Informações sobre o curso
4.8
3,959 classificações
764 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 29 horas para completar

Sugerido: 6 weeks of study, 5-8 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Árabe

Habilidades que você terá

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 29 horas para completar

Sugerido: 6 weeks of study, 5-8 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Árabe

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
1 hora para concluir

Welcome

Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Regression is used in a massive number of applications ranging from predicting stock prices to understanding gene regulatory networks.<p>This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have....
Reading
5 vídeos (total de (Total 20 mín.) min), 3 leituras
Video5 videos
What is the course about?3min
Outlining the first half of the course5min
Outlining the second half of the course5min
Assumed background4min
Reading3 leituras
Important Update regarding the Machine Learning Specialization10min
Slides presented in this module10min
Reading: Software tools you'll need10min
Horas para completar
3 horas para concluir

Simple Linear Regression

Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".<p> In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.<p> You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house....
Reading
25 vídeos (total de (Total 122 mín.) min), 5 leituras, 2 testes
Video25 videos
Regression fundamentals: data & model8min
Regression fundamentals: the task2min
Regression ML block diagram4min
The simple linear regression model2min
The cost of using a given line6min
Using the fitted line6min
Interpreting the fitted line6min
Defining our least squares optimization objective3min
Finding maxima or minima analytically7min
Maximizing a 1d function: a worked example2min
Finding the max via hill climbing6min
Finding the min via hill descent3min
Choosing stepsize and convergence criteria6min
Gradients: derivatives in multiple dimensions5min
Gradient descent: multidimensional hill descent6min
Computing the gradient of RSS7min
Approach 1: closed-form solution5min
Approach 2: gradient descent7min
Comparing the approaches1min
Influence of high leverage points: exploring the data4min
Influence of high leverage points: removing Center City7min
Influence of high leverage points: removing high-end towns3min
Asymmetric cost functions3min
A brief recap1min
Reading5 leituras
Slides presented in this module10min
Optional reading: worked-out example for closed-form solution10min
Optional reading: worked-out example for gradient descent10min
Download notebooks to follow along10min
Reading: Fitting a simple linear regression model on housing data10min
Quiz2 exercícios práticos
Simple Linear Regression14min
Fitting a simple linear regression model on housing data8min
Semana
2
Horas para completar
3 horas para concluir

Multiple Regression

The next step in moving beyond simple linear regression is to consider "multiple regression" where multiple features of the data are used to form predictions. <p> More specifically, in this module, you will learn how to build models of more complex relationship between a single variable (e.g., 'square feet') and the observed response (like 'house sales price'). This includes things like fitting a polynomial to your data, or capturing seasonal changes in the response value. You will also learn how to incorporate multiple input variables (e.g., 'square feet', '# bedrooms', '# bathrooms'). You will then be able to describe how all of these models can still be cast within the linear regression framework, but now using multiple "features". Within this multiple regression framework, you will fit models to data, interpret estimated coefficients, and form predictions. <p>Here, you will also implement a gradient descent algorithm for fitting a multiple regression model....
Reading
19 vídeos (total de (Total 87 mín.) min), 5 leituras, 3 testes
Video19 videos
Polynomial regression3min
Modeling seasonality8min
Where we see seasonality3min
Regression with general features of 1 input2min
Motivating the use of multiple inputs4min
Defining notation3min
Regression with features of multiple inputs3min
Interpreting the multiple regression fit7min
Rewriting the single observation model in vector notation6min
Rewriting the model for all observations in matrix notation4min
Computing the cost of a D-dimensional curve9min
Computing the gradient of RSS3min
Approach 1: closed-form solution3min
Discussing the closed-form solution4min
Approach 2: gradient descent2min
Feature-by-feature update9min
Algorithmic summary of gradient descent approach4min
A brief recap1min
Reading5 leituras
Slides presented in this module10min
Optional reading: review of matrix algebra10min
Reading: Exploring different multiple regression models for house price prediction10min
Numpy tutorial10min
Reading: Implementing gradient descent for multiple regression10min
Quiz3 exercícios práticos
Multiple Regression18min
Exploring different multiple regression models for house price prediction16min
Implementing gradient descent for multiple regression10min
Semana
3
Horas para completar
2 horas para concluir

Assessing Performance

Having learned about linear regression models and algorithms for estimating the parameters of such models, you are now ready to assess how well your considered method should perform in predicting new data. You are also ready to select amongst possible models to choose the best performing. <p> This module is all about these important topics of model selection and assessment. You will examine both theoretical and practical aspects of such analyses. You will first explore the concept of measuring the "loss" of your predictions, and use this to define training, test, and generalization error. For these measures of error, you will analyze how they vary with model complexity and how they might be utilized to form a valid assessment of predictive performance. This leads directly to an important conversation about the bias-variance tradeoff, which is fundamental to machine learning. Finally, you will devise a method to first select amongst models and then assess the performance of the selected model. <p>The concepts described in this module are key to all machine learning problems, well-beyond the regression setting addressed in this course....
Reading
14 vídeos (total de (Total 93 mín.) min), 2 leituras, 2 testes
Video14 videos
What do we mean by "loss"?4min
Training error: assessing loss on the training set7min
Generalization error: what we really want8min
Test error: what we can actually compute4min
Defining overfitting2min
Training/test split1min
Irreducible error and bias6min
Variance and the bias-variance tradeoff6min
Error vs. amount of data6min
Formally defining the 3 sources of error14min
Formally deriving why 3 sources of error20min
Training/validation/test split for model selection, fitting, and assessment7min
A brief recap1min
Reading2 leituras
Slides presented in this module10min
Reading: Exploring the bias-variance tradeoff10min
Quiz2 exercícios práticos
Assessing Performance26min
Exploring the bias-variance tradeoff8min
Semana
4
Horas para completar
3 horas para concluir

Ridge Regression

You have examined how the performance of a model varies with increasing model complexity, and can describe the potential pitfall of complex models becoming overfit to the training data. In this module, you will explore a very simple, but extremely effective technique for automatically coping with this issue. This method is called "ridge regression". You start out with a complex model, but now fit the model in a manner that not only incorporates a measure of fit to the training data, but also a term that biases the solution away from overfitted functions. To this end, you will explore symptoms of overfitted functions and use this to define a quantitative measure to use in your revised optimization objective. You will derive both a closed-form and gradient descent algorithm for fitting the ridge regression objective; these forms are small modifications from the original algorithms you derived for multiple regression. To select the strength of the bias away from overfitting, you will explore a general-purpose method called "cross validation". <p>You will implement both cross-validation and gradient descent to fit a ridge regression model and select the regularization constant....
Reading
16 vídeos (total de (Total 85 mín.) min), 5 leituras, 3 testes
Video16 videos
Overfitting demo7min
Overfitting for more general multiple regression models3min
Balancing fit and magnitude of coefficients7min
The resulting ridge objective and its extreme solutions5min
How ridge regression balances bias and variance1min
Ridge regression demo9min
The ridge coefficient path4min
Computing the gradient of the ridge objective5min
Approach 1: closed-form solution6min
Discussing the closed-form solution5min
Approach 2: gradient descent9min
Selecting tuning parameters via cross validation3min
K-fold cross validation5min
How to handle the intercept6min
A brief recap1min
Reading5 leituras
Slides presented in this module10min
Download the notebook and follow along10min
Download the notebook and follow along10min
Reading: Observing effects of L2 penalty in polynomial regression10min
Reading: Implementing ridge regression via gradient descent10min
Quiz3 exercícios práticos
Ridge Regression18min
Observing effects of L2 penalty in polynomial regression14min
Implementing ridge regression via gradient descent16min
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Melhores avaliações

por PDMar 17th 2016

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!

por CMJan 27th 2016

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

Instrutores

Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

Sobre University of Washington

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

Sobre o Programa de cursos integrados Machine Learning

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
Machine Learning

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