Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
htm.java
Exploratory
nowPredict.ai
Obviously.ai
Dataiku
Alteryx Designer
datarobot
Akkio
Zarla
nowPredict.ai empowers users to rapidly train, analyze, and explain machine learning models (regression and classification) without coding. With just a few guided clicks, users can go from raw data to a fully optimized model, complete with performance insights and explainability features, making ML accessible to both beginners and experts.
Scikit-learn nowPredict.ainowPredict.ai's answer:
nowPredict.ai is built using a robust stack of modern technologies to ensure performance, scalability, and security. The platform leverages Python and the latest machine learning libraries for cutting-edge model development and optimization. It is powered by a scalable cloud architecture, allowing seamless processing of large datasets and multi-user operations. To prioritize data privacy and integrity, customer data is stored in separatable tables, ensuring strict data isolation. This combination of technologies delivers a high-performance, secure, and flexible environment for no-code AI solutions.
nowPredict.ai's answer:
nowPredict.ai's answer:
nowPredict.ai stands out by delivering AI solutions without the need for coding, making it accessible to users of all skill levels. Its guided, intuitive platform enables rapid model creation, analysis, and explainability, while offering flexibility through predefined use cases or customizable workflows to meet diverse business needs.
nowPredict.ai's answer:
nowPredict.ai empowers analysts, scientists, and executives to harness AI effortlessly. With intuitive workflows, automated model optimization, and explainability tools, the platform bridges the gap between data complexity and actionable results, enabling smarter, faster decisions across industries.
nowPredict.ai's answer:
The story behind nowPredict.ai began with a data scientist who noticed recurring challenges in his work: repeatedly implementing the same use cases, handling non-standardized data preprocessing, and struggling with inconsistent model quality due to varying workflows. Additionally, he found the process of transitioning proof-of-concept models into production to be lengthy and tedious. These experiences inspired the creation of nowPredict.ai, a platform designed to streamline and standardize machine learning workflows, making AI development faster, more accessible, and easier to operationalize.
nowPredict.ai's answer:
Due to nowpredict.ai being in closed access, we cannot disclose customer base at this point.
Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 times since March 2021. 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.
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
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
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... - Source: dev.to / about 1 year ago
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. - Source: dev.to / almost 2 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Obviously.ai - The entire process of running Data Science - building Machine Learning algorithm, explaining results and predicting outcomes, packed in one single click.
OpenCV - OpenCV is the world's biggest computer vision library
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.
NumPy - NumPy is the fundamental package for scientific computing with Python
Alteryx Designer - Alteryx Designer is one of the foremost solutions for data prep, amalgamation, and analytics that comes with drag-and-drop competencies.
