Search Results for "math with python learn"
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214 projects for "math with python learn" with 1 filter applied:
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Respond 100x faster, more accurately, and improve your documentationSlash response times for questionnaires, audits, and RFPs by up to 90%. OptiValue.ai automates the heavy lifting, freeing your team to focus on strategic priorities with intuitive tools for seamless review and validation.
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The Industry Leading Platform for eCommerce Enablement and AnalyticsMikMak’s Where to Buy Shoppable Solutions help multichannel brands drive sales, grow market share, and increase profitability while reducing costs across categories such as CPG, Grocery, Alcohol, Beauty, Personal Care, Pet Care, Home Care, Consumer Electronics, Home Appliances, Toys, and more.
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. -
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Ai-Learn
The artificial intelligence learning roadmap compiles 200 cases
Ai-Learn is an open-source artificial intelligence learning roadmap that aggregates educational materials, tutorials, and practical projects designed to help beginners study AI and machine learning systematically. The repository was created to help learners start self-study programs in artificial intelligence without getting overwhelmed by the large number of available resources. -
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FastAPI Python
FastAPI framework, high performance, easy to learn, fast to code
FastAPI framework, high performance, easy to learn, fast to code, ready for production. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. -
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
Self-hosted n8n: No-code AI workflowsA free-to-use workflow automation tool, n8n lets you connect all your apps and data in one customizable, no-code platform. Design workflows and process data from a simple, unified dashboard.
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DeepSeek Math
Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek-Math is DeepSeek’s specialized model (or dataset + evaluation) focusing on mathematical reasoning, symbolic manipulation, proof steps, and advanced quantitative problem solving. The repository is likely to include fine-tuning routines or task datasets (e.g. MATH, GSM8K, ARB), demonstration notebooks, prompt templates, and evaluation results on math benchmarks. The goal is to push DeepSeek’s performance in domains that require rigorous symbolic steps, calculus, linear algebra, number... -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
Learn Claude Code is an educational repository that teaches how modern AI coding agents work by walking learners through a sequence of progressively more complex agent implementations, starting with a minimal Bash-based agent and culminating in agents with explicit planning, subagents, and skills. It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills,... -
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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions... -
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Python Programming Hub
Learn Python and Machine Learning from scratch
Python Programming Hub repository by Tanu-N-Prabhu is an educational resource designed to help programmers learn Python programming and data science concepts through practical examples and notebooks. The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. -
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Python-Spider
Python3 web crawler practice
Python-Spider is a repository intended to teach or provide examples for writing web spiders / crawlers in Python — part of a broader learning and resource collection by its author. The code and documentation are oriented toward beginners or intermediate learners who want to learn how to fetch, parse, and extract data from websites programmatically. -
Accounting practice management softwareCanopy is a cloud-based practice management software for accounting and tax firms, offering tools for client engagement, document management, workflow automation, and time & billing. Its Client Engagement platform centralizes interactions with a secure portal, customizable branding, and email integration, while the Document Management system enables organized, paperless file storage. The Workflow module enhances visibility into tasks and projects through templates, task assignments, and automation, reducing human error. Additionally, the Time & Billing feature tracks billable hours, generates invoices, and processes payments, ensuring accurate financial management. With its comprehensive features, Canopy streamlines operations, reduces stress, and enhances client experiences.
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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
scikit-learn-videos repository accompanies a video tutorial series designed to teach machine learning using Python’s scikit-learn library. 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. ... -
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skfolio
Python library for portfolio optimization built on top of scikit-learn
skfolio is a Python library designed for portfolio optimization and financial risk management that integrates closely with the scikit-learn ecosystem. The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. ... -
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LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
LTX-2 is a powerful, open-source toolkit developed by Lightricks that provides a modular, high-performance base for building real-time graphics and visual effects applications. It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries,... -
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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The... -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ... -
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Python Zero to Hero for DevOps Engineers
Learn Python from DevOps Engineer point of you
Python Zero to Hero for DevOps Engineers is a structured “Python Zero to Hero for DevOps Engineers” course laid out as a day-by-day learning path. The repository is organized into Day-01 through Day-19 folders plus a small sample app, which makes it very easy to follow in sequence like a bootcamp. The curriculum starts with Python installation, environment setup, and writing your first script, then quickly moves into data types, strings, regular expressions, variables, and functions. It... -
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Practical Machine Learning with Python
Master the essential skills needed to recognize and solve problems
...The repository emphasizes end-to-end workflows rather than isolated code snippets, showing how to handle common challenges like class imbalance, overfitting, hyperparameter optimization, and interpretability. By leveraging popular Python libraries such as pandas, scikit-learn, XGBoost, and visualization tools, it illustrates how to build reproducible and robust solutions that scale beyond small demos. -
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System Design Primer
Learn how to design large-scale systems
System Design Primer is a curated, open source collection of resources that helps engineers learn how to design large-scale systems. The project is structured as a comprehensive guide covering core system design concepts, trade-offs, and patterns necessary for building scalable, reliable, and maintainable systems. It offers both theoretical foundations—such as scalability principles, the CAP theorem, and consistency models—and practical exercises, including real-world system design interview... -
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Theseus
A library for differentiable nonlinear optimization
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost... -
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PyQuil
A Python library for quantum programming using Quil
PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions. PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. ... -
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openbench
Provider-agnostic, open-source evaluation infrastructure
openbench is an open-source, provider-agnostic evaluation infrastructure designed to run standardized, reproducible benchmarks on large language models (LLMs), enabling fair comparison across different model providers. It bundles dozens of evaluation suites — covering knowledge, reasoning, math, code, science, reading comprehension, long-context recall, graph reasoning, and more — so users don’t need to assemble disparate datasets themselves. With a simple CLI interface (e.g. bench eval... -
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Agentic Context Engine
Make your agents learn from experience
Agentic Context Engine (ACE) is an open-source framework designed to help AI agents improve their performance by learning from their own execution history. Instead of relying solely on model training or fine-tuning, the framework focuses on structured context engineering, allowing agents to accumulate knowledge from past successes and failures during task execution. The system treats context as a dynamic “playbook” that evolves over time through a process of generation, reflection, and... -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. 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. ...
