OSSU - Computer Science - Python for Everybody
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Updated
Mar 18, 2022 - Python
OSSU - Computer Science - Python for Everybody
solving problems from the course from https://books.trinket.io
Notes de l'especialització 'Python for Everybody' de la University of Michigan a Coursera
This is my solution for the Chapter 11 exercise of the book version of Python for Everybody. It simulates the grep command for regular expressions on Unix.
Based off of lessons from Dr. Chuck Severance's Py4e course on Coursera. Repo will be updated as I progress through the specialization.
This is my solution for "Finding Numbers in a Haystack" from Chapter 11 of Python for Everybody
This repository contains the projects and assignments developed during the Python for Everybody Specialization available on Coursera.
This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.
This repository contains Python games that I've worked on. You'll learn how to create python games with AI. I try to focus on creating board games without GUI in Jupyter-notebook.
This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply…
Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk thr…
I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.
Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to …
Time is undoubtedly the most critical factor in every aspect of life. Therefore, it becomes very essential to record and track this component. In Python, date and time can be tracked through its built-in libraries. This article on Date and time in Python will help you understand how to find and modify the dates and time using the time and dateti…
Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but like other concepts of Python, this concept here …
The best way to learn Python is by practicing examples. The repository contains examples of basic concepts of Python. You are advised to take the references from these examples and try them on your own.
Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.
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