Welcome to the "100 Days of Machine Learning" repository! This is a comprehensive collection of resources and code snippets that document my journey through the realms of basic statistics and machine learning algorithms over the course of 100 days. Whether you're a beginner looking to get started or an experienced practitioner seeking a refresher, this repository aims to provide a structured and hands-on approach to learning.
Machine learning is a fascinating field that combines statistical analysis with computer science to enable systems to learn and make decisions without explicit programming. In this repository, I have documented my journey through basic statistics concepts and popular machine learning algorithms. The goal is to provide a resource that is both educational and practical, allowing anyone to follow along and build a solid foundation in machine learning.
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/statistics: This directory contains notebooks and scripts related to basic statistics concepts, including descriptive statistics, inferential statistics, probability, and more. -
/machine_learning_algorithms: Here, you'll find implementations and explanations of various machine learning algorithms, such as linear regression, decision trees, support vector machines, and more. -
/datasets: A collection of datasets used throughout the 100 days. Each dataset is carefully selected to cover different aspects of machine learning and statistics. -
/resources: Additional resources, including books, articles, and online courses that complement the content covered during the 100 days.
Explore the notebooks and scripts in the /statistics and /machine_learning_algorithms directories to delve into the world of basic statistics and machine learning. Each file is accompanied by comments and explanations to facilitate understanding.
If you find any errors, have suggestions for improvement, or want to contribute additional content, please check out the Contributing Guidelines.
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Happy coding and learning! 🚀🤖