Caso precise, selecionamos alguns conteúdos que podem te ajudar a completar este desafio. Eles estão ordenados por nível iniciante, introdutório e avançado. Bons estudos!
The typical Pandas user learns one dataframe method at a time, slowly scraping features together through trial and error until they can solve the task in front of them. In this tutorial you will re-learn how to think about dataframes from the ground up, and discover how to select intelligently from their abilities to solve your data processing problems through direct and deliberately-chosen steps
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
From the Machine Learning course by Stanford University
Logistic regression, despite its name, is a linear model for classification rather than regression