MindsDB
scikit-learn
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MindsDB | scikit-learn | |
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16 | 37 | |
6,901 | 50,214 | |
9.5% | 1.5% | |
9.9 | 9.9 | |
7 days ago | about 14 hours ago | |
Python | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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MindsDB
- Machine Learning that is built-in SQL and databases
- Machine Learning via SQL
- Awesome GitHub project - MindsDB: In-Database Machine Learning
- GitHub/MindsDB: In-Database Machine Learning
- Show HN: PostgresML, now with analytics and project management
- In-Database Machine Learning
- MindsDB ML-SQL Server enables machine learning workflows using SQL
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[D] AutoML vs AI Tables. Is it a new rival for Self-Service Machine Learning?
I represent a community-driven open source project called MindsDB (see on GitHub). We need your feedback about our concept for doing machine learning using SQL! It is called AI Tables and aims to democratize machine learning for all who work with data. There's an article on Medium with SQL commands examples.
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Self-Service Machine Learning with Intelligent Databases
AI Tables is a part of GitHub project by MindsDB and are available as open-source or as a managed cloud service. They integrate with traditional SQL and NoSQL databases and data streams like Kafka & Redis.
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MindsDB: Creating machine learning predictive models using SQL.
Want to try it out for yourself? Sign up for a free MindsDB account and join our community! Engage with MindsDB community on Slack or Github to ask questions, share and express ideas and thoughts!
scikit-learn
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Will it benefit me having a portfolio alongside my cv?
I say 'usually' because it depends on what you're referring to as 'coding'. From what you're describing, it seems that you want to be able to take data, clean it up and perform a whole bunch of analysis/inferences on it. In that case, I think the coding skill there would be stuff that allows you to do data manipulation and data clean up (knowledge of R, knowing Python as it pertains to data stuff e.g. scikit learning). Knowing how to build an App would not necessarily be a selling skill
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[D] Looking for a python library that implements decision tree regressors handling categorical features
Perhaps this would be of interest to you: NOCATS
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What to do with some data?
Are you using scikit-learn for your training? If so, you may try running the models on one another. If you're using custom kernels, you may want to use a different set of them for the test set.
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Desmistificando roteirizações com Python
Scikit-learn
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Inside the hood of SciKit-Learn library??? How do I get original codes? what is the magic search word?
In short, you can read through the code, it's open-source. For instance, you can find LogisticRegression here.
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Python for everyone :Mastering Python The Right Way
http://scikit-learn.org/ - Machine learning with Python https://www.tensorflow.org/ - Deep learning with Python https://www.djangoproject.com/ - https://www.python.org/dev/peps/pep-0008
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Roadmap to self-learning AI
My only gripe is that the Labs are in R and not Python, but honestly the [scikit-learn](https://scikit-learn.org/) user guide & docs have been straightforward enough to apply the same knowledge in Python for me with some trial and error.
- scikit-learn test case results?
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How do you reduce information leakage and bias when going from descriptive analytics to prescriptive analytics?
I'd say, the first question you'd need to ask yourself is "Why do I want to do statistical tests" and "what kind of statistical tests do I want to do?". Most of them rely on a bunch of assumptions and just winging it will produce a number that will be reported and used but is terribly wrong. Funnily enough, scikit-learn does not directly give you p-values for this very reason and advise you to run the same regression in statsmodels.
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Learning python, what next?
Machine learning and statistical analysis? http://scikit-learn.org
What are some alternatives?
Keras - Deep Learning for humans
Surprise - A Python scikit for building and analyzing recommender systems
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
PyBrain
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
MLflow - Open source platform for the machine learning lifecycle
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow