Data science resources

This project is a comprehensive collection of machine learning educational resources, featuring a Python-based curriculum, study guides for deep learning, and a specialized knowledge base for machine learning operations. It provides structured learning paths that guide users from foundational programming through to advanced neural network implementations. The repository focuses on interactive learning by providing a directory of executable notebooks and cloud-hosted experiments. It maps theoretical research papers and textbooks to practical code implementations and maintains a curated directo

This repository is a collection of interactive Jupyter notebooks designed as an educational resource for learning machine learning and data science. It provides a structured curriculum that guides users through the development of predictive models and the analysis of datasets using standard Python libraries. The project utilizes a narrative-driven approach where explanatory text is interleaved with executable code blocks. This format allows learners to execute workflows step-by-step, enabling the visualization of data patterns and the practical implementation of mathematical models within a p

This project is a comprehensive, curated knowledge base designed to support the development and maintenance of production-grade machine learning systems. It serves as a centralized repository of industry-standard technical literature, engineering case studies, and research papers, providing a structured reference for practitioners navigating the complexities of modern data science and machine learning engineering. The resource distinguishes itself through a cross-domain approach that bridges the gap between academic research and practical implementation. By synthesizing proven industry archit

This project is a collection of interactive Python notebooks and educational resources designed for mastering data science, machine learning, and numerical computing. It provides a series of practical guides and tutorials covering deep learning, big data processing, and statistical analysis. The repository features specialized instructional suites for implementing classical machine learning algorithms, building deep learning model architectures, and managing AWS cloud infrastructure. It includes dedicated notebooks for data visualization and numerical computing exercises. The project covers

This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr

This project is a comprehensive educational curriculum designed to teach the fundamental concepts, workflows, and tools of data science. It provides a structured learning path that covers the end-to-end data science lifecycle, including data acquisition, maintenance, processing, and pattern discovery, while grounding theoretical knowledge in practical, real-world applications. The curriculum distinguishes itself through a data-driven pedagogical design that utilizes interactive, notebook-based lessons. By combining narrative text with live code blocks, the platform allows learners to experime

This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

This project is a comprehensive, community-driven knowledge repository that serves as a centralized hub for data science resources. It provides a structured index of educational materials, software packages, and professional development tools designed to support both students and practitioners in navigating the data science landscape. The repository distinguishes itself through a hierarchical taxonomy that organizes a vast collection of external links into a human-readable, markdown-based document. By relying on distributed contributions, the project maintains an up-to-date snapshot of the fi

This project is a Python education repository and programming tutorial designed to teach language fundamentals, from basic syntax and variables to advanced concepts. It serves as a data science starter kit and a guide for REST API integration. The repository provides instructional scripts and sample code covering object-oriented programming patterns and asynchronous programming. It includes practical demonstrations for fetching and processing JSON data from external web services using HTTP requests. The materials cover a broad capability surface including data analysis workflows with interac

This project is a curated collection of technical reference materials and study guides designed for machine learning interview preparation. It provides comprehensive resources for candidates pursuing engineering roles, focusing on deep learning, production infrastructure, and large-scale system design. The repository distinguishes itself through an architecture that combines theoretical research with industrial case studies. It utilizes a pattern-based approach to system design, breaking down complex deployments—such as recommendation engines, search ranking, and ad click prediction—into reus

This repository serves as a structured educational resource for machine learning and data science, providing a centralized collection of tutorials, lecture notes, and implementation guides. It is designed to support self-directed learning by organizing complex technical concepts into a clear, hierarchical path that spans from foundational statistical methods to advanced deep learning architectures. The project distinguishes itself through a comprehensive approach to skill development, bridging the gap between theoretical algorithmic foundations and functional software applications. It offers

This project is a technical curriculum and learning path for machine learning, providing a structured sequence of mathematical foundations, core concepts, and professional workflows. It serves as a comprehensive guide and resource index that connects theoretical principles to the specific software libraries and tools used in real-world implementation. The repository functions as a project workflow blueprint, outlining the sequential steps required to solve machine learning problems from initial discovery through to final deployment. It maps theoretical mathematical principles to practical app

This project is a structured, open-source educational roadmap designed to guide students through a comprehensive undergraduate-level curriculum in data science. It provides a curated sequence of high-quality learning materials that focus on mastering computational logic, software development, and statistical analysis using the Python programming language. The curriculum distinguishes itself by integrating project-based competency validation, requiring learners to execute capstone projects that demonstrate professional skill mastery. It utilizes version control tools to allow students to track

This repository contains resources and cheatsheets that should be helpful for anyone learning or practicing data science. Vast majority of the resources is geared towards Python users, but there's a page for R resources. There are translations of this page at the bottom. Please feel free to fork…

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place.

Probably the best curated list of data science software in Python.

This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries. The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical

This project is a structured data science curriculum and Python-based textbook designed to teach the fundamentals of data science through executable scripts and hands-on lessons. It functions as a guided programming tutorial for data manipulation and analysis within the Python ecosystem. The content covers introductory machine learning, including the implementation of basic models and algorithms, alongside Python data analysis for cleaning and processing datasets. The material is delivered via Jupyter Notebooks, combining modular exercises and markdown-driven documentation to map theoretical

This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

Ai-Learn is an educational repository and technical reference designed to facilitate the mastery of artificial intelligence and data science workflows. It provides a structured curriculum that combines theoretical mathematical foundations with practical coding exercises, enabling users to build predictive models, neural networks, and analytical pipelines using Python. The project distinguishes itself by emphasizing a first-principles approach to machine learning. Rather than relying solely on high-level abstractions, it guides users through the reconstruction of core algorithms from scratch,

This project is a comprehensive, day-by-day curriculum designed to guide learners through the Python programming language and its professional applications. The content spans from fundamental syntax and object-oriented design to advanced topics including database management, web development, data analysis, and machine learning. The curriculum is structured into distinct modules that cover practical software engineering practices, such as version control, containerization, and system architecture. It also provides resources for technical interview preparation and an analysis of career paths wi

This repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures. The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w

This repository is a collection of structured coding challenges designed to build proficiency in data manipulation, cleaning, and transformation using the Python data analysis library. It functions as a hands-on tutorial for learning how to process and analyze tabular datasets through a series of practical, real-world exercises. The project utilizes interactive documents that combine live code cells with narrative text, allowing users to execute data manipulation logic in a persistent environment. The content is organized into modular, progressive units that increase in complexity, enabling u

This project is a community-driven educational repository that provides a structured curriculum for mastering machine learning and data science. It serves as a resource for developers to build practical models from scratch, reinforcing theoretical knowledge through direct implementation and iterative experimentation with common algorithms. The repository is organized into modular directories, allowing learners to explore and experiment with specific machine learning exercises independently. The content is maintained through a collaborative workflow where contributors use version control and p

This project serves as a comprehensive educational resource and curriculum for mastering machine learning and deep learning within the Python data science ecosystem. It provides a structured collection of tutorials and code examples designed to guide users through the end-to-end process of building, training, and deploying predictive models. The material focuses on practical implementation, covering the construction of machine learning pipelines that integrate data processing, feature engineering, and model training. It distinguishes itself by offering hands-on guidance for complex domains, i

This project is an open-source educational curriculum designed to provide a structured path for developers to master machine learning and generative AI. It functions as a technical skill development platform, offering comprehensive study materials that guide learners through fundamental concepts, algorithms, and the practical implementation of artificial intelligence models from scratch. The curriculum distinguishes itself through a pedagogy centered on interactive Jupyter Notebooks, which allow students to execute code cells directly within narrative documents for immediate visual feedback.

Note from the Editor: Take Two

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

This project is a data science reference sheet and machine learning study guide. It provides a curated collection of formulas, definitions, and model summaries designed for quick lookup during project development and technical interview preparation. The resource is delivered as a static PDF educational resource. It organizes complex technical frameworks and theoretical machine learning concepts into a portable, fixed-layout document to ensure consistent visual presentation across different devices. The content covers machine learning concept references and data science knowledge synthesis, s

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

Free resources for learning data science

100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier

This project is a curated educational curriculum and technical skill roadmap designed to guide learners through the core competencies required for professional data science roles. It provides a structured sequence of educational materials and tutorials, arranging prerequisite skills and advanced topics into a dependency-based learning path. The curriculum covers specific training tracks for data science fundamentals, machine learning study plans, and data engineering guides. These tracks focus on the theoretical knowledge and practical skills needed to manage data pipelines, apply statistics

Ways of doing Data Science Engineering and Machine Learning in R and Python