datascience
kaggle-solutions
| datascience | kaggle-solutions | |
|---|---|---|
| 4 | 8 | |
| 4,071 | 3,753 | |
| - | - | |
| 8.3 | 6.4 | |
| 22 days ago | 28 days ago | |
| HTML | ||
| Creative Commons Zero v1.0 Universal | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
datascience
- Datasciene Libraries for Python
- Datascience Libraries for Python
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Good resources for learning ML with time series in Python? Some links I've found, but looking for canonical resources.
This GitHub repo maintains a good list of resources. Check out the "Time Series" section. https://github.com/r0f1/datascience
- Opinionated List of Data Science Libraries for Python
kaggle-solutions
What are some alternatives?
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
data-science-ipython-notebooks - 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.
mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
data-science-interviews - Data science interview questions and answers
machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.
jube - Jube is an open-source software designed for monitoring transactions and events. It offers a range of powerful features including real-time data wrangling, artificial intelligence, decision making, and case management. Jube's exceptional performance is particularly evident in its application to fraud prevention and abuse detection scenarios.
awesome-bigdata - A curated list of awesome big data frameworks, ressources and other awesomeness.
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
mlreef - The collaboration workspace for Machine Learning
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.