awesome-datascience
scikit-learn
awesome-datascience | scikit-learn | |
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9 | 82 | |
23,777 | 58,200 | |
3.7% | 0.6% | |
6.9 | 9.9 | |
9 days ago | 2 days ago | |
Python | ||
MIT 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.
awesome-datascience
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About Data analyst, data scientist and data engineer, resources and experiences
Awesome Data Science by Academic
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Good coding groups for black women?
- https://github.com/academic/awesome-datascience
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
9. Awesome Data Science If you’re on the hunt for data science resources, Awesome Data Science is a goldmine. This curated list includes MOOCs, books, courses, blogs, podcasts, software, and more, all related to data science.
- Does anyone know of comprehensive refresher material for a once Senior Data Scientist?
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
Awesome Data Science – The awesome lists repositories often provides a good collection of resources around a specific topic, and the awesome-datascience repository is no exception. It contains a very comprehensive list of books, moocs, tutorials, and other content for all learnes of all levels of experience.
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High income skills?
There are several on github, such as: https://github.com/academic/awesome-datascience
- ⚙️ Awesome Data Science: An #OpenSource #DataScience repository to learn and apply towards solving real world problems. h/t @Sauain
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Top GitHub repositories to learn Data Science
Awesome Data Science
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[IWantOut] 21f Peru student -> Canada/UK
If you want to expand your skills and knowledge in data science, there's a ton of free online resources out there. For example, this page is a good place to get started. There's lots of communities like /r/learndatascience or similar subs if you get stuck on something.
scikit-learn
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How to Build a Logistic Regression Model: A Spam-filter Tutorial
Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!
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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
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Polars
sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
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[D] Major bug in Scikit-Learn's implementation of F-1 score
Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
- Contraction Clustering (RASTER): A fast clustering algorithm
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Ask HN: Learning new coding patterns – how to start?
I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.
I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.
You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.
As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?
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Transformers as Support Vector Machines
It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says
Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.
[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...
[1] https://en.wikipedia.org/wiki/Platt_scaling
[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
- Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
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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.
What are some alternatives?
Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
gdelt
Surprise - A Python scikit for building and analyzing recommender systems
vagas-junior-estagio - Empresas que constantemente oferecem vagas para junior e estagiários sem exigir experiência prévia
Keras - Deep Learning for humans
DataScienceResources - Open Source Data Science Resources.
tensorflow - An Open Source Machine Learning Framework for Everyone
data-science-blogs - A curated list of data science blogs
gensim - Topic Modelling for Humans
ScribeSalad - A collection of YouTube videos transcripts : Podcasts (Joe Rogan Experience, Tim Ferris, Jocko podcast, ..), lectures (YaleCourses, MIT lectures, Jordan B. Peterson talks, ..). A big transcripts salad spanning history, geography, science, politics, film making and more.
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.