Data Science Jupyter Notebooks
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Explore the world of Data Science through Jupyter Notebooksβ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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πŸ”₯ Trending Repository: datascience

πŸ“ Description: This repository is a compilation of free resources for learning Data Science.

πŸ”— Repository URL: https://github.com/geekywrites/datascience

🌐 Website: https://twitter.com/geekywrites

πŸ“– Readme: https://github.com/geekywrites/datascience#readme

πŸ“Š Statistics:
🌟 Stars: 5.1K stars
πŸ‘€ Watchers: 381
🍴 Forks: 529 forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#data_science #machine_learning #natural_language_processing #computer_vision #machine_learning_algorithms #artificial_intelligence #neural_networks #deeplearning #datascienceproject


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🧠 By: https://t.iss.one/DataScienceN
πŸ”₯ Trending Repository: Data-Science-Interview-Resources

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

πŸ”— Repository URL: https://github.com/rbhatia46/Data-Science-Interview-Resources

πŸ“– Readme: https://github.com/rbhatia46/Data-Science-Interview-Resources#readme

πŸ“Š Statistics:
🌟 Stars: 3.1K stars
πŸ‘€ Watchers: 50
🍴 Forks: 719 forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#data_science #machine_learning #artificial_intelligence #interview_questions #interview_resources #learning_resources #machine_learning_interview #data_science_interview


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🧠 By: https://t.iss.one/DataScienceN
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πŸ”₯ Trending Repository: awesome-machine-learning-interpretability

πŸ“ Description: A curated list of awesome responsible machine learning resources.

πŸ”— Repository URL: https://github.com/jphall663/awesome-machine-learning-interpretability

πŸ“– Readme: https://github.com/jphall663/awesome-machine-learning-interpretability#readme

πŸ“Š Statistics:
🌟 Stars: 3.8K stars
πŸ‘€ Watchers: 132
🍴 Forks: 608 forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#python #data_science #machine_learning #awesome #r #awesome_list #transparency #fairness #ai_safety #privacy_enhancing_technologies #interpretability #interpretable_ai #interpretable_ml #explainable_ml #xai #interpretable_machine_learning #privacy_preserving_machine_learning #machine_learning_interpretability #secure_ml #reliable_ai


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🧠 By: https://t.iss.one/DataScienceN
πŸ”₯ Trending Repository: data-science-career

πŸ“ Description: Career Resources for Data Science, Machine Learning, Big Data and Business Analytics Career Repository

πŸ”— Repository URL: https://github.com/firmai/data-science-career

πŸ“– Readme: https://github.com/firmai/data-science-career#readme

πŸ“Š Statistics:
🌟 Stars: 975 stars
πŸ‘€ Watchers: 33
🍴 Forks: 150 forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#data_science #machine_learning #big_data #analytics #resources #career #business_intelligence #business_analytics


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🧠 By: https://t.iss.one/DataScienceN
πŸ”₯ Trending Repository: FreeML

πŸ“ Description: A List of Data Science/Machine Learning Resources (Mostly Free)

πŸ”— Repository URL: https://github.com/Shujian2015/FreeML

πŸ“– Readme: https://github.com/Shujian2015/FreeML#readme

πŸ“Š Statistics:
🌟 Stars: 1.1K stars
πŸ‘€ Watchers: 118
🍴 Forks: 517 forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#data_science #machine_learning #natural_language_processing #deep_learning


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🧠 By: https://t.iss.one/DataScienceN
πŸ”₯ Trending Repository: ML-From-Scratch

πŸ“ Description: Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

πŸ”— Repository URL: https://github.com/eriklindernoren/ML-From-Scratch

πŸ“– Readme: https://github.com/eriklindernoren/ML-From-Scratch#readme

πŸ“Š Statistics:
🌟 Stars: 27.8K stars
πŸ‘€ Watchers: 951
🍴 Forks: 4.8K forks

πŸ’» Programming Languages: Python

🏷️ Related Topics:
#data_science #machine_learning #data_mining #deep_learning #genetic_algorithm #deep_reinforcement_learning #machine_learning_from_scratch


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🧠 By: https://t.iss.one/DataScienceM
πŸ”₯ Trending Repository: airflow

πŸ“ Description: Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

πŸ”— Repository URL: https://github.com/apache/airflow

🌐 Website: https://airflow.apache.org/

πŸ“– Readme: https://github.com/apache/airflow#readme

πŸ“Š Statistics:
🌟 Stars: 41.9K stars
πŸ‘€ Watchers: 764
🍴 Forks: 15.5K forks

πŸ’» Programming Languages: Python - TypeScript - JavaScript - Shell - Dockerfile - Jinja

🏷️ Related Topics:
#python #workflow #data_science #machine_learning #airflow #automation #etl #workflow_engine #scheduler #apache #orchestration #data_engineering #data_integration #elt #data_pipelines #dag #apache_airflow #mlops #workflow_orchestration #data_orchestrator


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🧠 By: https://t.iss.one/DataScienceM
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πŸ”₯ Trending Repository: RD-Agent

πŸ“ Description: Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. πŸ”—https://aka.ms/RD-Agent-Tech-Report

πŸ”— Repository URL: https://github.com/microsoft/RD-Agent

🌐 Website: https://rdagent.azurewebsites.net/

πŸ“– Readme: https://github.com/microsoft/RD-Agent#readme

πŸ“Š Statistics:
🌟 Stars: 8.2K stars
πŸ‘€ Watchers: 59
🍴 Forks: 872 forks

πŸ’» Programming Languages: Python - Jupyter Notebook

🏷️ Related Topics:
#agent #data_science #development #data_mining #automation #research #ai #llm


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🧠 By: https://t.iss.one/DataScienceM
πŸ”₯ Trending Repository: best-of-ml-python

πŸ“ Description: πŸ† A ranked list of awesome machine learning Python libraries. Updated weekly.

πŸ”— Repository URL: https://github.com/lukasmasuch/best-of-ml-python

🌐 Website: https://ml-python.best-of.org

πŸ“– Readme: https://github.com/lukasmasuch/best-of-ml-python#readme

πŸ“Š Statistics:
🌟 Stars: 22.3K stars
πŸ‘€ Watchers: 444
🍴 Forks: 3K forks

πŸ’» Programming Languages: Not available

🏷️ Related Topics:
#python #nlp #data_science #machine_learning #deep_learning #tensorflow #scikit_learn #keras #ml #data_visualization #pytorch #transformer #data_analysis #gpt #automl #jax #data_visualizations #gpt_3 #chatgpt


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🧠 By: https://t.iss.one/DataScienceM
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πŸ’£ China's Alibaba company has released a new competitor for Cursor and Windsurf!

πŸ‘¨πŸ»β€πŸ’» Its name is Qoder, an AI IDE that thinks, plans, writes code, and executes it by itself so you can build software more easily.

✏️ Its interface is also very similar to Cursor; internal chat, code autocomplete, Agent Workflow, and support for MCP.

⬅️ What is Qoder's main focus? Context Engineering, and it is entirely built on that; meaning:

βœ… It deeply understands the project, structure, and codebase.

βœ… It builds persistent memory from past interactions.

βœ… It assigns tasks to the best possible AI model by itself.

⬅️ Two impressive features that really stand out:

1⃣ Quest Mode
⬅️ You just write and hand over the project specifications or the task you want, then go on with your other work, and later you receive the results. That means asynchronous coding without you having to oversee it.

2⃣ Repo Wiki
⬅️ It automatically generates documentation, architectural explanations, and project structure for the entire project.

β”Œ πŸ₯΅ Qoder
β”œ 🌎 Website
β””
πŸ“„ Documentation

🌐 #Data_Science #DataScience
βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–

https://t.iss.one/DataScienceN
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πŸ”° New version of Colab; Data Scientists' Partner
πŸ”ƒ Major update for Google Colab!

πŸ‘¨πŸ»β€πŸ’» From now on, you have a real AI assistant inside your notebook, not just a code completion tool!

▢️ This AI assistant is directly integrated inside Colab and Colab Enterprise (within Vertex AI and BigQuery), and it basically acts like a teammate and coding partner.


πŸ€– What exactly does this AI assistant do for you?

1⃣ It handles the entire workflow by itself!
🏷 You just need to tell it the goal, for example: "Build a model that predicts people's income level based on a BigQuery table." Then it plans a multi-step program, cleans the data, creates features, builds the model, and trains it.

πŸ”’ It writes code for every task!
🏷For example: it can create a chart, manage the cloud environment, perform causal analysis. You just have to ask.

πŸ”’ It finds and fixes errors!
🏷 If a cell throws an error, it explains the cause, provides a corrected version of the code, and even shows a diff so you can approve it.

βž– βž– βž–

🎯 What is its goal?

βœ… Professionals work much faster.

βœ… Beginners learn more easily.

βœ… The entire data science process from idea to final model becomes faster, cleaner, and less error-prone.

➑️ AI First Colab Notebooks
➑️ AI First Colab Notebooks

🌐 #Data_Science #DataScience
βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–
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