Caso precise, selecionamos alguns conteúdos que podem te ajudar a completar este desafio. Eles estão ordenados por nível iniciante, introdutório e avançado. Bons estudos!
In this section, we introduce the machine learning vocabulary that we use throughout scikit-learn and give a simple learning example.
In this video, you'll see what the model looks like and more importantly you'll see what the overall process of supervised learning looks like. Let's use some motivating example of predicting housing prices. We're going to use a data set of housing prices from the city of Portland, Oregon.
In this video, I want to start to talk about classification problems, where the variable y that you want to predict is valued. We'll develop an algorithm called logistic regression, which is one of the most popular and most widely used learning algorithms today.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
This module shows how logistic regression can be used for classification tasks, and explores how to evaluate the effectiveness of classification models
Logistic regression, despite its name, is a linear model for classification rather than regression