It is not a matter of language, it is the technique, the method and practice applied to the data.

This course challenges developers to understand the value that can be derived from incorporating algorithms from Machine Learning and encourages them in a practical way to implement programs capable of learning data, through specific examples scheduled with Java language-based tools.

The course is designed for software developers who use Java as a main language in their day-to-day can understand the potential and logic behind classification, regression and grouping algorithms without friction to learn a different language at the same time.This with the aim of understanding the basic concepts and basics of Machine Learning from a practical perspective.

·Who is going:

This course is aimed at software developers who use Java as a main language on their day to day.

· Pre-requirements:

Needed:

• Knowledge in Java Language

Recommended:

• Knowledge in statistics

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· Training content:

• Introduction to Machine Learning
  • What is the Machine Learning?
  • Uses of Machine Learning in the real world
  • Types of Learning
  • Supervised learning Non-supervised learning Reinforced learning
  • What is a data problem?
  • Data preparation
  • Selection of models
  • Validation of models
  • Tools and forms of deployment in Java
  • Programming my first ML model (KNN) in Java
• Classification models
  • Decision trees
  • Naive Bayes.
  • Vector support machines
• Models for text comparison
  • Understanding the distance metrics.
  • Search methods of similarity in chains
  • Distance from Levenshtein
  • Distance Jaro-Winkler
  • N-grams and sewing similarity
  • Creation of Bag of Words
  • Classification of feelings in texts with Naive Bayes
• Regression models
  • Linear regression
  • Gaussian regression
  • Regression with Pettoria Support Machines
  • Regression perceptron multilayer
• Grouping models
  • Dimensional reduction
  • Model K-Means
  • Cobweb model