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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scikit-learn: machine learning in Python
Deep Learning for humans
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
- airflow
- apache
- apache-airflow
- python
Streamlit — A faster way to build and share data apps.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
- machine-learning
- models
- ui
- ui-components
💫 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.
- machine-learning
- deep-learning
- deep-reinforcement-learning
- machine-learning-from-scratch
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.
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
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.
- notebooks
- python
- data-science
- machine-learning
Best Practices on Recommendation Systems
- machine-learning
- recommender
- ranking
- deep-learning
Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
🦉 Data Versioning and ML Experiments
- data-science
- machine-learning
- reproducibility
- data-version-control
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
- agent
- ai
- automation
- data-mining
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
- pandas-profiling
- pandas-dataframe
- statistics
- jupyter-notebook
Always know what to expect from your data.
- pipeline-tests
- dataquality
- datacleaning
- datacleaner
Low-code framework for building custom LLMs, neural networks, and other AI models
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
- weak-supervision
- data-cleaning
- data-quality
- noisy-labels
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
- machine-learning
- experiment-track
- deep-learning
- keras