

    6. πŸ“₯ Download Resources for the Case Study

    7. πŸ“₯ Download the Answers for the Case Study

    8. 🧐 Quiz - Basic Concepts in Computer Vision

    9. 🧩 Conclusion: Chapter highlights

    1. πŸ”‘ Introduction

    2. πŸŽ₯ - Introduction to Convolutional Neural Networks and Deep Learning

    3. 1️⃣ Part 1 - First, what is a β€œmodel”? πŸ€–

    4. 2️⃣ Part 2 - What’s the difference between AI, machine learning & deep learning ?πŸ’‘

    5. 3️⃣ Part 3 - What is a Convolutional Neural Network (CNN) 🧠

    6. πŸ“₯ Download Resources for the Case Study

    7. πŸ“₯ Download the Answers for the Case Study

    8. 🧩 Conclusion: Chapter highlights

    9. 🧐 Quiz - Introduction to Convolutional Neural Networks and Deep Learning

    1. πŸ”‘ Introduction

    2. πŸŽ₯ - Deep Dive into Convolutional Neural Networks

    3. 1️⃣ Part 1 - General architecture of CNNs πŸ—οΈ

    4. 2️⃣ Part 2 - Look inside a CNN 🧠

    5. 3️⃣ Part 3 - Ethical considerations in CNNs βš–οΈ

    6. πŸ“₯ Download Resources for the Case Study

    7. πŸš€ What’s next ?

    8. 🧩 Conclusion: Chapter highlights

    9. 🧐 Quiz - Deep Dive into Convolutional Neural Networks

    1. 🌟 Congratulations on completing the Machine Learning & Deep Learning course with Cleo Academy! 🌟

About this course

  • €350,00
  • 40 lessons

Your Instructor

Hanna Mergui

Data Scientist

Hanna Mergui is a graduate of Γ‰cole Polytechnique and currently works in the healthcare industry in computer vision. She is an expert in science communication, having co-led an art project to challenge stereotypes in artificial intelligence through visual storytelling.

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