scikit-learn
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| scikit-learn | PyBrain | |
|---|---|---|
| 75 | 0 | |
| 55,832 | 2,839 | |
| 1.1% | - | |
| 9.8 | 0.0 | |
| 5 days ago | 7 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" 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.
scikit-learn
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
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Link Prediction With node2vec in Physics Collaboration Network
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.
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List of AI-Models
Click to Learn more...
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PSA: You don't need fancy stuff to do good work.
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
1. Scikit-learn Scikit-learn is a must-know Python library for any data scientist. It offers a wide range of machine learning algorithms, data preprocessing tools, and model evaluation metrics that are easy to use and highly efficient. Whether you’re working on regression, classification, or clustering tasks, Scikit-learn has got you covered.
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
There's an issue here about that https://github.com/scikit-learn/scikit-learn/discussions/25450
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning theory: types of machine learning problems like regression and classification, the concept and process of Supervised machine learning (fit/predict and evaluate quality using metrics) and common models used for it, including Random Forest Classifier, and it's implementation in SciKit-Learn Python library. Additionally, it would be great if you previously participated in Kaggle competitions, because to understand and run all code of this article you need to have an account on https://kaggle.com.
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Best Websites For Coders
Scikit-learn : A Python module for machine learning build on top of SciPy
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scikit-learn VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
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Boston Dataset was Removed from scikit-learn 1.2
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
PyBrain
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What are some alternatives?
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Keras - Deep Learning for humans
Surprise - A Python scikit for building and analyzing recommender systems
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
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
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
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.