Acerca de este Curso
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Fechas límite flexibles

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Nivel intermedio

Aprox. 17 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

  • Check

    Evaluate the performance of regressors / classifiers using the above measures.

  • Check

    Understand the difference between training/testing performance, and generalizability.

  • Check

    Understand techniques to avoid overfitting and achieve good generalization performance.

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 17 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
2 horas para completar

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.

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6 videos (Total 49 minutos), 4 readings, 3 quizzes
6 videos
Motivation Behind the MSE8m
Regression Diagnostics: MSE and R²6m
Over- and Under-Fitting6m
Classification Diagnostics: Accuracy and Error11m
Classification Diagnostics: Precision and Recall12m
4 lecturas
Syllabus10m
Setting Up Your System10m
(Optional) Additional Resources and Recommended Readings10m
Course Materials10m
3 ejercicios de práctica
Review: Regression Diagnostics8m
Review: Classification Diagnostics4m
Diagnostics for Data30m
Semana
2
2 horas para completar

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.

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4 videos (Total 35 minutos), 4 quizzes
4 videos
Model Complexity and Regularization10m
Adding a Regularizer to our Model, and Evaluating the Regularized Model8m
Evaluating Classifiers for Ranking4m
4 ejercicios de práctica
Review: Setting Up a Codebase2m
Review: Regularization5m
Review: Evaluating a Model5m
Codebases, Regularization, and Evaluating a Model45m
Semana
3
1 hora para completar

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.

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4 videos (Total 24 minutos), 3 quizzes
4 videos
“Theorems” About Training, Testing, and Validation8m
Implementing a Regularization Pipeline in Python5m
Guidelines on the Implementation of Predictive Pipelines5m
3 ejercicios de práctica
Review: Validation4m
Review: Predictive Pipelines6m
Predictive Pipelines20m
Semana
4
2 horas para completar

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 readings, 1 quiz
2 lecturas
Project Description10m
Where to Find Datasets10m

Instructores

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Julian McAuley

Assistant Professor
Computer Science
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Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

Acerca de Universidad de California en 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....

Acerca del programa especializado 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....
Python Data Products for Predictive Analytics

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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