• Duración:
    6 semanas
  • Dedicación:
    8–10 horas por semana
  • Precio:

    GRATIS
    Agregar un Certificado Verificado por $300 USD

  • Institución
  • Tema:
  • Nivel:
    Advanced
  • Idioma:
    English
  • Transcripción de video:
    English
  • Tipo de curso:
    Al ritmo del instructor

Programas asociados:

Prerrequisitos

You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).

Learners pursuing the MicroMasters programme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python on the verified track prior to undertaking this course.

Sobre este curso

Omitir Sobre este curso

A predictive exercise is not finished when a model is built. This course will equip you with essential skills for understanding performance evaluation metrics, using Python, to determine whether a model is performing adequately.

Specifically, you will learn:

  • Appropriate measures that are used to evaluate predictive models
  • Procedures that are used to ensure that models do not cheat through, for example, overfitting or predicting incorrect distributions
  • The ways that different model evaluation criteria illustrate how one model excels over another and how to identify when to use certain criteria

This is the foundation of optimising successful predictive models. The concepts will be brought together in a comprehensive case study that deals with customer churn. You will be tasked with selecting suitable variables to predict whether a customer will leave a telecommunications provider by looking into their behaviour, creating various models, and benchmarking them by using the appropriate evaluation criteria.

Lo que aprenderás

Omitir Lo que aprenderás

In this course, you will:

  • Analyse the accuracy and quality of a predictive model
  • Implement effective measures and strategies to measure models
  • Evaluate datasets to determine appropriateness and strength of techniques
  • Understand the techniques used in recommender systems

Plan de estudios

Omitir Plan de estudios

Week 1: Evaluation Metrics and Feature Selection
Week 2: Feature Selection and Correlation Analysis
Week 3: Feature Selection with Decomposition Techniques
Week 4: Sampling Techniques
Week 5: Resampling Techniques
Week 6: Case Study

Conoce a tus instructores

Dr Johannes De Smedt
Dixons Carphone Lecturer in Business Analytics
The University of Edinburgh
Obinna Unigwe
Course Tutor
The University of Edinburgh
Sofia Varypati
Course Tutor
University of Edinburgh

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Preguntas frecuentes

What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.

What software will I be required to use?
All coding activities on this course will be hosted on Vocareum. You will be able to access this free software directly within the edX platform. There is no requirement to purchase further software in order to complete this course.

What do I need to complete the course?
For successful completion of this course, you will need access to a computer or mobile device and a reliable internet connection.

What is the University of Edinburgh Accessibility Guidance?

The University of Edinburgh is committed to providing online information and services accessible to all. Edx provide an accessibility statement which is available via the footer of all edx.org pages and includes an 'Accessibility Feedback' form which allows Learners to register feedback directly with the edx. Courses created by the University of Edinburgh contain an Accessibility Statement which addresses equality of access to information and servicesandis available via the 'Support' page.

¿Quién puede hacer este curso?

Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.