Bienvenidos
IPN-Dharma IA Lab
Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.
Nivel 1: Alfabetización y Fundamentos
Machine Learning: Regression, Classification, and Clustering
Welcome to the Machine Learning: Regression, Classification, and Clustering Learning Path! The content in this learning path pairs with in person workshops that run in Microsoft Reactors and are standalone learning resources (you don't have to come to a workshop to benefit from these modules). Throughout this Learning Path you will be encouraged to test out Python code in Visual Studio Code (VS Code) using the Python extension and Jupyter Notebooks.
In this learning path, you'll:
In this learning path, you'll:
- Learn ways to prepare data for analysis.
- Make predictive models with variations of linear regression.
- Make predictions on non-linear data with regression.
- Build logistic regression and support vector machine models.
- Learn about results obtained with the k-means algorithm.
Cursos en este programa
1) Join and Clean Datasets: Deep dive
Learn how to join and clean datasets and prepare your data for analysis.
In this module, you will:
In this module, you will:
- Learn ways to prepare data for analysis.
- Dive deeper into cleaning and joining datasets Complementary content for Microsoft Reactor Workshops.
Esfuerzo estimado 1 hora
Idioma inglés
2) Supervised Learning: Regression
Learn about linear regression models, and how to interpret their results.
In this module, you will:
In this module, you will:
- Learn to fit linear-regression models.
- Become familiar with interpreting the output of linear-regression models.
3) Unsupervised Learning: Clustering
Learn about k-means clustering - how to use it, the kinds of results to expect, and how to interpret the data.
In this module, you will:
In this module, you will:
- Learn about the kinds of results obtained with the k-means algorithm.
- Get basic knowledge about how to interpret those results.