Based on our record, Scikit-learn should be more popular than CodeClimate. 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.
Use tools like SonarQube or CodeClimate to spot the high-risk 20%. Then fix one thing at a time not everything at once. This isn’t Dark Souls. - Source: dev.to / 23 days ago
Vishal Shah, Sr. Technical Consultant at WPWeb Infotech, emphasizes this approach, stating, “The first step is to identify the bug by replicating the issue. Understanding the exact conditions that trigger the problem is crucial.” Shah’s workflow includes rigorous testing—unit, integration, and regression tests—followed by peer reviews and staging deployments. Data from GitLab’s 2024 DevSecOps Report supports this,... - Source: dev.to / about 1 month ago
- code climate It’s like Sonarqube but doesn’t offer detailed reports and doesn’t support all languages, you can see it from here Https://codeclimate.com/. - Source: dev.to / 9 months ago
For open-source projects, many SaaS platforms offer free tiers for monitoring. For tracking code coverage, you can use Codecov or Coveralls. For tracking complexity, CodeClimate is a good option. These platforms integrate well with GitHub repositories. - Source: dev.to / 10 months ago
Codeclimate.com — Automated code review, free for Open Source and unlimited organisation-owned private repos (up to 4 collaborators). Also free for students and institutions. - Source: dev.to / over 2 years 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 / 4 months ago
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 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 / 12 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 / over 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
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Codacy - Automatically reviews code style, security, duplication, complexity, and coverage on every change while tracking code quality throughout your sprints.
OpenCV - OpenCV is the world's biggest computer vision library
ESLint - The fully pluggable JavaScript code quality tool
NumPy - NumPy is the fundamental package for scientific computing with Python