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Showing 113 open source projects for "python q learning"
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Book1_Python-For-Beginners
The Iris Book: Addition, Subtraction, Multiplication, and Division
...The material is structured to support self-paced learning, making it suitable for students, career switchers, and hobbyists. Because the book is part of a larger data science pathway, it also prepares readers for later work in visualization and machine learning. Overall, it serves as an accessible on-ramp into Python within a broader analytical learning journey. -
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Best-of Python
A ranked list of awesome Python open-source libraries
This curated list contains 390 awesome open-source projects with a total of 1.4M stars grouped into 28 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! Ranked list of awesome python libraries for web... -
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Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the... -
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Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
Hello-Python is a comprehensive tutorial repository aimed at teaching the Python programming language from scratch for beginners. It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed... -
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Complete-Python-Bootcamp
The complete Python bootcamp guide
Complete-Python-Bootcamp is a structured learning repository designed to walk learners from the very basics of Python to more advanced, real-world topics. It is organized into multiple numbered folders that mirror a course syllabus, starting with Python basics and control flow, then moving into data structures, functions, and modules. As you progress, you encounter practical concerns such as file handling, exception handling, and working with classes and objects, which are essential for writing robust applications. ... -
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List of Free Learning Resources
Freely available programming books
List of Free Learning Resources is a curated open-source collection of free programming resources, including books, tutorials, and courses across many languages and disciplines. Maintained by the community, it organizes materials by topic, language, and skill level, making it easy to discover learning resources. The repository includes content on software development, computer science, data science, and more. It is continuously updated with new resources contributed by developers worldwide.... -
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Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
The Complete-Python-3-Bootcamp repository is an educational resource created by Pierian Data as part of their popular Python for Data Science and Machine Learning Bootcamp course. It contains a comprehensive collection of Jupyter Notebooks designed to teach Python programming from the ground up. The repository covers a wide range of Python topics, including data types, control flow, functions, object-oriented programming, error handling, modules, and advanced concepts like decorators and generators. ... -
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Python Core 50 Courses
Structured learning path that organizes Python fundamentals
Python-Core-50-Courses is a structured learning path that organizes Python fundamentals into 50 digestible lessons designed for steady, incremental progress. The curriculum starts with the basics—syntax, variables, data types, and control flow—then advances to functions, modules, object-oriented programming, and common standard-library utilities. -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. ... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. ... -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project outlines, course materials, and references across topics like Python, statistics, ML algorithms, deep learning, NLP, data preprocessing, model evaluation, and real-world problem solving. ... -
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Book4_Power-of-Matrix
Book_4_Matrix Power | The Iris Book: From Addition, Subtraction
...The repository is continuously updated and intended to accompany the broader Visualize-ML learning ecosystem. Overall, it serves as a visually driven mathematical foundation for students preparing for data science and machine learning work. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The... -
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Oppia
A free, online learning platform to make quality education accessible
...In addition to developing the Oppia platform, the team is also developing and piloting a set of free and effective lessons on basic mathematics. These lessons are targeted at learners who lack access to educational resources. Oppia is written using Python and AngularJS, and is built on top of Google App Engine. -
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Web Dev for Beginners
About 24 Lessons, 12 Weeks, Get Started as a Web Developer
Web-Dev-For-Beginners is Microsoft’s open source, project-based curriculum for learning web development from scratch. Designed as a 12-week, 24-lesson course, it covers HTML, CSS, and JavaScript fundamentals through hands-on projects like terrariums, browser extensions, and space games. Each lesson includes a mix of pre-lecture quizzes, written content, assignments, challenges, and post-lecture quizzes to reinforce learning. The course also offers global accessibility with translations in... -
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
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freeCodeCamp
freeCodeCamp.org's open-source codebase and curriculum
freeCodeCamp is a nonprofit educational platform that offers a self-paced curriculum for learning web development, programming, data visualization, APIs, and algorithms. It features interactive coding challenges, real-world projects, and guided progress through topic modules, culminating in certificates for completed tracks. A key aspect is that students contribute to open-source projects for nonprofits or internal tooling as part of their learning, reinforcing both technical and... -
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The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides,... -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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DevOps Exercises
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git
DevOps Exercises is a massive, community-maintained collection of questions, tasks, and mini-challenges that cover the breadth of modern DevOps and platform engineering. It spans Linux, networking, Docker, Kubernetes, CI/CD, monitoring, cloud providers, security, and even soft skills and troubleshooting. The idea is to give candidates and teams a realistic practice ground for interviews, certifications, and day-to-day operational work. Because it’s structured as Q&A and exercises, you can go... -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The... -
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rust-by-practice
Challenging examples, exercises and projects
rust-by-practice is a hands-on, exercise-oriented learning resource for the Rust programming language that takes users beyond theory into real code challenges and practical patterns. Rather than simply listing Rust syntax or language features, it structures its content around progressively complex problems, each designed to illustrate a core Rust concept such as ownership, borrowing, lifetimes, traits, concurrency, zero-cost abstractions, and safe systems programming idioms. The repository... -
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Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
Book2_Beauty-of-Data-Visualization is an open educational project that teaches the principles and techniques of effective data visualization using Python and modern plotting libraries. The repository focuses on both the technical and aesthetic aspects of visual analytics, helping learners understand how to communicate data clearly and persuasively. It includes practical examples that demonstrate how different chart types reveal patterns, trends, and distributions in real datasets. The... -
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CS-Books
Collection of computer science textbooks, learning materials
CS-Books is a massive curated collection of computer science textbooks, learning materials, and resource links that covers a wide range of topics from programming languages like C/C++ and Python to core subjects such as data structures, algorithms, operating systems, databases, networks, and design patterns. The repository aggregates over a thousand classic reference books and educational resources into a single index, making it a valuable starting point for self-learners, students preparing for technical interviews, and professionals deepening their knowledge across different CS domains. ... -
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Think Python 2
LaTeX source and supporting code for Think Python, 2nd edition
ThinkPython2 is the repository for the second edition of Allen Downey’s Think Python textbook, which teaches programming fundamentals in Python to beginners. The code includes all of the example programs, exercises, and supplementary files referenced in the book, allowing learners to run the examples, experiment, and extend them. The repository contains clean, well-commented Python scripts that are easy to follow and map directly to chapters of the text, covering topics like variables, control flow, functions, recursion, data structures (lists, dictionaries), classes and objects, file I/O, and algorithmic thinking. ...
