3 projects for "mining software linux" with 2 filters applied:

  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

    New customers can spin up VMs, build with AI, and query data at no cost.

    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
    Start Free
  • Veeam Data Platform v13.1 - Get Your Free Trial Icon
    Veeam Data Platform v13.1 - Get Your Free Trial

    Secure by design, portable by default. Recover clean, fast, anywhere. Start a free trial.

    Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
    Try it Free
  • 1
    sadsa

    sadsa

    SADSA (Software Application for Data Science and Analytics)

    SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    Self-learning-Computer-Science

    Self-learning-Computer-Science

    Resources to learn computer science in your spare time

    Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    Stats 337

    Stats 337

    Readings in applied data science

    Stats 337 is the repository for a Stanford discussion course titled Readings in Applied Data Science, taught in Spring 2018. Students were expected to read several papers or equivalent resources each week and discuss their implications in class. The reading list focuses on applied topics that the instructor considered important but often underrepresented in traditional data science curricula. Subjects include data collection, collaboration, software engineering, DevOps, teaching,...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next