Search Results for "matplotlib"
Sort By:
Showing 9 open source projects for "matplotlib"
View related business solutions-
IT Asset Management (ITAM) SoftwareDrowning in misplaced IT assets, compliance headaches, and shadow IT? Navigate to clarity with an intuitive IT Asset Management solution. Experience crisp visibility, effortless control, and unshakable security – all while freeing up your budget with optimized software licenses. The best part? It’s easy.
-
Cloudbrink Personal SASE serviceCloudbrink’s Personal SASE is a high-performance connectivity and security service that delivers a lightning-fast, in-office experience to the modern hybrid workforce anywhere. Combining high-performance ZTNA with Automated Moving Target Defense (AMTD), and Personal SD-WAN all connections are ultra-secure.
-
1
scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
2
Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
3
AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. ... -
4
pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
Software for managing apps and accounts | WebCatalogTurn websites into desktop apps with WebCatalog Desktop—your all-in-one tool to manage apps and accounts. Switch between multiple accounts, organize apps by workflow, and access a curated catalog of desktop apps for Mac and Windows.
-
5
Homemade Machine Learning
Python examples of popular machine learning algorithms
...The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
6
Yellowbrick
Visual analysis and diagnostic tools to facilitate ML selection
Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib. Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow. -
7
Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
...The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. It includes code implementations that show how to build models using popular libraries like scikit-learn, NumPy, pandas, and Matplotlib. The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. Many notebooks include explanations of algorithm behavior, data preparation techniques, and evaluation methods for machine learning models. The project also includes examples that demonstrate how to apply machine learning to real-world datasets and practical business problems. -
8
pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Python 3 code to reproduce the figures in the books Probabilistic Machine Learning: An Introduction (aka "book 1") and Probabilistic Machine Learning: Advanced Topics (aka "book 2"). The code uses the standard Python libraries, such as numpy, scipy, matplotlib, sklearn, etc. Some of the code (especially in book 2) also uses JAX, and in some parts of book 1, we also use Tensorflow 2 and a little bit of Torch. See also probml-utils for some utility code that is shared across multiple notebooks. -
9
AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning research
...The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. These materials summarize common functions, workflows, and best practices in a concise visual format that makes them easy to consult during development or study sessions. The repository functions as a centralized library where users can quickly access reference materials for both machine learning theory and practical programming tools. ... -
Save up to 90% off rates for USPS, UPS, DHL Express, and more with the best multi-carrier shipping software for e-commerce businesses.Whether you're established or just getting started, Shippo is the best shipping software for growing e-commerce brands that need to save time and money, fulfill and ship at scale, and delight customers. Create shipping labels for all carriers & save money with discounted rates. See all your online sales channels in one place and automatically access discounted USPS and DHL Express rates, or use your own carrier accounts. Sign up is free and there are no monthly fees or cancellation fees.
- Previous
- You're on page 1
- Next
