Unsupervised Learning Course in ML + 8 ECTS Credits

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Unsupervised Learning Course in ML + 8 ECTS Credits

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$308.00
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Duration

200 hours

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Credits

8 ECTS

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Language

Spanish

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At the end of the course, you will obtain the accredited qualification

This is not just about learning, but also about accrediting your knowledge with the most important institutions in the sector:

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Non-Supervised Learning Course Qualification in ML with 200 hours and 8 ECTS issued by UTAMED-Atlantic Mediterranean Technological University.

+20 years

boosting learning

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millions of students around the world

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Learn from the best in your sector

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carlos alvarez gonzález

Technical Engineer in Computer Science and teacher in ICT skills, with extensive experience in technical support, implementation of ERP systems and technological training.

Professional experience:

More than 15 years of experience in the technological field as a consultant, analyst-programmer and ICT trainer. Specialized in implementation and support of ERP systems (SAGE), training in digital skills and maintenance of computer systems. Recent experience as a teacher in ICT skills in training projects of the Junta de Andalucía, accumulating more than 600 hours of face-to-face training.

Professional skills:

ERP systems and technical support (SAGE), Training in ICT skills, Computer programming, analysis and maintenance, Quality management (UNE-EN-ISO 9001).

Technical Engineer in Computer Science and teacher in ICT skills, with extensive experience in technical support, implementation of ERP systems and technological training.

Professional experience:

More than 15 years of experience in the technological field as a consultant, analyst-programmer and ICT trainer. Specialized in implementation and support of ERP systems (SAGE), training in digital skills and maintenance of computer systems. Recent experience as a teacher in ICT skills in training projects of the Junta de Andalucía, accumulating more than 600 hours of face-to-face training.

Professional skills:

ERP systems and technical support (SAGE), Training in ICT skills, Computer programming, analysis and maintenance, Quality management (UNE-EN-ISO 9001).

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Unsupervised Learning Course in ML + 8 ECTS Credits

Información adicional del UNIVERSITY COURSE

Unsupervised Learning Course in ML + 8 ECTS Credits

6 months de tutorización

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Course information

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Course information

Description
The Unsupervised Learning in ML Course is your gateway to a world of opportunities in the burgeoning field of machine learning. With the growing demand for experts capable of interpreting large volumes of data, the skills acquired in this course will position you at the forefront of technological innovation. You will learn to master essential techniques such as clustering, dimensionality reduction and generative models, essential tools in detecting patterns and anomalies in complex data. This online training offers you the flexibility to learn at your own pace, adapting to your needs. Join this course and transform your professional future with skills that are highly valued in today's job market.

Job opportunities

The main professional opportunities of this Unsupervised Learning Course in ML are in areas such as data analysis to detect hidden patterns and anomalies, artificial intelligence consulting for business process optimization, data science in technology companies focused on predictive analysis, development of clustering models, among others.

What does it prepare you for?

This Unsupervised Learning in ML Course prepares you to address complex unsupervised learning problems in machine learning. Aprenderás a realizar preprocesamiento de datos, reducir dimensionalidad con PCA, y utilizar modelos generativos y autoencoders. You will also develop skills in anomaly detection and model evaluation, allowing you to implement effective solutions.

Who is it addressed to?

This Unsupervised Learning Course in ML is aimed at professionals and graduates from the technological, scientific or engineering sector who wish to delve deeper into unsupervised learning. Suitable for those interested in techniques such as clustering and dimensionality reduction, the course offers practical and theoretical training to apply these methods in real projects.

Objectives

- Understand the concept of unsupervised learning and its applications in real problems. - Identify and prepare data to apply unsupervised models effectively. - Implement basic clustering techniques such as K-Means in data sets. - Apply advanced clustering methods to improve data segmentation. - Use dimensionality reduction techniques such as PCA to simplify complex data. - Explore generative models and autoencoders to generate and transform data. - Evaluate the effectiveness of unsupervised models using specific metrics.

Unsupervised Learning Course in ML + 8 ECTS Credits

$308.00