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Mastering Computer Vision: Deep Learning & Generative AI with 12 Projects

Course Description:

Step into the future of artificial intelligence with a complete, hands-on guide to computer vision, deep learning, and generative AI.

Mastering Computer Vision: Deep Learning & Generative AI with 12 Projects takes you from the foundations of image processing to cutting-edge technologies like YOLOv8, Stable Diffusion, DeepFakes, and more—through 12 real-world projects designed to build your portfolio and practical expertise.

This course is designed for aspiring machine learning engineers, data scientists, AI enthusiasts, and developers who want to break into the world of visual intelligence and build systems that see, understand, and create.

What You’ll Learn

Computer Vision Fundamentals

  • Image processing basics, color spaces, and real-world CV applications
  • Introduction to neural networks and deep learning workflows
  • Model building with TensorFlow, Keras, activation functions, and optimizers

Deep Learning in Action

  • Hands-on CNN projects for image classification
  • Data preprocessing, augmentation, and transfer learning
  • RNN, LSTM, and GRU overview for temporal vision tasks

Generative AI for Vision

  • GANs from the ground up + Image Captioning project
  • DeepFake generation techniques
  • Stable Diffusion models, CLIP, UNet, and prompting-based image generation

Advanced Computer Vision Applications

  • Object detection with YOLOv5, YOLOv8 (classification, segmentation, pose)
  • Semantic vs instance segmentation, Mask R-CNN, Fast/Faster R-CNN
  • Object tracking, counting, and human action recognition
  • Face detection, emotion/age/gender analysis with DeepFace
  • Hands-on annotation tools and project-ready workflows

Explore the New Frontier

  • Experiment with YOLO-World and Moondream1 models
  • Dive into modern Generative AI image pipelines
  • Learn best practices for model optimization and performance improvement

What’s Inside

  • 17 in-depth modules with 80+ lessons
  • 12 guided projects including detection, classification, captioning, deepfake, and more
  • Practical code, real datasets, and end-to-end implementation
  • Lifetime access with self-paced flexibility

By the end of this course, you’ll be equipped to build real-world computer vision systems, implement state-of-the-art GenAI tools, and stand out with a project-rich portfolio in one of tech’s fastest-growing fields.

Lessons

    1. 1. Introduction

    2. 2. Past Present Future Trends

    3. 3. Applications

    4. 4. Image Processing basics

    5. 5. Color Spaces

    1. 1. Intuition Neural Networks

    2. 2. Neural Networks

    3. 3. Approach to deep learning problems

    4. 4. Lifecycle of model 5 steps

    1. 1. Sequential Vs Functional API

    2. 2. Sequential API code

    3. 3. Functional API Code

    4. 4. ML problem Cost Gradient CV

    5. 5. Activation Functions

    6. 6. Sequential Vs Functional API

    7. 7. Tips for Improving Model Performance

    8. 8. Feed Forward Network Implementation and Keras Callbacks

    9. 9. Optimizers

    10. 10. Loss functions

    11. 11. Performance Metrics

    1. 1. CNN INTRO

    2. 2. CNN_Implementation

    3. 3. CNN Exercise -1 Problem

    4. 4. CNN Exercise -1 Solution

    5. 5. CNN Exercise -2 Problem

    6. 6. CNN Exercise -2 Solution

    1. 1. Keras Preprocessing Layers Intro

    2. 2. Keras Preprocessing Layers Image Augmentation Code

    3. 3. Keras Preprocessing Layers Exercise-3

    4. 4. Keras Preprocessing Layers Solution-3

    5. 5. Transfer Learning Introduction

    6. 6. transfer learning code

    7. 7. Transfer Learning Exercise 4 -XrayDataset

    8. 8. Transfer learning Exercise-4 Solution

    1. 1. LSTM GRU Introduction

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