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Machine Learning Training

Machine Learning Training

StratebiMadrid, Comunidad de Madrid, España
Hace más de 30 días
Descripción del trabajo

Before organising a course or seminar, we listen to the real needs and objectives of each client, in order to adapt the training and get the most out of it. We tailor each course to your needs.

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.

Goal

This course will understand the concepts needed to perform processes in Machine Learning, a 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, and Spam Detection.

Target Audience

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

    1. Introduction to Machine Learning

  • Classification
  • Regression
  • Preprocessing and dimensional reduction
  • Performance evaluation
  • Matrices de confusión

  • KPIs R2, MAE, MSE
  • 2. Regression (Prediction of continuous values)

  • Algorithms
  • Ordinary Least Squares

  • Ridge Regression
  • Lasso Regression
  • Elastic Net
  • Examples
  • 3. Classification (Identification of the category to which an object belongs)

  • Algorithms
  • Logistic Regression

  • Support Vector Machines
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forest
  • Multi-layer Perceptron
  • Examples
  • 4. Clustering (Grouping similar objects in sets)

    Schedule

    16 : 00H - 21 : 00H (CEST - Madrid)

    9 : 00H - 14 : 00H and 15 : 30H - 18 : 30H

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    Machine Learning • Madrid, Comunidad de Madrid, España