Complete Computer Vision With GenAI-12 Projects

Complete Computer Vision With GenAI-12 Projects


Welcome to the world of Deep Learning! This course is designed to equip you with the knowledge and skills needed to excel in this exciting field. Whether you’re a Machine Learning practitioner seeking to advance your skillset or a complete beginner eager to explore the potential of Deep Learning, this course caters to your needs.

What You’ll Learn:

Master the fundamentals of Deep Learning, including Tensorflow and Keras libraries.

Build a strong understanding of core Deep Learning algorithms like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

Gain practical experience through hands-on projects covering tasks like image classification, object detection, and image captioning.

Explore advanced topics like transfer learning, data augmentation, and cutting-edge models like YOLOv8 and Stable Diffusion.

The course curriculum is meticulously structured to provide a comprehensive learning experience:

Section 1: Computer Vision Introduction & Basics: Provides a foundation in computer vision concepts, image processing basics, and color spaces.

Section 2: Neural Networks – Into the World of Deep Learning: Introduces the concept of Neural Networks, their working principles, and their application to Deep Learning problems.

Section 3: Tensorflow and Keras: Delves into the popular Deep Learning frameworks, Tensorflow and Keras, explaining their functionalities and API usage.

Section 4: Image Classification Explained & Project: Explains Convolutional Neural Networks (CNNs), the workhorse for image classification tasks, with a hands-on project to solidify your understanding.

Section 5: Keras Preprocessing Layers and Transfer Learning: Demonstrates how to leverage Keras preprocessing layers for data augmentation and explores the power of transfer learning for faster model development.

Section 6: RNN LSTM & GRU Introduction: Provides an introduction to Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) for handling sequential data.

Section 7: GANS & Image Captioning Project: Introduces Generative Adversarial Networks (GANs) and their applications, followed by a project on image captioning showcasing their capabilities.

Section 9: Object Detection Everything You Should Know: Delves into object detection, covering various approaches like two-step detection, RCNN architectures (Fast RCNN, Faster RCNN, Mask RCNN), YOLO, and SSD.

Section 10: Image Annotation Tools: Introduces tools used for image annotation, crucial for creating labeled datasets for object detection tasks.

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Section 11: YOLO Models for Object Detection, Classification, Segmentation, Pose Detection: Provides in-depth exploration of YOLO models, including YOLOv5, YOLOv8, and their capabilities in object detection, classification, segmentation, and pose detection. This section includes a project on object detection using YOLOv5.

Section 12: Segmentation using FAST-SAM: Introduces FAST-SAM (Segment Anything Model) for semantic segmentation tasks.

Section 13: Object Tracking & Counting Project: Provides an opportunity to work on a project involving object tracking and counting using YOLOv8.

Section 14: Human Action Recognition Project: Guides you through a project on human action recognition using Deep Learning models.

Section 15: Image Analysis Models: Briefly explores pre-trained models for image analysis tasks like YOLO-WORLD and Moondream1.

Section 16: Face Detection & Recognition (AGE GENDER MOOD Analysis): Introduces techniques for face detection and recognition, including DeepFace library for analyzing age, gender, and mood from images.

Section 17: Deepfake Generation: Provides an overview of deepfakes and how they are generated.

Section 18: BONUS TOPIC: GENERATIVE AI – Image Generation Via Prompting – Diffusion Models: Introduces the exciting world of Generative AI with a focus on Stable Diffusion models, including CLIP, U-Net, and related tools and resources.

What Sets This Course Apart:

Up-to-date Curriculum: This course incorporates the latest advancements in Deep Learning, including YOLOv8, Stable Diffusion, and Fast-SAM.

Hands-on Projects: Apply your learning through practical projects, fostering a deeper understanding of real-world applications.

Clear Explanations: Complex concepts are broken down into easy-to-understand modules with detailed explanations and examples.

Structured Learning Path: The well-organized curriculum ensures easy learning experience

Who this course is for:

  • Beginner ML practitioners eager to learn Deep Learning
  • Python Developers with basic ML knowledge
  • Anyone who wants to learn about deep learning based computer vision algorithms


  • Python

Last Updated 3/2024

Total Size: 13.0 GB

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