À propos de ce cours
4.8
3,899 notes
756 avis
Spécialisation
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Heures pour terminer

Approx. 27 heures pour terminer

Recommandé : 6 weeks of study, 5-8 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais, Arabe

Compétences que vous acquerrez

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis
Spécialisation
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Heures pour terminer

Approx. 27 heures pour terminer

Recommandé : 6 weeks of study, 5-8 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais, Arabe

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
1 heure pour terminer

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 vidéos (Total 20 min), 3 lectures
Video5 vidéos
What is the course about?3 min
Outlining the first half of the course5 min
Outlining the second half of the course5 min
Assumed background4 min
Reading3 lectures
Important Update regarding the Machine Learning Specialization10 min
Slides presented in this module10 min
Reading: Software tools you'll need10 min
Heures pour terminer
3 heures pour terminer

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 vidéos (Total 122 min), 5 lectures, 2 quiz
Video25 vidéos
Regression fundamentals: data & model8 min
Regression fundamentals: the task2 min
Regression ML block diagram4 min
The simple linear regression model2 min
The cost of using a given line6 min
Using the fitted line6 min
Interpreting the fitted line6 min
Defining our least squares optimization objective3 min
Finding maxima or minima analytically7 min
Maximizing a 1d function: a worked example2 min
Finding the max via hill climbing6 min
Finding the min via hill descent3 min
Choosing stepsize and convergence criteria6 min
Gradients: derivatives in multiple dimensions5 min
Gradient descent: multidimensional hill descent6 min
Computing the gradient of RSS7 min
Approach 1: closed-form solution5 min
Approach 2: gradient descent7 min
Comparing the approaches1 min
Influence of high leverage points: exploring the data4 min
Influence of high leverage points: removing Center City7 min
Influence of high leverage points: removing high-end towns3 min
Asymmetric cost functions3 min
A brief recap1 min
Reading5 lectures
Slides presented in this module10 min
Optional reading: worked-out example for closed-form solution10 min
Optional reading: worked-out example for gradient descent10 min
Download notebooks to follow along10 min
Reading: Fitting a simple linear regression model on housing data10 min
Quiz2 exercices pour s'entraîner
Simple Linear Regression14 min
Fitting a simple linear regression model on housing data8 min
Semaine
2
Heures pour terminer
3 heures pour terminer

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 vidéos (Total 87 min), 5 lectures, 3 quiz
Video19 vidéos
Polynomial regression3 min
Modeling seasonality8 min
Where we see seasonality3 min
Regression with general features of 1 input2 min
Motivating the use of multiple inputs4 min
Defining notation3 min
Regression with features of multiple inputs3 min
Interpreting the multiple regression fit7 min
Rewriting the single observation model in vector notation6 min
Rewriting the model for all observations in matrix notation4 min
Computing the cost of a D-dimensional curve9 min
Computing the gradient of RSS3 min
Approach 1: closed-form solution3 min
Discussing the closed-form solution4 min
Approach 2: gradient descent2 min
Feature-by-feature update9 min
Algorithmic summary of gradient descent approach4 min
A brief recap1 min
Reading5 lectures
Slides presented in this module10 min
Optional reading: review of matrix algebra10 min
Reading: Exploring different multiple regression models for house price prediction10 min
Numpy tutorial10 min
Reading: Implementing gradient descent for multiple regression10 min
Quiz3 exercices pour s'entraîner
Multiple Regression18 min
Exploring different multiple regression models for house price prediction16 min
Implementing gradient descent for multiple regression10 min
Semaine
3
Heures pour terminer
2 heures pour terminer

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 vidéos (Total 93 min), 2 lectures, 2 quiz
Video14 vidéos
What do we mean by "loss"?4 min
Training error: assessing loss on the training set7 min
Generalization error: what we really want8 min
Test error: what we can actually compute4 min
Defining overfitting2 min
Training/test split1 min
Irreducible error and bias6 min
Variance and the bias-variance tradeoff6 min
Error vs. amount of data6 min
Formally defining the 3 sources of error14 min
Formally deriving why 3 sources of error20 min
Training/validation/test split for model selection, fitting, and assessment7 min
A brief recap1 min
Reading2 lectures
Slides presented in this module10 min
Reading: Exploring the bias-variance tradeoff10 min
Quiz2 exercices pour s'entraîner
Assessing Performance26 min
Exploring the bias-variance tradeoff8 min
Semaine
4
Heures pour terminer
3 heures pour terminer

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 vidéos (Total 85 min), 5 lectures, 3 quiz
Video16 vidéos
Overfitting demo7 min
Overfitting for more general multiple regression models3 min
Balancing fit and magnitude of coefficients7 min
The resulting ridge objective and its extreme solutions5 min
How ridge regression balances bias and variance1 min
Ridge regression demo9 min
The ridge coefficient path4 min
Computing the gradient of the ridge objective5 min
Approach 1: closed-form solution6 min
Discussing the closed-form solution5 min
Approach 2: gradient descent9 min
Selecting tuning parameters via cross validation3 min
K-fold cross validation5 min
How to handle the intercept6 min
A brief recap1 min
Reading5 lectures
Slides presented in this module10 min
Download the notebook and follow along10 min
Download the notebook and follow along10 min
Reading: Observing effects of L2 penalty in polynomial regression10 min
Reading: Implementing ridge regression via gradient descent10 min
Quiz3 exercices pour s'entraîner
Ridge Regression18 min
Observing effects of L2 penalty in polynomial regression14 min
Implementing ridge regression via gradient descent16 min
4.8
756 avisChevron Right
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38%

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Meilleurs avis

par 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!

par 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!

Enseignants

Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics
Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

À propos de 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....

À propos de la Spécialisation 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

Foire Aux Questions

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