Supervised Learning Course in ML + 8 ECTS Credits

UNIVERSITY COURSE 100% On-line

Supervised Learning Course in ML + 8 ECTS Credits

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$303.00
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Duration

200 hours

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Credits

8 ECTS

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Language

Spanish

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No limit

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Agenda

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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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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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+2M

millions of students around the world

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Scholarships and funding

Make your training more accessible: finance at 0% interest and obtain personalized scholarships.

Scholarships of up to 25%

+500

specialized courses in EducaHub

Cloister

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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Course information

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Course information

Description
The Supervised Learning Course in ML is designed so that you acquire the necessary skills in a booming sector, where the demand for trained professionals does not stop growing. This course will provide you with a solid foundation in regression, classification and model evaluation techniques, essential for any aspiring data scientist. You will learn from the fundamentals of machine learning to advanced methods such as neural networks and support vector machines, ensuring you are prepared to meet today's challenges in the world of work. This online course offers flexibility and accessibility, allowing you to progress at your own pace as you become a supervised learning professional.

Job opportunities

The main professional opportunities of the Supervised Learning Course in ML are in areas such as data science focused on predictive analysis, machine learning engineering in technology companies, data analysis for process optimization, artificial intelligence consulting for model development, data analysis in financial and marketing sectors, etc.

What does it prepare you for?

The Supervised Learning Course in ML prepares you to address complex data analysis problems using advanced machine learning techniques. You will learn from data preparation and preprocessing to the application of regression and classification algorithms. You will master models such as decision trees and neural networks, evaluating their effectiveness with cross-validation, among other things.

Who is it addressed to?

The Supervised Learning Course in ML is designed for professionals and graduates who want to delve deeper into machine learning techniques. It covers everything from the introduction to supervised learning to the application of neural networks, including linear regression, decision trees and SVM, providing essential tools for data analysis and modeling.

Objectives

- Understand the fundamentals of supervised learning in machine learning. - Learn to prepare and preprocess data for machine learning models. - Apply linear regression to solve prediction problems. - Use logistic regression in binary classification tasks. - Evaluate models using cross-validation techniques. - Implement decision trees and ensembles such as Random Forest. - Develop neural networks focused on supervised learning.

Supervised Learning Course in ML + 8 ECTS Credits

$303.00

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