Welcome to "Harnessing AI and Machine Learning for Geospatial Analysis"! This live course is designed for professionals, students, and enthusiasts eager to apply AI and ML to solve geospatial data challenges. With live sessions, hands-on labs, and practical projects, you’ll build skills that can be applied to real-world scenarios in agriculture, environmental monitoring, and more.

• 👨‍🏫Instructor: Dr. Azad Rasul
• 💻Platform: Google Meet
• 📅Duration: November 20, 2024 - January 20, 2025
• 🕒Session Time: Weekly, Wednesday, 3:00 pm to 5:00 pm (+3 GMT)
• 💵Fee: $200
• 👥Group Size: Maximum of 10 students per session for optimal interaction.
• 🎓Certificate: A certificate of completion will be provided to all students who finish the course.
• 🔗GitHub Repository: Link to GitHub Repository: https://github.com/Azad77/AI4Geospatial
• 📧Contact with Instructor: Email: azad.rasul@soran.edu.iq

• Lecture 1: Welcome and Course Overview
• Lecture 2: Introduction to Geospatial Analysis
• Lecture 3: Introduction to Artificial Intelligence
• Lecture 4: Introduction to Machine Learning

• Lecture 5: Introduction to Python Programming
• Lecture 6: Python’s Role in Geospatial Applications
• Lecture 7: Setting Up Python - Installing Miniconda, Conda, and Python 3
• Lecture 8: Managing Python Environments and Packages with Anaconda
• Lecture 9: Installing and Running Jupyter Notebooks
• Lecture 10: Getting Started with Google Colab
• Lecture 11: Calculating Remote Sensing Indices in Python
• Lecture 12: Conducting Zonal Statistics in Python
• Lecture 13: Visualizing Geospatial Data with Python (Parts 1-3)
• Lecture 14: Hands-On Crop Data Analysis with Python

• Lecture 15: Practical Project Part 1 - Geospatial Analysis, ML, and Data Processing
• Lecture 16: Practical Project Part 2 - Geospatial Analysis, ML, and Data Processing
• Lecture 17: Practical Project Part 3 - Geospatial Analysis, ML, and Data Processing
• Lecture 18: Practical Project Part 4 - Geospatial Analysis, ML, and Data Processing
• Lecture 19: Practical Project Part 5 - Geospatial Analysis, ML, and Data Processing
• Lecture 20: Building a Machine Learning Model for Crop Health Analysis

• Lecture 21: Building a Convolutional Neural Network (CNN) for Image Classification with PyTorch
• Lecture 22: Applying Deep Learning for Global Weather Emulation with FourCastNet

• Lecture 23: Air Quality Monitoring in India - A Python and ML Case Study (Parts 1-4)
• Lecture 24: Advanced Machine Learning Techniques for Classifying Complex Geospatial Data

• Lecture 25: Detecting and Counting Plants Using Computer Vision Techniques

By the end of this course, students will be able to:

1. Grasp foundational AI and ML concepts for geospatial data analysis.
2. Utilize Python to manipulate and visualize geospatial data effectively.
3. Apply machine learning algorithms to spatial datasets.
4. Develop and interpret models to derive actionable insights from geospatial data.
5. Use deep learning techniques to analyze complex geospatial datasets.

• Basic Python knowledge is recommended.
• Familiarity with geospatial data concepts is helpful but not required.
• Access to a computer with internet for running Python tools and attending live sessions.
• No prior experience with machine learning or AI required – beginners are welcome!

This course is ideal for:

• Data scientists and analysts looking to specialize in geospatial AI applications.
• Researchers and students interested in advancing their skills in AI for environmental monitoring and spatial analysis.
• Professionals in agriculture, climate science, and urban planning wanting to use data-driven insights for better decision-making.
• Anyone passionate about learning AI-driven solutions to tackle geospatial data challenges.

Unlock the transformative potential of AI, Deep Learning, and Machine Learning in geospatial analysis. This course, designed for Python users, provides essential skills for solving real-world problems across fields like agriculture, environmental monitoring, and air quality analysis.

Beginning with foundational Python skills, you'll learn to manipulate, visualize, and analyze geospatial data. Key concepts in machine learning and deep learning are introduced, followed by applications tailored for spatial data. Practical projects include image classification for vegetation health, plant detection, and air quality monitoring using ML.

By the course’s end, you’ll confidently preprocess geospatial data, build models, and interpret results to drive insights. Tailored for researchers, analysts, and developers, this course offers a structured path to mastering AI and ML for geospatial challenges. Join us on this journey and start making an impact with AI-driven geospatial analysis.

• Price: $500 for personalized instruction

• Description: This option includes tailored lessons focusing on the student’s specific interests and projects in geospatial analysis and AI/ML. Students will receive dedicated support, personalized feedback, and the flexibility to schedule sessions according to their availability.

• Contact: For more details and to discuss your learning goals, please contact the instructor at azad.rasul@soran.edu.iq.