Este curso faz parte do Programa de cursos integrados Machine Learning

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Programa de cursos integrados Machine Learning

University of Washington

Informações sobre o curso

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Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.
In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.
Learning Outcomes: By the end of this course, you will be able to:
-Describe the input and output of a regression model.
-Compare and contrast bias and variance when modeling data.
-Estimate model parameters using optimization algorithms.
-Tune parameters with cross validation.
-Analyze the performance of the model.
-Describe the notion of sparsity and how LASSO leads to sparse solutions.
-Deploy methods to select between models.
-Exploit the model to form predictions.
-Build a regression model to predict prices using a housing dataset.
-Implement these techniques in Python.

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

Legendas: Inglês, Árabe

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis

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

Legendas: Inglês, Árabe

Semana

1Regression 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....

5 vídeos (total de (Total 20 mín.) min), 3 leituras

Welcome!1min

What is the course about?3min

Outlining the first half of the course5min

Outlining the second half of the course5min

Assumed background4min

Important Update regarding the Machine Learning Specialization10min

Slides presented in this module10min

Reading: Software tools you'll need10min

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....

25 vídeos (total de (Total 122 mín.) min), 5 leituras, 2 testes

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

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

Simple Linear Regression14min

Fitting a simple linear regression model on housing data8min

Semana

2The 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....

19 vídeos (total de (Total 87 mín.) min), 5 leituras, 3 testes

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

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

Multiple Regression18min

Exploring different multiple regression models for house price prediction16min

Implementing gradient descent for multiple regression10min

Semana

3Having 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....

14 vídeos (total de (Total 93 mín.) min), 2 leituras, 2 testes

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

Slides presented in this module10min

Reading: Exploring the bias-variance tradeoff10min

Assessing Performance26min

Exploring the bias-variance tradeoff8min

Semana

4You 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....

16 vídeos (total de (Total 85 mín.) min), 5 leituras, 3 testes

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

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

Ridge Regression18min

Observing effects of L2 penalty in polynomial regression14min

Implementing ridge regression via gradient descent16min

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recebi um aumento ou promoção

por PD•Mar 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 CM•Jan 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!

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....

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....

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