Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
MLJAR
Teachable Machine
Amazon Machine Learning
Google Cloud Machine Learning
GPUClub.com
OpenCV
Logical Glue
Spell
Pandas| Categories |
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|---|---|
| Website | pandas.pydata.org |
| Pricing URL | - |
| Details $ |
MLJAR| Categories |
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| Website | mljar.com |
| Pricing URL | Official MLJAR Pricing |
| Details $ |
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Based on our record, Pandas seems to be a lot more popular than MLJAR. While we know about 196 links to Pandas, we've tracked only 3 mentions of MLJAR. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / about 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 4 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 4 months ago
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential. - Source: dev.to / 4 months ago
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:. Source: 4 months ago
Sure, at the bottom of our website you can subscribe for newsletter. Source: about 1 year ago
In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools. Source: about 2 years ago
Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ). Source: over 2 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Teachable Machine - Easily create machine learning models for your apps, no coding required.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
OpenCV - OpenCV is the world's biggest computer vision library
Google Cloud Machine Learning - Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
