What you'll learn
Complete end-to-end resume worthy project
Learn about Aerial Imagery and Related Data
Data Analysis and Preprocessing of Aerial Image data
Image Machine Learning Algorithms such as CNN
Requirements
Python Programming Basic Knowledge is Required
Description
Welcome to "Project - Rooftop Solar Panel Detection using Deep Learning"!In today's era of renewable energy, solar panels are sprouting on rooftops worldwide. Recognizing them efficiently can empower industries, city planners, and researchers alike. In this hands-on course, we dive deep into the world of artificial intelligence to develop a cutting-edge model capable of detecting solar panels from aerial images.What you'll learn:Fundamentals of Deep Learning: Kickstart your journey with a foundational understanding of neural networks, their architectures, and the magic behind their capabilities.Data Preparation: Learn how to source, cleanse, and prepare aerial imagery datasets suitable for training deep learning models.Model Building: Delve into the practicalities of building, training, and fine-tuning Convolutional Neural Networks (CNNs) for precise detection tasks.Evaluation and Optimization: Master techniques to evaluate your model's performance and optimize it for better accuracy.Real-World Application: By the end of this course, you will have a deployable model to identify rooftop solar installations from a bird's-eye view.Whether you're a student, a professional, or an enthusiast in the renewable energy or AI sector, this course is designed to equip you with the skills to contribute to a greener and more technologically advanced future. No previous deep learning experience required, though a basic understanding of Python programming will be helpful.Harness the synergy of AI and renewable energy and propel your skills to the forefront of innovation. Enroll now and embark on a journey of impactful learning!
Overview
Section 1: Introduction to Project and Data Processing
Lecture 1 Workflow of the Project
Lecture 2 Project Content
Lecture 3 Introduction to Project Statement
Lecture 4 Gist of the Dataset
Lecture 5 Importing the Libraries and the Dataset
Lecture 6 Function to prepare data for training and validation
Lecture 7 Analysing and Preprocessing the data
Section 2: Introduction to Machine Learning
Lecture 8 Quick Explanation on CNN
Lecture 9 Function to build Convolutional Neural Network (CNN)
Lecture 10 Stratified K-Fold Cross Validation to check the model performance
Lecture 11 Building, Training and Assessing the CNN Model
Section 3: Evaluation Metrics and Conclusion
Lecture 12 Evaluation Metrics for Classification (TP, FP, TN, FN)
Lecture 13 Visualising these Evaluation Metrics (TP, FP, TN, FN)
Lecture 14 Understanding and Implementing ROC curve and AUC
Lecture 15 Confusion Matrix to evaluate the model's performance
Lecture 16 Conclusion of the Project
Whoever interested in Satellite and Aerial image and data science

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