Python Lab

Introducción al Machine Learning

Esquemas útiles

ParadigmaAlgoritmos
Supervised LearningRegression, Classification
Unsupervised LearningClustering
Reinforcement Learning
AlgoritmoEjemplo de uso
ClusteringAgrupar categorías de preguntas en «10de10»

Proceso

  1. Formulating a Question
  2. Finding and Understanding the Data
  3. Cleaning the Data and Feature Engineering
    1. Link: Normalización
  4. Choosing a Model
  5. Tuning and Evaluating
  6. Using the Model and Presenting Results

Link: https://www.codecademy.com/paths/machine-learning/tracks/introduction-to-machine-learning-skill-path/modules/introduction-to-machine-learning-skill-path/articles/the-ml-process

Citas útiles

Sometimes you learn more about your data by looking at the model. For example, using Multiple Learning Regression can give you insights into the importance of each feature. We can create a feature importance graph to visualize this for those unfamiliar with our model:

https://content.codecademy.com/programs/machine-learning/process/feature_importance.png