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University Microcredential for Supervised Learning in ML + 2 ECTS Credits

Name: University Microcredential for Supervised Learning in ML + 2 ECTS Credits en Estados Unidos
Brand: EducaHub
SKU: 419088-2501
Availability: InStock

$70.00

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Duration

50 hours

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Credits

2 ECTS

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Language

Spanish

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Credit your success

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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Supervised Learning Microcredential Degree in ML with 50 hours and 2 ECTS issued by UTAMED-Atlantic Mediterranean Technological University

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Learn from the best in your sector

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).

Información adicional del MICRECREDENTIALS

University Microcredential for Supervised Learning in ML + 2 ECTS Credits

6 months de tutorización

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Course information

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Supervised Learning in ML is your gateway to a field in constant expansion and increasing labor demand. In a world where data is the new gold, knowing how to manage it and extract value from it is essential. You will learn from the fundamentals of supervised learning, through data preparation and feature engineering, to advanced models such as Support Vector Machines and assembly techniques such as Random Forest and Gradient Boosting. You will acquire skills to evaluate and optimize models, from linear regression to complex ensemble systems. This training gives you the necessary tools to become an expert in creating intelligent and effective solutions, all in a flexible and online way.

Supervised Learning in ML offers a multitude of job opportunities, including machine learning engineer in technology companies, data analyst specialized in predictive models, data scientist for optimization of industrial processes, or artificial intelligence consultant for the financial sector.

The Supervised Learning in ML training prepares you to address complex classification and regression problems through the use of advanced techniques and ensemble models. You will learn how to clean and prepare data, selecting relevant features and applying dimensionality reduction techniques such as PCA. You will be able to implement models from linear regression to advanced techniques such as SVM and Random Forest, optimizing their performance.

The Supervised Learning in ML training is aimed at professionals and graduates in the technology and scientific sector who want to delve deeper into the fundamentals of supervised learning, including data preparation and feature engineering, as well as explore basic and advanced models such as SVM and Random Forest, all with a practical and accessible approach.

- Understand the context and application of supervised learning compared to other paradigms. - Differentiate between classification and regression problems in supervised learning. - Evaluate supervised learning models using key metrics and cross-validation. - Apply data cleaning and coding techniques to improve its quality. - Implement selection and extraction techniques of relevant features. - Build basic models such as linear regression and decision trees effectively. - Optimize advanced and assembly models through hyperparameter tuning.

University Microcredential for Supervised Learning in ML + 2 ECTS Credits

$70.00

University Microcredential for Supervised Learning in ML + 2 ECTS Credits

Agenda

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

Learn from the best in your sector

Course information

Course information

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