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Scikit-learn Alternatives
Similar projects and alternatives to scikit-learn
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Revelo Payroll
Free Global Payroll designed for tech teams. Building a great tech team takes more than a paycheck. Zero payroll costs, get AI-driven insights to retain best talent, and delight them with amazing local benefits. 100% free and compliant.
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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.
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Scrapy
Scrapy, a fast high-level web crawling & scraping framework for Python.
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PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
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seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
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awesome-courses
:books: List of awesome university courses for learning Computer Science!
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better scikit-learn alternative or higher similarity.
scikit-learn reviews and mentions
Posts with mentions or reviews of scikit-learn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-30.
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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...
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A note from our sponsor - InfluxDB
www.influxdata.com | 1 Oct 2023
Stats
Basic scikit-learn repo stats
75
55,985
9.8
1 day ago
scikit-learn/scikit-learn is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of scikit-learn is Python.
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