Artificial Intelligence For Lunar Exploration - Python To Ai

Master AI for Lunar Exploration: Python, Machine Learning, Deep Learning, and Image Segmentation

What you'll learn

Master the installation of essential coding tools such as Python, VS Code, Git, and GitHub.

Gain a solid foundation in Python, covering basics like data types, control flow, and functions, and learn to upload code to GitHub.

Develop a deep understanding of Object-Oriented Programming by building a rocket simulation.

Explore key Python libraries such as NumPy and Matplotlib for data manipulation and visualization.

Understand the fundamentals of machine learning, including linear regression, and deploy ML models as APIs using FastAPI.

Dive into deep learning, build neural networks from scratch, and learn about convolutional neural networks (CNNs) for image classification.

Apply AI techniques to classify celestial objects and perform lunar image segmentation using advanced models like UNET.

Create a web application using Streamlit to visualize and interact with lunar image segmentation results.

Requirements

No Programming experience required.

Description

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.Enroll now to start your journey in "AI for Lunar Exploration" and take a giant leap in your AI and space exploration career!

Overview

Section 1: Introduction

Lecture 1 Introduction to the program and modules

Lecture 2 Different Coding Platforms we will be using

Lecture 3 Python Installation

Lecture 4 VS Code Editor Installation

Lecture 5 Git and GitHub

Lecture 6 Git Installation

Lecture 7 GitHub: Account Setup

Lecture 8 GitHub: Mini Demonstration

Lecture 9 GitHub: Branches - Pull Request

Lecture 10 Module Outro

Section 2: Master the Basics of Python

Lecture 11 Introduction

Lecture 12 Google Colab Introduction

Lecture 13 Comments in Python

Lecture 14 Variables and Constants

Lecture 15 Basic Data Types

Lecture 16 f-Strings

Lecture 17 User Inputs

Lecture 18 Data Type Conversion

Lecture 19 Control Flow

Lecture 20 Functions

Lecture 21 Python Notebook to GitHub

Lecture 22 Module Outro

Section 3: Build a Rocket using Object Oriented Programming

Lecture 23 Introduction to Module 3

Lecture 24 Introduction to OOPs and General Terminologies

Lecture 25 Create a Simple Rocket Class that does Nothing

Lecture 26 Adding Constructor for Rocket

Lecture 27 Adding Move Up Method

Lecture 28 Create multiple Rockets and move some of them

Lecture 29 Refining the Rocket Class - Adding Parameters

Lecture 30 Adding a new Method: Get Distance between the Rockets

Lecture 31 Upload the Python Notebook to GitHub

Lecture 32 Module Outro

Section 4: Important Data Processing and Analysis Libraries in Python

Lecture 33 Module 4 Introduction

Lecture 34 Introduction to Python Libraries

Lecture 35 Import libraries in Python

Lecture 36 Create Numpy arrays and use its functionalities

Lecture 37 Different ways to create Numpy Arrays

Lecture 38 Random Module of NumPy

Lecture 39 Create first visualisation using Matplotlib

Lecture 40 Customising the Plot

Lecture 41 Upload the Python Notebook to GitHub

Lecture 42 Module outro

Section 5: Introduction to Machine Learning

Lecture 43 Module 5 Introduction

Lecture 44 Definitions of AI and ML

Lecture 45 Applications of AI

Lecture 46 Supervised vs Un-Supervised vs Reinforcement

Lecture 47 Linear Regression: Intuition

Lecture 48 Linear Regression: Cost Function

Lecture 49 Linear Regression: Gradient Descent

Lecture 50 Get ready with the Code Along file!

Lecture 51 Generate the Dummy Training Dataset

Lecture 52 Customise the Plot and Get Ready with Model Parameters

Lecture 53 Build functions for prediction and cost

Lecture 54 Build function for Updating Parameters

Lecture 55 Build function for Training and Train the Model

Lecture 56 Check the Model Performance

Lecture 57 Generate the Testing Dataset and Evaluate the Model

Lecture 58 Upload the Python Notebook to GitHub

Lecture 59 Module Outro

Section 6: Deploy ML model as API using FastAPI

Lecture 60 Module 6 Introduction

Lecture 61 Introduction to Logistic Regression

Lecture 62 Dataset and Aim

Lecture 63 Explore the Dataset

Lecture 64 Prepare the Dataset and Pipeline

Lecture 65 Use Pipeline for Training and Testing

Lecture 66 Download the Pipeline and Test it

Lecture 67 Introduction to FastAPI

Lecture 68 Project Setup and model.pkl

Lecture 69 Load the Model and Make Predictions

Lecture 70 Refactoring the predictor.py file

Lecture 71 Create FastAPI App

Lecture 72 BaseModel and Field from Pydantic

Lecture 73 Testing API on the Real Star Data

Lecture 74 Adding README.md

Lecture 75 Module Outro

Section 7: Introduction to Deep Learning

Lecture 76 Module 7 Introduction

Lecture 77 Introduction to Deep Learning

Lecture 78 Artificial Neuron and Biological Neuron

Lecture 79 Introduction to Multi-Layer Perceptron

Lecture 80 Most Commonly used Activation Functions

Lecture 81 Problem Statement to Build a Neural Network from scratch

Lecture 82 Understanding the Network to Build

Lecture 83 Equations - Cost Function, Forward and Backward Propagation

Lecture 84 Derivation of Backward Propagation Equations

Lecture 85 Code the Neural Network from Scratch

Lecture 86 Module Outro

Section 8: Classify Stars, Galaxies, and Quasars using Deep Learning

Lecture 87 Module 8 Introduction

Lecture 88 Problem Statement and Adding Data to Notebook

Lecture 89 Read the csv file and Explore the data

Lecture 90 Create Visualisations

Lecture 91 Split the Data into Training and Testing

Lecture 92 Preprocessing the Data

Lecture 93 Build the Network using Tensorflow and Keras and Compile it

Lecture 94 Train the Network and Visualise the Training

Lecture 95 Test the Model on the Unseen Data

Lecture 96 Concluding the Problem Statement and Saving the Model

Lecture 97 Module Outro

Section 9: Introduction to Convolutional Neural Networks

Lecture 98 Module 9 Introduction

Lecture 99 Understand an Image

Lecture 100 Example of a CNN Architecture

Lecture 101 Convolution Operation

Lecture 102 Importance of Strides and Padding

Lecture 103 Calculate the Output Shape of Conv2D layer

Lecture 104 Calculate total trainable Parameters in Conv2D layer

Lecture 105 Example of a complete Convolution Calculation

Lecture 106 Summary of convolution operation

Lecture 107 Pooling Operation

Lecture 108 Fully Connected Layers

Lecture 109 Module Outro

Section 10: Morphological Classification of Galaxy Images

Lecture 110 Module 10 Introduction

Lecture 111 Important Notes

Lecture 112 Upload the Code Along file to Kaggle

Lecture 113 Intro to Dataset and Problem Statement

Lecture 114 Get the Dataset in the notebook

Lecture 115 Importing libraries

Lecture 116 Read the csv file and perform the train test split

Lecture 117 Visualise random images in the data

Lecture 118 Create a function to preprocess one image

Lecture 119 Create a function to preprocess all the images in the data

Lecture 120 Build, Compile the CNN Model

Lecture 121 Train the CNN Model

Lecture 122 Make the predictions on the test dataset

Lecture 123 Module outro

Section 11: Getting Started with the Final Project

Lecture 124 Module 11 Introduction

Lecture 125 Different types of Problem Statements using CNN

Lecture 126 Understand our problem statement

Lecture 127 Which evaluation Metric will we use?

Lecture 128 Which cost function will we use?

Lecture 129 UNET Intro and Transfer Learning

Lecture 130 Contractive and Expansive Paths of UNET

Lecture 131 Skip Connections and Bridge in the UNET

Lecture 132 How does UNET actually work in this way?

Lecture 133 Module Outro

Section 12: Create UNET for the Lunar Dataset

Lecture 134 Module 12 Introduction

Lecture 135 More on Dataset

Lecture 136 Importing some of the necessary libraries

Lecture 137 Select the Dataset for Demonstration

Lecture 138 Data Preprocessing

Lecture 139 Splitting the Data into Training and Validation

Lecture 140 Data Pipeline

Lecture 141 How will we create UNET architecture?

Lecture 142 Create UNET architecture for our Data

Lecture 143 Load and Compile the Model

Lecture 144 Train the Model

Lecture 145 Evaluate the Model on Test Data

Lecture 146 Module Outro

Section 13: Web Application using Streamlit for Lunar Image Segmentation

Lecture 147 Module 13 Introduction

Lecture 148 Prepare Training, Validation, and Testing Dataset

Lecture 149 Understand the mask preprocessing and postprocessing

Lecture 150 Build a Class for loading the Lunar Dataset

Lecture 151 Build and Visualise the Dataset

Lecture 152 Setting up segmentation_models for Transfer Learning

Lecture 153 Build UNET with segmentation_models and VGG16 backbone

Lecture 154 Compile the Model

Lecture 155 Add the Callbacks

Lecture 156 Train the Model

Lecture 157 Predict Image Function

Lecture 158 Evaluate the Model Performance and Save the Model

Lecture 159 Create a Web App for Lunar Segmentation using Streamlit

Lecture 160 Module Outro

Individuals looking to build a strong foundation in AI and machine learning with a unique focus on lunar exploration.,Those studying computer science, astrophysics, or related fields who want to apply AI techniques to space exploration.,Developers with a basic understanding of Python who wish to advance their skills in machine learning, deep learning, and data science.,Engineers and scientists working in the space industry who want to leverage AI for innovative solutions in lunar exploration.,Anyone passionate about space and technology, eager to learn how AI can be used to explore and understand the moon.