Machine Learning

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We are also specialists in 'in company' trainings adapted to the needs of each organisation, where the benefit for several attendees from the same company is much greater. If this is your case, contact us.

Calls for course

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Ofrecemos también la posibilidad de realizar formaciones en base a ‘Casos de Uso’

Se complementa la formación tradicional de un temario/horas/profesor con la realización de casos prácticos en las semanas posteriores al curso en base a datos reales de la propia organización, de forma que se puedan ir poniendo en producción proyectos iniciales con nuestro soporte, apoyo al desarrollo y revisión con los alumnos y equipos, etc…

En los 10 últimos años, ¡hemos formado a más de 250 organizaciones y 3.000 alumnos!

Ah, y regalamos nuestras famosas camisetas de Data Ninjas a todos los asistentes. No te quedes si las tuyas

Goal

This course will understand the concepts needed to perform processes Machine Learning, this branch of artificial intelligence that aims to develop techniques that allow computers to learn.

Machine Learning projects create algorithms that can generalize and recognize behavior patterns from information provided by way of example ( training). Machine Learning techniques are used among others in the following areas: Medicine, Bioinformatics, Marketing, Natural Language Processing, Image Processing, Machine Vision, Spam Detection.

Machine Learning

Target audiences

ICT professionals: Consultants BI, Scientific Data.
Professionals of Applied Sciences: Mathematics, Statistics, Physics.

Observations

Methodology: The course intersperses theoretical parts where fundamental concepts are taught to understand the practical exercises taught.
Requirements: Basics: Linear Algebra, calculus and probability theory.

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Syllabus

Machine Learning with Scikit-Learn Data Science framework (Anaconda with Python 3)

1. Introduction to Machine Learning

Techniques
- Classification
- Regression
- Clustering
Preprocessing and dimensional reduction
Attribute selection
Performance evaluation
- Matrices de confusión
- KPIs R2, MAE, MSE

2. Regression (Prediction of continuous values)

Algorithms
- Ordinal Least Squares
- Ridge Regression
- Laso Regression
- Elastic Net
Examples

3. Classification (Identification of the category to which an object belongs)

Algorithms
- Logistic Regression
- Support Vector Machines
- KNearest Neighbors
- Decision Trees
- Random Forest
- Multi-layer Perceptron
Examples

4. Clustering (Grouping similar objects in sets)

Algorithms
- KMeans
- Spectral Clustering
- DBSCAN
Examples

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