[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tool-jbagnato--machine-learning":3,"similar-jbagnato--machine-learning":65},{"id":4,"github_repo":5,"name":6,"description_en":7,"description_zh":8,"ai_summary_zh":8,"readme_en":9,"readme_zh":10,"quickstart_zh":11,"use_case_zh":12,"hero_image_url":13,"owner_login":14,"owner_name":15,"owner_avatar_url":16,"owner_bio":17,"owner_company":18,"owner_location":19,"owner_email":20,"owner_twitter":14,"owner_website":21,"owner_url":22,"languages":23,"stars":36,"forks":37,"last_commit_at":38,"license":39,"difficulty_score":40,"env_os":41,"env_gpu":42,"env_ram":42,"env_deps":43,"category_tags":46,"github_topics":48,"view_count":53,"oss_zip_url":54,"oss_zip_packed_at":54,"status":55,"created_at":56,"updated_at":57,"faqs":58,"releases":64},220,"jbagnato\u002Fmachine-learning","machine-learning","Código Python, Jupyter Notebooks, archivos csv con ejemplos para los ejercicios del Blog aprendemachinelearning.com y del libro Aprende Machine Learning en Español","machine-learning 是一个面向中文学习者的机器学习实战资源库，提供大量 Python 代码示例、Jupyter Notebook 教程和配套数据集（如 CSV 文件），内容与博客网站 aprendemachinelearning.com 和同名西班牙语书籍同步。它帮助初学者从零搭建 Python 开发环境（如 Anaconda）、在云端使用 GPU 加速训练（Google Colab），并逐步掌握主流算法——包括线性回归、逻辑回归、决策树、K-Means 聚类、K近邻分类，甚至从零实现神经网络或用 Keras\u002FTensorFlow 构建深度学习模型。部分案例结合真实场景，比如爬取西班牙股市数据或预测 Billboard 排行榜，让理论更接地气。适合刚入门的开发者、学生或自学者，无需深厚数学背景，只需基础 Python 知识即可上手。亮点在于所有教程配有可视化图表和分步讲解，强调“动手做中学”，降低理解门槛。无论你是在家自学、准备项目，还是想巩固课堂知识，这个仓库都能为你提供清晰、可运行的参考代码和实用技巧。","# Aprende Machine Learning\nCódigo ejemplo para prácticas de [www.AprendeMachineLearning.com](https:\u002F\u002Fwww.aprendemachinelearning.com\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) Algoritmos y source code en Python (Jupyter Notebooks).\n\nArtículos completos en el Blog:\n\n![Crea tu Ambiente de Programación Python](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5d3d73561453.png)\n\n* [Instalar y configurar ambiente desarrollo Python Anaconda](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Finstalar-ambiente-de-desarrollo-python-anaconda-para-aprendizaje-automatico\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n![Machine Learning en la Nube](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_eb71246133ad.png)\n\n* [Machine Learning en la Nube: Google Colaboratory con GPU!](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fmachine-learning-en-la-nube-google-colaboratory-con-gpu\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n![WebScraping](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_aca6dc3d56b3.png)\n\n* [Ejemplo Web Scraping en Python: IBEX35® la Bolsa de Madrid](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fejemplo-web-scraping-python-ibex35-bolsa-valores\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n| Artículos ML | Artículos ML |\n| ------------- | ----------- |\n| ![Ejercicio de Regresión Logistica](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_73c24a928bfe.png) \u003Cbr>[Regresión Logística con Python paso a paso](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fregresion-logistica-con-python-paso-a-paso\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Aprende Regresión Lineal en Python](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_265b0014a523.png) \u003Cbr>[Ejercicio de Regresión Lineal Simple y Múltiple](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fregresion-lineal-en-espanol-con-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Ejemplo Machine Learning arboles de decisión](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c8e3f2cbde1d.png)\u003Cbr> [Árbol de Decisión, predicción Billboard 100](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Farbol-de-decision-en-python-clasificacion-y-prediccion\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Red Neuronal en Python con Keras](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_b33d606610e8.png) \u003Cbr>[Una sencilla Red Neuronal en Python con Keras y Tensorflow](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Funa-sencilla-red-neuronal-en-python-con-keras-y-tensorflow\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Ejemplo Machine Learning clustering](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_0be406f54244.png) \u003Cbr>[K-Means en Python paso a paso](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fk-means-en-python-paso-a-paso\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![K-Nearest-Neighbor en Python](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_a09c24508f0f.png) \u003Cbr>[Clasificar con K-Nearest-Neighbor ejemplo en Python](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificar-con-k-nearest-neighbor-ejemplo-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Red Neuronal desde cero](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_76a35fbefed6.png) \u003Cbr>[Crear una Red Neuronal en Python desde cero](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcrear-una-red-neuronal-en-python-desde-cero\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Coche Arduino con IA](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_950230710891.png) \u003Cbr>[Programa un coche Arduino con Inteligencia Artificial](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fprograma-un-coche-arduino-con-inteligencia-artificial\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Gaussian Naive Bayes](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_217d5cd44c19.png) \u003Cbr>[¿Comprar casa o Alquilar? Naive Bayes usando Python](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomprar-casa-o-alquilar-naive-bayes-usando-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![PCA](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5f1127422e7b.png) \u003Cbr>[Comprende Principal Component Analysis](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomprende-principal-component-analysis\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Clasificacion imagenes Python](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_69f37bd32085.png) \u003Cbr>[Clasificacion de imagenes en Python](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificacion-de-imagenes-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![CNN](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_54c5b02157d7.png) \u003Cbr>[Como funcionan una Convolutional Neural Network](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomo-funcionan-las-convolutional-neural-networks-vision-por-ordenador\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![NLP](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_3e96ca578a35.png) \u003Cbr>Introducción \u002F Teoría [Procesamiento del Lenguaje Natural](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fprocesamiento-del-lenguaje-natural-nlp\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![NLP](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_eef73a230cef.png) \u003Cbr>Introducción \u002F Práctica [NLP: Analizamos los cuentos de Hernan Casciari](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fejercicio-nlp-cuentos-de-hernan-casciari-python-espanol\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Pronostico Series Temporales Parte 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_000fb0918a86.png) \u003Cbr>[Pronóstico de Series Temporales con Redes Neuronales en Python](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fpronostico-de-series-temporales-con-redes-neuronales-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Pronostico Series Temporales Parte 2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c586fe2ef10f.png) \u003Cbr>[Pronóstico de Ventas con Redes Neuronales – Parte 2](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fpronostico-de-ventas-redes-neuronales-python-embeddings\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Random Forest, el poder del Ensamble](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_68f0ffd8e10b.png) \u003Cbr>[Cómo funciona Random Forest](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Frandom-forest-el-poder-del-ensamble\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Clasificación con datos desbalanceados](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_17e41ca7568f.png) \u003Cbr>[Clasificación con datos desbalanceados](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificacion-con-datos-desbalanceados\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![12 Consejos útiles para aplicar Machine Learning](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_af883b282afc.png) \u003Cbr>[Consejos para tus modelos de Machine Learning](https:\u002F\u002Fwww.aprendemachinelearning.com\u002F12-consejos-utiles-para-aplicar-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Interpretación de Modelos de Machine Learning](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_e40561adba28.png) \u003Cbr>[Interpretación de Modelos de Machine Learning](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Finterpretacion-de-modelos-de-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Sistemas de Recomendacion](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_8fd99440b30a.png) \u003Cbr>[Sistemas de Recomendacion](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fsistemas-de-recomendacion\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![EDA Exploratory Data Analysis](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c181d411be89.png) \u003Cbr>[Analisis Exploratorio de Datos](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fanalisis-exploratorio-de-datos-pandas-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Ouliers anomaly detection](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5d50ff8b8a3a.png) \u003Cbr>[Detección de Outliers](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fdeteccion-de-outliers-en-python-anomalia\u002F) | ![Vision artificial Lego](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c2e30dac1761.png) \u003Cbr>[Detección de Objetos con YOLO](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fdeteccion-de-objetos-con-python-yolo-keras-tutorial\u002F) |\n| ![Perfiles y Roles Machine Learning](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_082976691bc1.png) \u003Cbr>[Perfiles y Roles para Proyectos IA, Machine Learning](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fperfiles-roles-proyectos-ia-ml-data-science\u002F) | ![Reinforcement Learning Pong](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_b91cc17ce59a.png)\u003Cbr> [Aprendizaje por Refuerzo, Pong](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Faprendizaje-por-refuerzo\u002F) |\n\n\n![Tu API Flask para ML](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_38498c85b74d.png)\n\n* [Tu propio Servicio de Machine Learning](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Ftu-propio-servicio-de-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n## El libro\n[![Aprende Machine Learning en Español](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_01a48d65951c.png)](https:\u002F\u002Fleanpub.com\u002Faprendeml\u002F) \u003Cbr>\nDisponible el libro \"Aprende Machine Learning en Español\" en [formato digital](https:\u002F\u002Fleanpub.com\u002Faprendeml\u002F) ó en papel [tapa blanda en Amazon](https:\u002F\u002Famzn.to\u002F2LLeiGf) -debes buscar la tienda de tu páis-\n\n@jbagnato\n","# 学习机器学习（Aprende Machine Learning）\n用于 [www.AprendeMachineLearning.com](https:\u002F\u002Fwww.aprendemachinelearning.com\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) 实践的示例代码。包含 Python（Jupyter Notebooks）实现的算法与源代码。\n\n完整文章请见博客：\n\n![创建你的 Python 编程环境](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5d3d73561453.png)\n\n* [安装并配置 Python Anaconda 开发环境（用于机器学习）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Finstalar-ambiente-de-desarrollo-python-anaconda-para-aprendizaje-automatico\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n![云端机器学习](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_eb71246133ad.png)\n\n* [云端机器学习：使用 GPU 的 Google Colaboratory！](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fmachine-learning-en-la-nube-google-colaboratory-con-gpu\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n![网络爬虫（Web Scraping）](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_aca6dc3d56b3.png)\n\n* [Python 网络爬虫示例：西班牙 IBEX35® 股票指数](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fejemplo-web-scraping-python-ibex35-bolsa-valores\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n| 机器学习文章 | 机器学习文章 |\n| ------------- | ----------- |\n| ![逻辑回归练习](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_73c24a928bfe.png) \u003Cbr>[使用 Python 逐步实现逻辑回归（Logistic Regression）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fregresion-logistica-con-python-paso-a-paso\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![在 Python 中学习线性回归](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_265b0014a523.png) \u003Cbr>[简单与多元线性回归（Linear Regression）练习](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fregresion-lineal-en-espanol-con-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![决策树机器学习示例](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c8e3f2cbde1d.png)\u003Cbr> [决策树（Decision Tree），预测 Billboard 100](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Farbol-de-decision-en-python-clasificacion-y-prediccion\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![使用 Keras 的 Python 神经网络](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_b33d606610e8.png) \u003Cbr>[使用 Keras 和 Tensorflow 在 Python 中构建简易神经网络（Neural Network）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Funa-sencilla-red-neuronal-en-python-con-keras-y-tensorflow\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![机器学习聚类示例](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_0be406f54244.png) \u003Cbr>[Python 中逐步实现 K-Means](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fk-means-en-python-paso-a-paso\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![Python 中的 K-近邻算法](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_a09c24508f0f.png) \u003Cbr>[使用 K-近邻（K-Nearest Neighbor）分类的 Python 示例](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificar-con-k-nearest-neighbor-ejemplo-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![从零开始构建神经网络](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_76a35fbefed6.png) \u003Cbr>[在 Python 中从零开始创建神经网络](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcrear-una-red-neuronal-en-python-desde-cero\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![带人工智能的 Arduino 小车](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_950230710891.png) \u003Cbr>[用人工智能编程控制 Arduino 小车](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fprograma-un-coche-arduino-con-inteligencia-artificial\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![高斯朴素贝叶斯](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_217d5cd44c19.png) \u003Cbr>[买房还是租房？使用 Python 实现朴素贝叶斯（Naive Bayes）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomprar-casa-o-alquilar-naive-bayes-usando-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![主成分分析 PCA](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5f1127422e7b.png) \u003Cbr>[理解主成分分析（Principal Component Analysis, PCA）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomprende-principal-component-analysis\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![Python 图像分类](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_69f37bd32085.png) \u003Cbr>[Python 中的图像分类](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificacion-de-imagenes-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![卷积神经网络 CNN](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_54c5b02157d7.png) \u003Cbr>[卷积神经网络（Convolutional Neural Network, CNN）工作原理](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fcomo-funcionan-las-convolutional-neural-networks-vision-por-ordenador\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![自然语言处理 NLP](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_3e96ca578a35.png) \u003Cbr>理论介绍：[自然语言处理（Natural Language Processing, NLP）](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fprocesamiento-del-lenguaje-natural-nlp\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![自然语言处理 NLP](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_eef73a230cef.png) \u003Cbr>实践入门：[NLP：用 Python 分析 Hernán Casciari 的故事](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fejercicio-nlp-cuentos-de-hernan-casciari-python-espanol\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![时间序列预测 第一部分](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_000fb0918a86.png) \u003Cbr>[使用神经网络进行时间序列（Time Series）预测 - Python 实现](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fpronostico-de-series-temporales-con-redes-neuronales-en-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![时间序列预测 第二部分](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c586fe2ef10f.png) \u003Cbr>[销售预测与神经网络 – 第二部分](http:\u002F\u002Fwww.aprendemachinelearning.com\u002Fpronostico-de-ventas-redes-neuronales-python-embeddings\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![随机森林，集成的力量](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_68f0ffd8e10b.png) \u003Cbr>[随机森林（Random Forest）工作原理](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Frandom-forest-el-poder-del-ensamble\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![不平衡数据分类](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_17e41ca7568f.png) \u003Cbr>[不平衡数据（Imbalanced Data）下的分类问题](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fclasificacion-con-datos-desbalanceados\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![12 条实用的机器学习建议](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_af883b282afc.png) \u003Cbr>[提升你的机器学习模型的 12 条建议](https:\u002F\u002Fwww.aprendemachinelearning.com\u002F12-consejos-utiles-para-aplicar-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![机器学习模型解释](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_e40561adba28.png) \u003Cbr>[机器学习模型的可解释性（Model Interpretability）](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Finterpretacion-de-modelos-de-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![推荐系统](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_8fd99440b30a.png) \u003Cbr>[推荐系统（Recommendation Systems）](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fsistemas-de-recomendacion\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) | ![探索性数据分析 EDA](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c181d411be89.png) \u003Cbr>[探索性数据分析（Exploratory Data Analysis, EDA）](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fanalisis-exploratorio-de-datos-pandas-python\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) |\n| ![异常值检测](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_5d50ff8b8a3a.png) \u003Cbr>[异常值（Outliers）检测](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fdeteccion-de-outliers-en-python-anomalia\u002F) | ![乐高计算机视觉](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_c2e30dac1761.png) \u003Cbr>[使用 YOLO 进行目标检测](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fdeteccion-de-objetos-con-python-yolo-keras-tutorial\u002F) |\n| ![机器学习角色与岗位](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_082976691bc1.png) \u003Cbr>[AI 与机器学习项目中的角色与岗位](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Fperfiles-roles-proyectos-ia-ml-data-science\u002F) | ![强化学习 Pong](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_b91cc17ce59a.png)\u003Cbr> [强化学习（Reinforcement Learning）实战：Pong 游戏](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Faprendizaje-por-refuerzo\u002F) |\n\n![你的 ML Flask API](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_38498c85b74d.png)\n\n* [打造你自己的机器学习（Machine Learning）服务](https:\u002F\u002Fwww.aprendemachinelearning.com\u002Ftu-propio-servicio-de-machine-learning\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio)\n\n\n\n## 这本书\n[![学习西班牙语的机器学习（Aprende Machine Learning en Español）](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_readme_01a48d65951c.png)](https:\u002F\u002Fleanpub.com\u002Faprendeml\u002F) \u003Cbr>\n《Aprende Machine Learning en Español》一书现已提供[数字版](https:\u002F\u002Fleanpub.com\u002Faprendeml\u002F)，或在 Amazon 上购买[平装纸质版](https:\u002F\u002Famzn.to\u002F2LLeiGf) ——请搜索你所在国家的 Amazon 商店—\n\n@jbagnato","```markdown\n# Machine Learning 开源工具快速上手指南\n\n## 环境准备\n\n- **操作系统**：支持 Windows \u002F macOS \u002F Linux\n- **Python 版本**：推荐 Python 3.7 或以上\n- **开发环境**：\n  - 推荐使用 [Anaconda](https:\u002F\u002Fwww.anaconda.com\u002F)（国内用户可使用清华镜像加速下载：https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Farchive\u002F）\n  - 或使用 Google Colab（免费 GPU 支持，无需本地安装）：https:\u002F\u002Fcolab.research.google.com\u002F\n\n## 安装步骤\n\n1. 安装 Anaconda（若未安装）：\n   ```bash\n   # 下载后运行安装程序，或使用命令行安装 Miniconda（轻量版）\n   wget https:\u002F\u002Frepo.anaconda.com\u002Fminiconda\u002FMiniconda3-latest-Linux-x86_64.sh\n   bash Miniconda3-latest-Linux-x86_64.sh\n   ```\n\n2. 克隆项目代码：\n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Fjbagnato\u002Fmachine-learning.git\n   cd machine-learning\n   ```\n\n3. 创建并激活虚拟环境（推荐）：\n   ```bash\n   conda create -n ml-env python=3.8\n   conda activate ml-env\n   ```\n\n4. 安装依赖库（根据具体 Notebook 需求，基础包如下）：\n   ```bash\n   pip install jupyter pandas scikit-learn matplotlib seaborn tensorflow keras numpy requests beautifulsoup4\n   # 国内用户建议使用清华源加速：\n   pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple jupyter pandas scikit-learn matplotlib seaborn tensorflow keras numpy requests beautifulsoup4\n   ```\n\n5. 启动 Jupyter Notebook：\n   ```bash\n   jupyter notebook\n   ```\n\n## 基本使用\n\n1. 在浏览器中打开 Jupyter Notebook 界面（默认地址：http:\u002F\u002Flocalhost:8888）\n\n2. 进入任意示例目录，例如 `regresion_logistica\u002F`，打开 `.ipynb` 文件\n\n3. 按顺序执行单元格（Cell → Run All 或逐个 Shift+Enter）\n\n4. 查看输出结果和图表，修改参数尝试不同效果\n\n> 💡 提示：所有示例均配有详细中文教程，访问 [www.AprendeMachineLearning.com](https:\u002F\u002Fwww.aprendemachinelearning.com\u002F?utm_source=github&utm_medium=readme&utm_campaign=repositorio) 获取完整文章。\n```","一位西班牙语区的大学讲师正在为本科生设计“机器学习实战”课程，需要准备从数据预处理到模型训练的完整教学案例，同时确保学生能轻松复现结果。\n\n### 没有 machine-learning 时\n- 教师需从零编写每个算法示例（如逻辑回归、K-Means），调试耗时且容易出错，影响备课效率。\n- 学生常因环境配置问题（如缺少库或版本冲突）无法运行代码，课堂进度被打断。\n- 教学案例缺乏真实数据支撑（如股票爬取、音乐排行榜预测），学生难以理解实际应用场景。\n- 每次更新课程内容都要重新整理 Jupyter Notebook 和数据文件，版本管理混乱。\n- 缺乏配套博客文章引导，学生自学时遇到报错无处查阅解决方案。\n\n### 使用 machine-learning 后\n- 直接调用仓库中经过验证的 Python 示例代码（如决策树预测 Billboard 排行榜），节省80%以上开发时间，专注讲解核心概念。\n- 学生一键克隆项目，配合 Anaconda 环境指南快速搭建统一开发环境，课堂实操成功率提升至95%。\n- 使用内置真实数据集（如 IBEX35 股票数据、音乐特征CSV），结合 Web Scraping 教程，让抽象算法具象化。\n- 所有练习按主题分类存放，教师只需替换部分参数即可生成新作业，版本迭代清晰可控。\n- 每个 Notebook 对应博客详解文章（含GPU云部署技巧），学生课后可自助排错，减少教师答疑负担。\n\nmachine-learning 让非英语母语的教学者也能高效构建沉浸式机器学习课堂，把精力从“调试代码”转向“启发思维”。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjbagnato_machine-learning_905184f8.png","jbagnato","Juan Ignacio Bagnato","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjbagnato_dde139f5.jpg","Machine Learning, Data Science, Big Data, Statistics, Artificial Intelligence, Data Storytelling, Data Visualization","Na8","A Coruña, Spain","jbagnato@gmail.com","http:\u002F\u002Fwww.aprendemachinelearning.com\u002F","https:\u002F\u002Fgithub.com\u002Fjbagnato",[24,28,32],{"name":25,"color":26,"percentage":27},"Jupyter Notebook","#DA5B0B",99.5,{"name":29,"color":30,"percentage":31},"Python","#3572A5",0.5,{"name":33,"color":34,"percentage":35},"C++","#f34b7d",0.1,561,729,"2026-03-18T11:48:49","GPL-3.0",2,"","未说明",{"notes":44,"python":42,"dependencies":45},"建议使用 Anaconda 配置 Python 环境，或通过 Google Colab 在云端运行（支持 GPU）。项目包含 Jupyter Notebook 示例，需安装相应库如 scikit-learn、Keras、TensorFlow、pandas、numpy 等，具体版本未明确指定。",[],[47],"开发框架",[6,49,50,51,52],"jupyter-notebooks","algoritmos","modelos","aprendizaje-automatico",3,null,"ready","2026-03-27T02:49:30.150509","2026-04-06T05:17:18.506019",[59],{"id":60,"question_zh":61,"answer_zh":62,"source_url":63},628,"运行 detectar.py 时传入视频参数后无任何输出，是什么原因？","检测结果为空导致无输出。请尝试使用包含行人等明显目标的视频进行测试，确认模型能正常检测到对象后再排查数据加载器问题。","https:\u002F\u002Fgithub.com\u002Fjbagnato\u002Fmachine-learning\u002Fissues\u002F6",[],[66,76,85,93,101,114],{"id":67,"name":68,"github_repo":69,"description_zh":70,"stars":71,"difficulty_score":53,"last_commit_at":72,"category_tags":73,"status":55},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[47,74,75],"图像","Agent",{"id":77,"name":78,"github_repo":79,"description_zh":80,"stars":81,"difficulty_score":40,"last_commit_at":82,"category_tags":83,"status":55},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,"2026-04-05T11:33:21",[47,75,84],"语言模型",{"id":86,"name":87,"github_repo":88,"description_zh":89,"stars":90,"difficulty_score":40,"last_commit_at":91,"category_tags":92,"status":55},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[47,74,75],{"id":94,"name":95,"github_repo":96,"description_zh":97,"stars":98,"difficulty_score":40,"last_commit_at":99,"category_tags":100,"status":55},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[47,84],{"id":102,"name":103,"github_repo":104,"description_zh":105,"stars":106,"difficulty_score":40,"last_commit_at":107,"category_tags":108,"status":55},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[74,109,110,111,75,112,84,47,113],"数据工具","视频","插件","其他","音频",{"id":115,"name":116,"github_repo":117,"description_zh":118,"stars":119,"difficulty_score":53,"last_commit_at":120,"category_tags":121,"status":55},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[75,74,47,84,112]]