Search Results for "decision"
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22 projects for "decision" with 2 filters applied:
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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
...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. The repository includes lecture materials, assignments, and projects that allow learners to gain hands-on experience implementing machine learning pipelines. -
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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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Finance
150+ quantitative finance Python programs
...The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical frameworks used in financial decision-making. It organizes concepts into structured documents that explain both theoretical principles and practical calculations used in finance. The materials often include definitions, formulas, conceptual explanations, and examples to help readers understand how financial models and instruments function in real markets. -
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AI-Job-Notes
AI algorithm position job search strategy
...The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. It’s designed to reduce uncertainty and decision fatigue during the often lengthy job-hunt cycle. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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hls4ml
Machine learning on FPGAs using HLS
...This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. The framework was originally developed for high-energy physics experiments where real-time decision systems must process large volumes of data with strict latency constraints. Over time, it has expanded to support a variety of scientific and industrial applications including signal processing, embedded systems, and biomedical monitoring. -
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Data Science Interviews
Data science interview questions and answers
...The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
pytorch-grad-cam is an open-source library that provides advanced explainable AI techniques for interpreting the predictions of deep learning models used in computer vision. The project implements Grad-CAM and several related visualization methods that highlight the regions of an image that most strongly influence a neural network’s decision. These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. The library supports a wide variety of tasks including image classification, object detection, semantic segmentation, and similarity analysis. It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. ... -
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GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Machine Learning Git Codebook
For extensive instructor led learning
...The project is designed as a self-paced learning resource that walks learners through the full data science workflow, including data preprocessing, exploratory analysis, feature engineering, and model development. It covers a wide range of machine learning techniques such as decision trees, clustering methods, nearest neighbor algorithms, anomaly detection, and probabilistic classifiers. The repository organizes these topics into sequential notebooks that explain theoretical concepts while allowing users to experiment directly with code. Many lessons emphasize hands-on exercises where learners analyze datasets, implement algorithms, and evaluate results through visualizations and statistical metrics. -
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MuZero General
A commented and documented implementation of MuZero
muzero-general is an open-source implementation of the MuZero reinforcement learning algorithm introduced by DeepMind. MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. It allows researchers and developers to train reinforcement learning agents that can learn to play games such as Atari environments or board games. ... -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying solely on black-box frameworks. ... -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
...The project focuses on deriving formulas and explaining algorithms step by step so that learners can understand the mathematical foundations behind machine learning methods. The notes span sixteen chapters that cover a wide range of topics, including model evaluation, linear models, decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimensionality reduction, and reinforcement learning. Each section explains the theoretical principles of the algorithms and walks through derivations to help readers understand why the methods work rather than simply how to use them. ... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
...The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. Because the code avoids external machine learning libraries, it exposes the full logic behind model training, optimization, and prediction processes. The project also provides examples and explanations that illustrate how the algorithms behave and how different components interact during training. -
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ELI5
A library for debugging/inspecting machine learning classifiers
...It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. Additionally, the library integrates explanation algorithms such as LIME to interpret predictions from arbitrary machine learning models. -
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DeepTraffic
DeepTraffic is a deep reinforcement learning competition
DeepTraffic is a deep reinforcement learning simulation designed to teach and evaluate autonomous driving algorithms in a dense highway environment. The system presents a simulated multi-lane highway where an AI-controlled vehicle must navigate traffic while maximizing speed and avoiding collisions. Participants design neural network policies that determine the vehicle’s actions, such as accelerating, decelerating, changing lanes, or maintaining speed. The project was created as part of an... -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
...The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector machines, and hidden Markov models work in practice. In addition to code examples, the project contains supplementary materials such as formula references, glossaries of technical terms, and documentation explaining mathematical notation used throughout the algorithms. The repository also provides links to related research papers and references that expand on the theoretical background presented in the book. -
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Learn_Machine_Learning_in_3_Months
This is the code for "Learn Machine Learning in 3 Months"
...Because ML is a wide field, the curriculum favors pragmatic coverage over academic completeness, pointing learners to widely used tools and approachable resources. It’s intended to help beginners overcome decision paralysis by giving a concrete schedule and a minimal set of action-oriented tasks. -
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Machine-Learning-Flappy-Bird
Machine Learning for Flappy Bird using Neural Network
Machine-Learning-Flappy-Bird is an educational machine learning project that demonstrates how an artificial intelligence agent can learn to play the Flappy Bird game using neural networks and evolutionary algorithms. The system simulates a population of birds that each possess their own neural network, which acts as a decision-making controller during gameplay. The neural network receives input features representing the bird’s position relative to the next obstacle and determines whether the bird should flap or remain idle. Over successive generations, a genetic algorithm evolves the neural networks by selecting high-performing agents and recombining their parameters to produce improved offspring. ... -
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Highly reusable and extensible Decision-Tree (Max-Gain) framework comprising of comprehensive input-processing and display functionality. Handles nominal, linear, continuous data. For preliminary description, refer - http://sushain.com/blog/archives/
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TimeSleuth - Temporal Rule Discovery
Temporal and Causal Decision Rules
TimeSleuth discovers temporal decision rules. It also judges the (a)causality of the rules. TimeSleuth can discover rules that involve time: {if (rainy_yesterday = true) then rainy_today = true}, or {if (rainy_tomorrow = true) then rainy_today = true}. -
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