#python #data_mining #data_science #deep_learning #deep_reinforcement_learning #genetic_algorithm #machine_learning #machine_learning_from_scratch

This project offers Python code for many basic machine learning models and algorithms built from scratch, focusing on clear, understandable implementations rather than speed or optimization. You can learn how these algorithms work inside by running examples like polynomial regression, convolutional neural networks, clustering, and genetic algorithms. This hands-on approach helps you deeply understand machine learning concepts and build your own custom models. Using Python makes it easier because of its simple, readable code and flexibility, letting you quickly test and modify algorithms. This can improve your skills and confidence in machine learning development.

https://github.com/eriklindernoren/ML-From-Scratch

#html #data_science #education #machine_learning #machine_learning_algorithms #machinelearning #machinelearning_python #microsoft_for_beginners #ml #python #r #scikit_learn #scikit_learn_python

Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5].

https://github.com/microsoft/ML-For-Beginners

#typescript #data_visualization #geospatial_analysis #javascript #maps #python #visualization #webgl

deck.gl is a powerful tool that helps you create fast, interactive, and visually impressive maps and data visualizations using WebGL technology. It lets you turn large sets of data into layers like icons, polygons, and text, which you can view in different ways such as maps or 3D scenes. It works well with popular map providers like Google Maps and Mapbox, and supports easy interaction like clicking and filtering. You can use it simply by adding a script or installing it via npm or Python. This makes it easier for you to build custom, high-performance visualizations quickly and with less coding effort.

https://github.com/visgl/deck.gl

#other #artificial_intelligence #artificial_intelligence_projects #awesome #computer_vision #computer_vision_project #data_science #deep_learning #deep_learning_project #machine_learning #machine_learning_projects #nlp #nlp_projects #python

You can access a huge, constantly updated list of over 500 artificial intelligence projects with ready-to-use code covering machine learning, deep learning, computer vision, and natural language processing. This collection includes projects for beginners and advanced users, with links to tutorials, datasets, and real-world applications like chatbots, healthcare, and time series forecasting. Using this resource helps you learn AI by doing practical projects, speeding up your coding skills, and building a strong portfolio for jobs or research. It saves you time searching for quality projects and gives you tested, working code to study and modify.

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#java #cache #caffine #data #draft #fetch #graphql #immer #immutable #immutable_collections #immutable_datastructures #java #jdbc #kotlin #orm #orm_framework #orm_library #orms #redis #redis_cache

Jimmer is a powerful and advanced ORM (Object-Relational Mapping) framework for Java and Kotlin that lets you easily read and write complex data structures without needing to predefine their shapes. It supports dynamic multi-table queries, automatic SQL optimization, and efficient saving of incomplete or nested objects. Jimmer also generates type-safe DTOs (Data Transfer Objects) for complex queries and updates, avoids common problems like "N+1" queries, and offers strong caching and GraphQL support. This means you can build complex business logic faster and with less hassle, improving both development speed and code quality. It works well with modern IDEs and supports both Java and Kotlin seamlessly.

https://github.com/babyfish-ct/jimmer

#rust #artificial_intelligence #big_data #data_engineering #distributed_computing #machine_learning #multimodal #python #rust

Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns.

https://github.com/Eventual-Inc/Daft

#go #archival #data_archiving #data_import #family_history #self_hosted #timeline

Timelinize helps you organize your personal data from different sources like photos, messages, and social media into a single timeline on your computer. This keeps your data private and under your control, unlike cloud services. You can import data from many places, view it on a map, and see conversations across different platforms. It's like having a personal journal that you can add to and keep forever, without relying on companies to store it for you. This way, you can keep your memories safe and easily look back at them whenever you want.

https://github.com/timelinize/timelinize

#python #agent_framework #data_analysis #deep_research #deep_search #llms #multi_agent_system #nlp #public_opinion_analysis #python3 #sentiment_analysis

You can use the "Weibo Public Opinion Analysis System" (called "微舆") to automatically analyze public opinion from over 30 major social media platforms and millions of comments. It uses AI agents working together to monitor, search, analyze text and videos, and generate detailed reports based on real-time data. The system supports easy setup, custom models, and integration with your own databases, helping you understand public sentiment, trends, and make better decisions. It offers continuous monitoring, deep multi-angle analysis, and flexible report generation, all accessible by simply asking questions like chatting. This saves you time and gives clear insights into public opinion dynamics.

https://github.com/666ghj/BettaFish

#python #data_analysis #dingtalk_robot #docker #feishu_robot #hot_news #mail #mcp #mcp_server #news #ntfy #python #telegram_bot #trending_topics #wechat_robot

TrendRadar is a lightweight, easy-to-deploy tool that gathers trending topics from 11+ major platforms like Zhihu, Douyin, and Baidu in just 30 seconds. It lets you set custom keywords to filter only news you care about, eliminating information overload. The tool offers three smart notification modes—daily summaries, current rankings, or incremental alerts—and supports multiple channels including WeChat Work, Feishu, DingTalk, Telegram, and email. You can customize how trends are ranked using a personalized algorithm that weighs ranking position, frequency, and hotness. With GitHub Pages for web reports, Docker support, and AI-powered analysis through MCP protocol, TrendRadar transforms scattered platform algorithms into one unified, user-controlled news feed tailored to your interests.

https://github.com/sansan0/TrendRadar

#rust #ai #change_data_capture #context_engineering #data #data_engineering #data_indexing #data_infrastructure #data_processing #etl #hacktoberfest #help_wanted #indexing #knowledge_graph #llm #pipeline #python #rag #real_time #rust #semantic_search

**CocoIndex** is a fast, open-source Python tool (Rust core) for transforming data into AI formats like vector indexes or knowledge graphs. Define simple data flows in ~100 lines of code using plug-and-play blocks for sources, embeddings, and targets—install via `pip install cocoindex`, add Postgres, and run. It auto-syncs fresh data with minimal recompute on changes, tracking lineage. **You save time building scalable RAG/semantic search pipelines effortlessly, avoiding complex ETL and stale data issues for production-ready AI apps.**

https://github.com/cocoindex-io/cocoindex