Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
htm.java
Exploratory
WEKA
MLJAR
Google Cloud Machine Learning
NumPy
Teachable Machine
Amazon Machine Learning
OpenCV
Deep Learning Gallery
Apache PredictionIO
Pandas MLJARBased on our record, Pandas seems to be a lot more popular than MLJAR. While we know about 219 links to Pandas, we've tracked only 4 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.
Libraries for data science and deep learning that are always changing. - Source: dev.to / 3 days ago
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 19 days ago
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 22 days ago
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 8 months ago
I'm working on visual programming for Python. I created an Python editor, that is notebook based (similar to Jupyter) but each cell code in the notebook has graphical user interface. In this GUI you can select your code recipe, a simple code step, for example here is a recipe to list files in the directory https://mljar.com/docs/python-list-files-in-directory/ - you fill the UI and the code is generated. You can... - Source: Hacker News / 10 months ago
Sure, at the bottom of our website you can subscribe for newsletter. Source: about 2 years 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 3 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 3 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
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.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
OpenCV - OpenCV is the world's biggest computer vision library
Teachable Machine - Easily create machine learning models for your apps, no coding required.
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.