[NEW] 2024:Build 15+ Real-Time Computer Vision Projects

[NEW] 2024:Build 15+ Real-Time Computer Vision Projects, available at $54.99, has an average rating of 3.71, with 38 lectures, based on 7 reviews, and has 81 subscribers.

You will learn about DEEP LEARNING PROJECTS COMPUTER VISION YOLOV8 YOLO DEEPFAKE OBJECT RECOGNITION OBJECT TRACKING INSTANCE SEGMENTATION IMAGE CLASSIFICATION IMAGE ANNOTATION HUMAN ACTION RECOGNITION FACE RECOGNITION FACE ANALYSIS IMAGE CAPTIONING POSE DETECTION/ACTION RECOGNITION KEYPOINT DETECTION SEMANTIC SEGMENTATION Image Processing Pixel manipulation edge detection feature extraction Machine Learning Pattern Recognition Object detection classification segmentation Python TensorFlow PyTorch R-CNN ImageNet COCO This course is ideal for individuals who are Beginner ML practitioners eager to learn Deep Learning or Anyone who wants to learn about deep learning based computer vision algorithms or Python Developers with basic ML knowledge It is particularly useful for Beginner ML practitioners eager to learn Deep Learning or Anyone who wants to learn about deep learning based computer vision algorithms or Python Developers with basic ML knowledge.

Enroll now: [NEW] 2024:Build 15+ Real-Time Computer Vision Projects

Summary

Title: [NEW] 2024:Build 15+ Real-Time Computer Vision Projects

Price: $54.99

Average Rating: 3.71

Number of Lectures: 38

Number of Published Lectures: 38

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

DEEP LEARNING
PROJECTS
COMPUTER VISION
YOLOV8
YOLO
DEEPFAKE
OBJECT RECOGNITION
OBJECT TRACKING
INSTANCE SEGMENTATION
IMAGE CLASSIFICATION
IMAGE ANNOTATION
HUMAN ACTION RECOGNITION
FACE RECOGNITION
FACE ANALYSIS
IMAGE CAPTIONING
POSE DETECTION/ACTION RECOGNITION
KEYPOINT DETECTION
SEMANTIC SEGMENTATION
Image Processing
Pixel manipulation
edge detection
feature extraction
Machine Learning
Pattern Recognition
Object detection
classification
segmentation
Python
TensorFlow
PyTorch
R-CNN
ImageNet
COCO

Who Should Attend

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

Target Audiences

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

Build 15+ Real-Time Deep Learning(Computer Vision) Projects

Ready to transform raw data into actionable insights?

This project-driven Computer Vision Bootcamp equips you with the practical skills to tackle real-world challenges.

Forget theory, get coding!

Through 12 core projects and 5 mini-projects,you’ll gain mastery by actively building applications in high-demand areas:

Object Detection & Tracking:

Project 6: Master object detectionwith the powerful YOLOv5 model.

Project 7: Leverage the cutting-edge YOLOv8-cls for image and video classification.

Project 8: Delve into instance segmentation using YOLOv8-seg to separate individual objects.

Mini Project 1: Explore YOLOv8-pose for keypoint detection.

Mini Project 2 & 3: Make real-time predictions on videos and track objects using YOLO.

Project 9: Build a system for object tracking and counting.

Mini Project 4: Utilize the YOLO-WORLD Detect Anything Model for broader object identification.

Image Analysis & Beyond:

Project 1 & 2: Get started with image classification on classic datasets like MNIST and Fashion MNIST.

Project 3: Master Keras preprocessing layers for image manipulation tasks like translations.

Project 4: Unlock the power of transfer learning for tackling complex image classification problems.

Project 5: Explore the fascinating world of image captioning using Generative Adversarial Networks (GANs).

Project 10: Train models to recognize human actions in videos.

Project 11: Uncover the secrets of faces with face detection, recognition, and analysis of age, gender, and mood.

Project 12: Explore the world of deepfakes and understand their applications.

Mini Project 5: Analyze images with the pre-trained MoonDream1 model.

Why Choose This Course?

Learn by Doing: Each project provides practical coding experience, solidifying your understanding.

Cutting-edge Tools: Master the latest advancements in Computer Vision with frameworks like YOLOv5 and YOLOv8.

Diverse Applications: Gain exposure to various real-world use cases, from object detection to deepfakes.

Structured Learning: Progress through projects with clear instructions and guidance.

Ready to take your Computer Vision skills to the next level? Enroll now and start building your portfolio!

Core Concepts:

Image Processing: Pixel manipulation, filtering, edge detection, feature extraction.

Machine Learning: Supervised learning, unsupervised learning, deep learning (specifically convolutional neural networks – CNNs).

Pattern Recognition: Object detection, classification, segmentation.

Computer Vision Applications: Robotics, autonomous vehicles, medical imaging, facial recognition, security systems.

Specific Terminology:

Object Recognition: Identifying and classifying objects within an image.

Semantic Segmentation: Labeling each pixel in an image according to its corresponding object class.

Instance Segmentation: Identifying and distinguishing individual objects of the same class.

Technical Skills:

Programming Languages: Python (with libraries like OpenCV, TensorFlow, PyTorch).

Hardware: High-performance computing systems (GPUs) for deep learning tasks.

Additionally:

Acronyms: YOLO, R-CNN (common algorithms used in computer vision).

Datasets: ImageNet, COCO (standard datasets for training and evaluating computer vision models).

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Project 1. Image Classification MNIST Dataset

Lecture 1: Problem : Image Classification MNIST Dataset

Lecture 2: Solution : Image Classification MNIST Dataset

Chapter 3: Project 2. Image Classification on Fashion MNIST Dataset

Lecture 1: Problem :Image Classification on Fashion MNIST Dataset

Lecture 2: Solution :Image Classification on Fashion MNIST Dataset

Chapter 4: Project 3. Using Keras Preprocessing Layers for image translations.

Lecture 1: Problem : Using Keras Preprocessing Layers for image translations.

Lecture 2: Solution : Using Keras Preprocessing Layers for image translations.

Chapter 5: Project 4. Transfer Learning for Image classification on complex dataset

Lecture 1: Problem :Transfer Learning for Image classification on complex dataset

Lecture 2: Solution :Transfer Learning for Image classification on complex dataset

Chapter 6: Project 5. Image Captioning using GANs

Lecture 1: Problem : Image Captioning using GANs

Lecture 2: Solution : Image Captioning using GANs Part1

Lecture 3: Solution : Image Captioning using GANs Part2

Lecture 4: Solution : Image Captioning using GANs Part3

Chapter 7: Annotation Tools

Lecture 1: Annotation Tools

Chapter 8: Project 6. Object Detection using YOLOv5 Model

Lecture 1: Problem : Object Detection using YOLOv5 Model

Lecture 2: Solution : Object Detection using YOLOv5 Model

Chapter 9: Project 7. Image / video classification using YOLOV8-cls

Lecture 1: Problem : Image / video classification using YOLOV8-cls

Lecture 2: Solution : Image / video classification using YOLOV8-cls

Chapter 10: Project 8. Instance Segmentation using YOLOV8-seg

Lecture 1: Problem : Instance Segmentation using YOLOV8-seg

Lecture 2: Solution : Instance Segmentation using YOLOV8-seg

Chapter 11: Mini Project 1 :Yolov8-Pose Keypoint Detection

Lecture 1: Problem :Yolov8-Pose Keypoint Detection

Lecture 2: Solution :Yolov8-Pose Keypoint Detection

Chapter 12: Mini Project 2: Predictions on Videos using YOLOV8

Lecture 1: Problem :Predictions on Videos using YOLOV8

Lecture 2: Solution :Predictions on Videos using YOLOV8

Chapter 13: Mini Project 3: Object Tracking using YOLO

Lecture 1: Problem :Object Tracking using YOLO

Lecture 2: Solution :Object Tracking using YOLO

Chapter 14: Project 9. Object Tracking and Counting

Lecture 1: Problem :Object Tracking and Counting

Lecture 2: Solution :Object Tracking and Counting

Chapter 15: Mini Project 4: YOLO-WORLD Detect Anything Model

Lecture 1: Problem : YOLO-WORLD Detect Anything Model

Lecture 2: Solution : YOLO-WORLD Detect Anything Model

Chapter 16: Mini Project 5 MoonDream1 Image Analysis

Lecture 1: Problem : MoonDream1 Image Analysis

Lecture 2: Solution : MoonDream1 Image Analysis

Chapter 17: Project 10. Human Action Recognition

Lecture 1: Problem : Human Action Recognition

Lecture 2: Solution : Human Action Recognition

Chapter 18: Project 11. Face Detection & Recognition (AGE GENDER MOOD Analysis)

Lecture 1: Problem : Face Detection & Recognition

Lecture 2: Solution : Face Detection & Recognition

Chapter 19: Project 12. Deepfake Generation

Lecture 1: Problem : Deepfake Generation

Lecture 2: Solution : Deepfake Generation

Instructors

MG Analytics
Data Scientist and Professional Trainer

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 4 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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