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Develop a wildlife or farm monitoring system that identifies animal species from video feeds using CNN-based classification. Helpful for biodiversity and security.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI helps monitor and identify wild animals in natural habitats or restricted zones.
Use Python with OpenCV and models like YOLO or TensorFlow for accurate animal detection.
Create a real-time system that detects animals through surveillance or drone video feeds.
Log detected animal species, time, and location into a database for tracking and research.
Develop an intuitive GUI to display animal detections and tracking information live.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Introduces the objective and structure of an animal detection system using machine learning and deep learning, highlighting real-world use cases like wildlife monitoring or farm automation.
Course Overview and Features
Detect animals with AI
Use ML/DL techniques
Spot animals in images
Understand system goals
Learn project structure.
Helpful for wildlife tracking
Setting up Python and its development tools is the foundation of building AI applications. This ensures the environment is ready to run and develop object detection models.
Installing Python
VS Code Setup for Python Development
Provides an outline of the entire project, explaining the workflow and how each component contributes to building a working animal detection solution.
Animal Detection System Project Overview
View full project flow
Plan detection pipeline
Build end-to-end system
Demonstrates how to configure Google Colab and mount Google Drive for easy access to datasets and code during model development and training.
Setting Up Google Colab and Mounting Google Drive
Open Colab online
Smooth model training
Shows how to download animal datasets and explore their structure and classes to gain a better understanding before preprocessing and training.
Dataset Download and Exploration
Check folder structure
Ready for preprocessing
Explains techniques to clean, resize, and augment dataset images to improve model performance and reduce overfitting during training.
Dataset Preprocessing and Augmentation
Apply image flipping
Prevent overfitting
Covers how to split the dataset into appropriate subsets to ensure accurate model evaluation and generalization.
Splitting the Dataset for Training, Validation, and Testing
Divide data into sets
Ensure fair evaluation
Improve model generalization
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recruiters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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Get full project code for 20+ real-world applications – build, customize, and learn hands-on with working solutions.
Join weekly live Q&As to resolve queries and deepen your understanding with real-time support
Enhance your confidence with communication tips, resume builder templates, and personal branding guides tailored for tech careers.
Get feedback, share wins, and grow with other learners in a safe and supportive environment.
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