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 2: Conocimiento Contextual
Create Machine Learning Models
From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy to digest conceptual content and interactive Jupyter notebooks, all without leaving your browser.
Create Machine Learning Models is a subset of the complete learning path Foundations of Data Science for Machine Learning for people with some experience or a strong mathematical background.
If you already have some idea what machine learning is about or you have a strong mathematical background you may best enjoy jumping right in to the Create Machine Learning Models learning path. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.
Create Machine Learning Models is a subset of the complete learning path Foundations of Data Science for Machine Learning for people with some experience or a strong mathematical background.
If you already have some idea what machine learning is about or you have a strong mathematical background you may best enjoy jumping right in to the Create Machine Learning Models learning path. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.
Cursos en este programa
1) Explore and Analyze Data with Python
Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.
In this module, you will learn:
In this module, you will learn:
- Common data exploration and analysis tasks.
- How to use Python packages like NumPy, Pandas, and Matplotlib to analyze data.
Esfuerzo estimado 1 hora
Idioma inglés
2) Train and Evaluate Regression Models
Regression is a commonly used kind of machine learning for predicting numeric values.
In this module, you'll learn:
In this module, you'll learn:
- When to use regression models.
- How to train and evaluate regression models using the Scikit-Learn framework.
3) Train and Evaluate Classification Models
Classification is a kind of machine learning used to categorize items into classes.
In this module, you'll learn:
In this module, you'll learn:
- When to use classification.
- How to train and evaluate a classification model using the Scikit-Learn framework.
4) Train and Evaluate Clustering Models
Clustering is a kind of machine learning that is used to group similar items into clusters.
In this module, you'll learn:
In this module, you'll learn:
- When to use clustering.
- How to train and evaluate a clustering model using the scikit-learn framework.
5) Train and Evaluate Deep Learning Models
Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.
In this module, you will learn:
In this module, you will learn:
- Basic principles of deep learning.
- How to train a deep neural network (DNN) using PyTorch or Tensorflow.
- How to train a convolutional neural network (CNN) using PyTorch or Tensorflow.
- How to use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow.