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The "LearningPython" repository is a collection of codes and scripts developed during the journey of learning the Python programming language. The codes cover a wide range of topics from basic concepts to advanced techniques and libraries. The repository serves as a valuable resource for those who are learning Python.

Python 0.23% Jupyter Notebook 99.77%
machine-learning numpy pandas python

learning-python's Introduction

Learning Python

Welcome to the "LearningPython" repository! Here, you will find all of the codes and scripts developed during my journey to learn the Python programming language. The codes cover a wide range of topics, from basic programming concepts to advanced techniques and libraries. The repository is intended to be a valuable resource for those who are also learning Python, and as such, it includes detailed comments and explanations for each code sample.

Python Pandas

One of the most important libraries in python is Pandas. Pandas is a powerful library used for working with data sets. It provides a wide range of functions for analyzing, cleaning, exploring, and manipulating data. This library is built on top of the Python programming language which makes it easy to use and understand. It is a fast, flexible and powerful open-source data analysis and manipulation tool.

For more information on Pandas, please refer to the Pandas Notes file located in the Learning Pandas folder of this repository. Feel free to use the codes in this repository as a reference or starting point for your own projects.

Machine Learning

Machine learning is a branch of artificial intelligence that focuses on developing computer systems capable of learning and improving from data without being explicitly programmed. It involves the development of algorithms and statistical models that enable machines to recognize patterns, make predictions, and make decisions based on past experiences or examples. Machine learning algorithms can automatically analyze and interpret vast amounts of data, extracting meaningful insights and patterns to make accurate predictions or classifications. These algorithms typically learn iteratively, adjusting their internal parameters based on training data, in order to optimize their performance over time. Machine learning has a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous vehicles, and medical diagnostics, among others.

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