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M1V8 - GitHub Mini Demonstration - Artificial Intelligence for Lunar Exploration
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Welcome to "AI for Lunar Exploration - Python to AI" – your comprehensive guide to harnessing the power of artificial intelligence for space discovery. Designed for aspiring data scientists, AI enthusiasts, and space technology professionals, this course provides a unique opportunity to delve into the world of AI with a focus on lunar exploration.
In this course, you'll start with the basics by setting up your development environment, including Python, VS Code, Git, and GitHub. You'll then move on to mastering Python programming, covering essential concepts like data types, control flow, functions, and uploading your code to GitHub.
Next, you'll explore Object-Oriented Programming (OOP) by building a rocket simulation. This hands-on project will deepen your understanding of OOP principles and how to apply them in real-world scenarios.
The course then introduces you to critical Python libraries such as NumPy and Matplotlib. You'll learn how to manipulate data and create stunning visualizations, skills crucial for any data scientist.
We then dive into machine learning, starting with the basics of linear regression, and progressing to deploying your models as APIs using FastAPI. You'll gain practical experience in training, testing, and evaluating machine learning models.
Our deep learning modules will guide you through building neural networks from scratch, understanding convolutional neural networks (CNNs), and applying these techniques to classify celestial objects like stars, galaxies, and quasars. You'll also learn to perform lunar image segmentation using advanced models like UNET.
Finally, you'll create a web application using Streamlit to visualize and interact with lunar image segmentation results, bringing your AI models to life.
By the end of this course, you'll have a robust skill set in Python programming, machine learning, deep learning, and web application development. You'll be ready to tackle real-world challenges in lunar exploration and beyond.
In this course, you'll start with the basics by setting up your development environment, including Python, VS Code, Git, and GitHub. You'll then move on to mastering Python programming, covering essential concepts like data types, control flow, functions, and uploading your code to GitHub.
Next, you'll explore Object-Oriented Programming (OOP) by building a rocket simulation. This hands-on project will deepen your understanding of OOP principles and how to apply them in real-world scenarios.
The course then introduces you to critical Python libraries such as NumPy and Matplotlib. You'll learn how to manipulate data and create stunning visualizations, skills crucial for any data scientist.
We then dive into machine learning, starting with the basics of linear regression, and progressing to deploying your models as APIs using FastAPI. You'll gain practical experience in training, testing, and evaluating machine learning models.
Our deep learning modules will guide you through building neural networks from scratch, understanding convolutional neural networks (CNNs), and applying these techniques to classify celestial objects like stars, galaxies, and quasars. You'll also learn to perform lunar image segmentation using advanced models like UNET.
Finally, you'll create a web application using Streamlit to visualize and interact with lunar image segmentation results, bringing your AI models to life.
By the end of this course, you'll have a robust skill set in Python programming, machine learning, deep learning, and web application development. You'll be ready to tackle real-world challenges in lunar exploration and beyond.