Astronomy Data Science With Python Programming

Learn Astronomy with Python, Image Processing, and Machine Learning with practical projects and step-by-step guidance.

What you'll learn

Master Python programming, including data structures, loops, and libraries like NumPy and Matplotlib.

Learn digital image processing and apply convolution, edge detection, and filters on real-world datasets.

Understand and implement machine learning models like Linear and Logistic Regression using Python.

Build and train neural networks and convolutional neural networks (CNNs) from scratch using TensorFlow and Keras.

Requirements

No Programming or Astronomy Experience Required

Description

This course is designed to take you from a beginner to a confident practitioner in Python programming, image processing, and machine learning. Through step-by-step lessons and hands-on projects, you will build a solid foundation in these essential skills and apply them to real-world problems.What You’ll Learn:Python Programming: Master Python basics, including data types, variables, loops, conditional statements, and libraries like NumPy and Matplotlib.Image Processing: Learn how to process digital images using Python, including convolution operations, edge detection, and filters.Machine Learning: Gain a strong understanding of core ML concepts, including Linear and Logistic Regression, with practical coding examples.Deep Learning and CNNs: Build neural networks from scratch, train them using TensorFlow and Keras, and explore convolutional neural networks (CNNs).Hands-on Projects:You’ll work on engaging projects such as:Analyzing real astronomical image datasets like NGC3184 and M87.Building and training machine learning models for classification and regression tasks.Implementing neural networks and CNNs to solve real-world problems using Kaggle datasets.Who This Course Is For:Beginners with no prior experience in Python or machine learning.Students and professionals looking to strengthen their knowledge of AI and data science.Anyone interested in exploring how programming and AI are applied to real-world scenarios, such as image processing and astronomy.By the end of this course, you’ll have the skills to confidently build Python programs, process digital images, and implement machine learning models. Whether you’re a student, researcher, or tech enthusiast, this course will empower you to take the next step in your learning journey.Let me know if you’d like to adjust this further!

Overview

Section 1: Python Module

Lecture 1 Introduction

Lecture 2 Python Module

Lecture 3 Python Comments

Lecture 4 Data Type – Strings

Lecture 5 Variables and Constants

Lecture 6 Data Type – Numerical

Lecture 7 Data Types Conversion

Lecture 8 Data Type – Boolean and Python Operators

Lecture 9 String Methods

Lecture 10 Data Structure – List

Lecture 11 Data Structure – Tuple

Lecture 12 Data Structure – Set

Lecture 13 Data Structure – Dictionary

Lecture 14 Data Structure Conversions

Lecture 15 Conditional Statements

Lecture 16 For Loop

Lecture 17 While Loop

Lecture 18 Functions

Lecture 19 Object Oriented Programming

Lecture 20 Numpy Library – 1

Lecture 21 Numpy Library – 2

Lecture 22 Matplotlib Library

Lecture 23 Module 1 Conclusion

Section 2: Image Processing

Lecture 24 Image Processing Module

Lecture 25 Digital Images

Lecture 26 Bits and Bits per Pixel

Lecture 27 Digital Image Processing and Computer Vision

Lecture 0 Images in Python – 1

Lecture 28 Images in Python – 2

Lecture 29 Convolutions

Lecture 30 Gaussian Kernel

Lecture 31 Unsharp Mask

Lecture 32 Canny Edge Detector

Lecture 33 Basic Multiscale Features

Lecture 34 FITS Files

Lecture 35 Galaxies Morphology Classification

Lecture 0 Header – NGC3184

Lecture 36 Image Data – NGC3184

Lecture 37 Wide Scale and Nucleus Scale – NGC3184

Lecture 38 Implement Filters on Image Data – NGC3184

Lecture 39 Concluding – NGC3184 Project

Lecture 40 Introducing M87 and M87*

Lecture 41 Exploring M87 using Python – 1

Lecture 42 Exploring M87 using Python – 2

Lecture 43 Exploring M87 using Python – 3

Lecture 44 Module 2 Conclusion

Section 3: Introduction to Machine Learning

Lecture 45 Introduction to Machine Learning

Lecture 46 Introduction to Artificial Intelligence and Machine Learning

Lecture 47 Applications of Artificial Intelligence

Lecture 48 Supervised vs Unsupervised vs Reinforcement

Lecture 49 Linear Regression: Intuition

Lecture 50 Linear Regression: Cost Function

Lecture 51 Linear Regression: Gradient Descent

Lecture 52 Get ready with the Code Along file!

Lecture 0 Generate the Dummy Training Dataset

Lecture 53 Customise the Plot and Get Ready with Model Parameters

Lecture 54 Build functions for prediction and cost

Lecture 0 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 Introduction to Logistic Regression

Lecture 0 Dataset and Aim

Lecture 59 Explore the Dataset

Lecture 0 Prepare the Dataset and Pipeline

Lecture 0 Use Pipeline for Training and Testing

Lecture 60 Download the Pipeline and Test it

Lecture 61 Module 3 Conclusion

Section 4: Introduction to Deep Learning

Lecture 62 Introduction to Deep Learning

Lecture 63 What is Deep Learning?

Lecture 0 Artificial Neuron and Biological Neuron

Lecture 64 Introduction to Multi-Layer Perceptron

Lecture 0 Most Commonly used Activation Functions

Lecture 65 Problem Statement to Build a Neural Network from scratch

Lecture 0 Understanding the Network to Build

Lecture 0 Equations - Cost Function, Forward and Backward Propagation

Lecture 66 Derivation of Backward Propagation Equations

Lecture 67 Code the Neural Network from Scratch

Lecture 0 Problem Statement and Adding Data to Notebook

Lecture 0 Read the csv file and explore the data

Lecture 68 Create Visualisations

Lecture 69 Split the Data into Training and Testing

Lecture 70 Preprocessing the Data

Lecture 71 Build the Network using Tensorflow and Keras and Compile it

Lecture 0 Train the Network and Visualise the Training

Lecture 0 Test the Model on the Unseen Data

Lecture 0 Concluding the Problem Statement and Saving the Model

Lecture 0 Module 4 Conclusion

Section 5: Convolutional Neural Networks (CNNs)

Lecture 0 Convolutional Neural Networks (CNNs)

Lecture 0 Example of a CNN Architecture

Lecture 72 Calculate the Output Shape of Conv2D layer

Lecture 0 Calculate total trainable Parameters in Conv2D layer

Lecture 73 Example of a complete Convolution Calculation

Lecture 74 Summary of convolution operation

Lecture 0 Pooling Operation

Lecture 75 Fully Connected Layers

Lecture 76 Kaggle Setup

Lecture 77 Intro to Dataset and Problem Statement

Lecture 78 Get the Dataset in the notebook

Lecture 79 Importing libraries

Lecture 80 Read the csv file and perform the train test split

Lecture 81 Visualise random images in the data

Lecture 82 Create a function to preprocess one image

Lecture 83 Create a function to preprocess all the images in the data

Lecture 84 Build, Compile the CNN Model

Lecture 85 Train the CNN Model

Lecture 86 Make the predictions on the test dataset

Lecture 87 Module 5 Conclusion

Lecture 88 Course Conclusion

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