Code refers to the set of instructions written in a programming language that a computer can execute to perform a specific task. This topic covers the fundamentals of writing, reading, and understanding code, as well as best practices for maintaining and optimizing it. It includes discussions on different programming languages, coding standards, and tools used to write and debug code. The topic also explores the importance of code in software development, automation, and various technological innovations.
Jupyter Notebooks for How to Think Like a Computer Scientist - Learning with Python 3 (RLE) Textbook
FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, Kaggle, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya, Midjourney, RunPod
Scientific Computing for Chemists with Python is a free book for teaching basic coding skills to chemists using Python, Jupyter notebooks, and the other Python software. This textbook teaches a variety of Python packages including NumPy, SciPy, matplotlib, pandas, seaborn, nmrglue, SymPy, scikit-image, scikit-learn, and others.
A free notebook on Notion of bunches of details about what makes a qualified developer.
General-purpose repository for illustrative examples on coding and data analysis
My notebook for lessons from the QA Training Academy.
This is a collection of Markdown notes written in Obsidian for programming and it includes notes on C, C++, using and configuring VSCode and many other things programming related. Anyone is welcome to contribute to this Markdown repository and make corrections.
Welcome to my notebook
Analysis using Pandas DataFrame in Jupyter Notebook
Welcome to my 15-day python challenge! This repository contains the Jupyter notebook utilized for practicing python fundamentals. This notebook is concise and is suitable for learning purposes.
It is a notebook of pre-processing steps and visualization of health screening data
Machine Learning in PySpark
This project demonstrates how to interact with RESTful APIs using Python in a Jupyter Notebook environment. It includes real-world examples, error handling, data extraction, and formatting. The project aims to help developers and analysts get started with fetching data from APIs,
The data is being analysed on the basis of the types of attack. The attack includes various types and we will be working with those IP addresses that are attacking our personal computers, so in this project I have excluded distinct ip addresses of every attack that are causing harm to our computers. The project is coded on jupyter notebook in anna
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