The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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Apache Superset is a Data Visualization and Data Exploration Platform
scikit-learn: machine learning in Python
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Streamlit — A faster way to build and share data apps.
💫 Industrial-strength Natural Language Processing (NLP) in Python
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.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Data Apps & Dashboards for Python. No JavaScript Required.
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
matplotlib: plotting with Python
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Best Practices on Recommendation Systems
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
Topic Modelling for Humans
🦉 Data Versioning and ML Experiments
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. 🔗https://aka.ms/RD-Agent-Tech-Report
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Rich MySQL Terminal Client with AutoCompletion, Syntax Highlighting, and Dataframes
Always know what to expect from your data.