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Predictive Analytics using Machine Learning

Learn how to build predictive models using machine learning.

Predictive Analytics using Machine Learning

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Comenzó el 26 oct
Termina el 20 dic
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Comienza el 15 mar 2022
Termina el 8 may 2022
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6 semanas estimadas
8–10 horas por semana
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Verificación opcional disponible
Predictive Analytics using Machine Learning

Sobre este curso

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This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.

The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.

You will also learn:

  • Sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests
  • Support vector machines by introducing you to the concept of optimising the separation between classes, before diving into support vector regression
  • Neural networks; their topology, the concepts of weights, biases, and kernels, and optimisation techniques

De un vistazo

  • Institución: EdinburghX
  • Tema: Análisis de datos
  • Nivel: Advanced
  • 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 MicroMastersprogramme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python and PA1.2x Successfully Evaluating Predictive Modelling and PA1.3x Statistical Predictive Modelling and Applications on the verified track prior to undertaking this course.

Lo que aprenderás

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In this course, you will:

  • Understand the difference between machine learning and other statistical models
  • Practice building tree-based models, support vector machines and neural networks
  • Implement the theoretic models in machine learning-based software packages in Python
  • Apply machine learning models to business situations

Plan de estudios

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Week 1: Decision trees
Week 2: Random forests and support vector machines
Week 3: Support vector machines
Week 4: Neural networks
Week 5: Neural network estimation and pitfalls
Week 6: Model comparison

Acerca de los instructores

Preguntas frecuentes

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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 thiscourse will be hosted onVocareum. You will be able to access this free software directlywithinthe 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 ormobiledevice anda reliableinternet 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 residentes en 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.

Formas de realizar este curso

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Modalidad verificada
Modalidad de auditoría

Costo

300 US$

Gratis

Acceso a los materiales del curso

Ilimitado

Limitado

Caduca el 7 dic

Instituciones y universidades de primera categoría

Asistencia de edX

Certificado para compartir al finalizar

Tareas con calificación y exámenes

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