60-Days-of-Data-Science-and-ML
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Jupyter Notebook | Python | |
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60-Days-of-Data-Science-and-ML
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60 Days of Data Science and Machine Learning
Day 31 - Machine Learning Linear Regression
Followings are fourth week of this series. You can find them on my GitHub. You can run all the notebook on colab or jupyter notebook as well.
Day 15 - Repression Part2
Day 1 - Python Basics Part1
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
data-science-notes - Notes of IBM Data Science Professional Certificate Courses on Coursera
glue - Linked Data Visualizations Across Multiple Files
MAPIE - A scikit-learn-compatible module for estimating prediction intervals.
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
eip1559_analysis - Can we estimate the economic impact of EIP-1559 on miners? This repository try to estimate the loss of miners' revenue coming from transactions fees, using Ethereum historical data.
RDKit - The official sources for the RDKit library
collatz-conjecture - A calculator as Jupyter Lab notebook for Collatz Conjecture or commonly known as 3x+1 problem.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
hamilton - A scalable general purpose micro-framework for defining dataflows. THIS REPOSITORY HAS BEEN MOVED TO www.github.com/dagworks-inc/hamilton
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
linear-regression-from-scratch - A data science project for part II physics project E (surveying using stars)
NumPy - The fundamental package for scientific computing with Python.