This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
Este curso faz parte do Programa de cursos integrados Python Data Products for Predictive Analytics
Meaningful Predictive Modeling
oferecido por
Programa de cursos integrados Python Data Products for Predictive AnalyticsUniversidade da Califórnia, San Diego
Informações sobre o curso
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O que você vai aprender
Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).
Evaluate the performance of regressors / classifiers using the above measures.
Understand the difference between training/testing performance, and generalizability.
Understand techniques to avoid overfitting and achieve good generalization performance.
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 10 horas para completar
Sugerido: 4 hours/week
Inglês
Legendas: Inglês
Programa - O que você aprenderá com este curso
2 horas para concluir
Week 1: Diagnostics for Data
For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.
2 horas para concluir
6 vídeos (Total 49 mín.), 4 leituras, 3 testes
6 videos
Motivation Behind the MSE8min
Regression Diagnostics: MSE and R²6min
Over- and Under-Fitting6min
Classification Diagnostics: Accuracy and Error11min
Classification Diagnostics: Precision and Recall12min
4 leituras
Syllabus10min
Setting Up Your System10min
(Optional) Additional Resources and Recommended Readings10min
Course Materials10min
3 exercícios práticos
Review: Regression Diagnostics8min
Review: Classification Diagnostics4min
Diagnostics for Data30min
2 horas para concluir
Week 2: Codebases, Regularization, and Evaluating a Model
This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.
2 horas para concluir
4 vídeos (Total 35 mín.), 4 testes
4 videos
Model Complexity and Regularization10min
Adding a Regularizer to our Model, and Evaluating the Regularized Model8min
Evaluating Classifiers for Ranking4min
4 exercícios práticos
Review: Setting Up a Codebase2min
Review: Regularization5min
Review: Evaluating a Model5min
Codebases, Regularization, and Evaluating a Model45min
1 hora para concluir
Week 3: Validation and Pipelines
This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.
1 hora para concluir
4 vídeos (Total 24 mín.), 3 testes
4 videos
Validation5min
“Theorems” About Training, Testing, and Validation8min
Implementing a Regularization Pipeline in Python5min
Guidelines on the Implementation of Predictive Pipelines5min
3 exercícios práticos
Review: Validation4min
Review: Predictive Pipelines6min
Predictive Pipelines20min
2 horas para concluir
Final Project
In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.
2 horas para concluir
2 leituras, 1 teste
2 leituras
Project Description10min
Where to Find Datasets10min
Instrutores
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
Sobre Universidade da Califórnia, San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Sobre Programa de cursos integrados Python Data Products for Predictive Analytics
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.
This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.
Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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