Search Results for "regression"
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54 projects for "regression" with 2 filters applied:
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TabPFN
Foundation Model for Tabular Data
...The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. This allows it to generate predictions extremely quickly, often within seconds, while maintaining competitive accuracy on small and medium-sized datasets. ... -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...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. -
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Coursera-ML-AndrewNg-Notes
Personal notes from Wu Enda's machine learning course
...The project aims to help students understand the mathematical concepts, algorithms, and intuition behind fundamental machine learning techniques taught in the course. It organizes the material into clear written summaries that accompany each lecture topic, including supervised learning, regression methods, neural networks, and optimization algorithms. The repository often expands on the original lecture material by adding additional explanations, diagrams, and formulas that clarify the theoretical foundations of the algorithms. These notes serve as a structured reference that learners can review while studying or revisiting machine learning fundamentals. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
...Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. These materials allow learners to see how algorithms behave during training and how different parameters affect model performance. -
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TabFM
scikit-learn compatible tabular foundation model
TabFM is a tabular foundation model from Google Research for zero-shot classification and regression on structured datasets. It is designed to work with mixed numerical and categorical columns without requiring a custom training run for every new table. Instead of fitting model weights to the user’s dataset, TabFM uses in-context learning by reading training examples and test rows together at inference time. The library provides scikit-learn-compatible classifier and regressor interfaces, which makes it familiar for data scientists already using Python ML workflows. ... -
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PySINDy
A package for the sparse identification of nonlinear dynamical systems
...This approach is particularly valuable in scientific fields such as physics, engineering, and biology where researchers seek both predictive accuracy and theoretical insight. The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. It integrates with standard Python scientific computing libraries, making it easy to apply to experimental datasets or simulated systems. -
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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
...The project is designed to guide learners through the complete lifecycle of developing machine learning systems, starting with data preparation and model training and ending with production deployment. Participants learn how to build regression and classification models using Python libraries such as NumPy, Pandas, and Scikit-learn. The course also introduces more advanced topics including decision trees, ensemble methods, and neural networks. Later modules focus on practical engineering topics such as containerization with Docker, API development with FastAPI, and scaling machine learning services using Kubernetes and cloud platforms. ... -
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Agentless
An agentless approach to automatically solve software development
...It then generates multiple candidate patches for the identified locations using language model reasoning and diff-style edits. In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit. -
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FlowLens MCP
Open-source MCP server that gives your coding agent
FlowLens MCP Server is an open-source tool designed to give AI-powered coding agents (like Claude Code, Cursor, GitHub Copilot / Codex, and others) full, replayable browser context to dramatically improve debugging, bug reporting, and regression testing for web applications. It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console logs, DOM events, storage changes, and more, and exports it. The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots). -
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AG KIT
Antigravity-first agent engineering kit with rules, skills
...A native pre-tool safety gate blocks high-confidence destructive commands without interfering with normal project cleanup. Merge-aware updates preserve local changes, create backups, and support rollback. Built-in validation, doctor checks, regression tests, and reproducible manifests help teams maintain reliable agent configurations. -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
...It aims to strike a balance between theoretical explanation and practical coding by demonstrating algorithms both from scratch and using established libraries. The content is organized into multiple sections covering topics such as clustering, regression, dimensionality reduction, recommender systems, and model evaluation. -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
...It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows. -
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AutoTrain Advanced
Faster and easier training and deployments
AutoTrain Advanced is an open-source machine learning training framework developed by Hugging Face that simplifies the process of training and fine-tuning state-of-the-art AI models. The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence... -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets. -
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stkpp
C++ Statistical ToolKit
STK++ (http://www.stkpp.org) is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions (http://cran.at.r-project.org/web/packages/rtkore/index.html). At a convenience, we propose the source packages on sourceforge. The library offers a dense set of (mostly) template classes in C++ and is suitable for projects ranging from small one-off projects to complete data mining application suites. -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
...It provides the Jupyter notebooks used in each lesson so learners can reproduce the demonstrations and experiment with the code themselves. The series introduces fundamental machine learning concepts such as classification, regression, model evaluation, feature engineering, and cross-validation using clear examples and real datasets. Each video corresponds to a notebook that walks through the code step by step, allowing students to see both the theoretical explanation and its practical implementation. The project emphasizes accessibility and beginner-friendly explanations, making it suitable for learners who are new to data science or machine learning programming. ... -
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pattern_classification
A collection of tutorials and examples for solving machine learning
...It includes notebooks and guides that demonstrate data preprocessing, feature extraction, model training, and evaluation techniques used in machine learning workflows. The repository also covers algorithms such as Bayesian classification, logistic regression, neural networks, clustering methods, and ensemble models. In addition to algorithm tutorials, the project contains supplementary resources such as dataset collections, visualization examples, and links to recommended books and talks. These materials are designed to support both theoretical understanding and practical experimentation with machine learning tools. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Point-E
Point cloud diffusion for 3D model synthesis
...While it does not match the fine detail of some slower methods, the tradeoff in speed makes it practical for prototyping and interactive 3D generation. The repository includes inference scripts, utilities for converting point clouds to meshes (e.g. via signed distance function regression), sample notebooks, and weight checkpoints. It also provides documentation on limitations, usage instructions, and example outputs. -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
...It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes demo apps for live webcam capture as well as batch processing. Outputs include textured meshes, joint locations, and model parameters that can be exported to common DCC tools and game engines. The codebase offers pretrained models, clear inference scripts, and utilities to visualize results, making single-camera motion capture approachable on commodity hardware. ... -
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MTCNN Face Detection Alignment
Joint Face Detection and Alignment
...The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage. The repository includes Caffe / MATLAB code, support scripts, and instructions for dependencies. Non-maximum suppression and bounding box regression at each stage. Online hard sample mining to improve training robustness. -
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learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
...The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow. It progressively moves from foundational theory to more advanced subjects including regression, classification, neural networks, and model deployment. The repository emphasizes understanding the underlying principles of machine learning while also providing practical exercises and examples that allow learners to build and experiment with real models. Many sections include notebooks and code examples that demonstrate how algorithms are implemented and trained using modern machine learning frameworks. -
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Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
Python ML Jupyter Notebooks is an educational repository that demonstrates how to implement machine learning algorithms and data science workflows using Python. 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. ...
