Machine Learning Especialist

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 Machine LearningEspecialistMódulo 1Introdução1. Introdução ao Machine Learnig2. Métodos de Machile Learning bioinspirados3. Redes neurais artificiais4. Algoritmos Genéticos5. Algoritmos de SVMMáquina de vetores de Suporte6. Criação e utilização de DatasetsMódulo 2Programação para ML1. Linguagens de programação para ML2. Python para Machine Learning3. Scilab (Mathlab) para MachineLearning4. R para Machine Learning5. Instalação de bibliotecas e dependências6. Plataformas de colaboração: GitHub e ColabMódulo 3Algoritmos de treinamento1. Treinamento supervisionado2. Treinamento não-supervisionado3. Algoritmos de classificação4. Algoritmos de regressão5. Algoritmos de clusterização6. Extração de features e redução e redução de dimensionamentoMódulo 4Teoria do aprendizado estatístico1. Método de validação2. Métodos de otimização3. Otimização de modelos em hiperparâmetros4. Métricas de avaliação de desempenhoMódulo 5Deep Learning1. Tipos de redes de Deep Learning2. Redes de classificação3. Redes de detecção4. Redes de segmentação5. Aplicação de Deep LearningMódulo 6Deep Learning Frameworks1. Bibliotecas de Machine Learning2. Bibliotecas Pandas e Scikit-Learning3. Frameworks de Machine Learning4. Desenvolvimento de algoritmos no TensorFlow5. Desenvolvimento de algoritmos no Keras6. Ambientes de projetos colaborativosMódulo 7Processamento de imagens1. Introdução ao processamento de imagens2. Métodos de processamentos de imagens3. Segmentação de imagens4. Programação com OpenCV5. Filtros de detecção de bordas6. Filtros de eliminação de ruídosMódulo 8Visão computacional com Machine Learning1. Introdução à visão computacional2. Algoritmos de rastreamento de objetos3. Algoritmos de reconhecimento de objetos4. Algoritmos de visão em imagens 2D e 3D5. Implementação e testes de algoritmos de visãoAprendizado por reforçoAlgoritmos determinísticosAlgoritmos heurísticosLógica booleanaLógica difusa(fuzzy)Algoritmos de colônia deformigas ou abelhasRoteamento emrobóticaRoteamento emredesAlgoritmo supervisionadoAlgoritmo de classificaçãoAlgoritmo de detecçãoSegmentação de imagens

Excalidraw Data

Text Elements

Machine Learning
Especialist

Módulo 1
Introdução

  1. Introdução ao Machine Learnig

  2. Métodos de Machile Learning bioinspirados

  3. Redes neurais artificiais

  4. Algoritmos Genéticos

  5. Algoritmos de SVM
    Máquina de vetores de Suporte

  6. Criação e utilização de Datasets

Módulo 2
Programação para ML

  1. Linguagens de programação para ML

  2. Python para Machine Learning

  3. Scilab (Mathlab) para Machine Learning

  4. R para Machine Learning

  5. Instalação de bibliotecas e dependências

  6. Plataformas de colaboração: GitHub e Colab

Módulo 3
Algoritmos de treinamento

  1. Treinamento supervisionado

  2. Treinamento não-supervisionado

  3. Algoritmos de classificação

  4. Algoritmos de regressão

  5. Algoritmos de clusterização

  6. Extração de features e redução e redução de dimensionamento

Módulo 4
Teoria do aprendizado estatístico

  1. Método de validação

  2. Métodos de otimização

  3. Otimização de modelos em hiperparâmetros

  4. Métricas de avaliação de desempenho

Módulo 5
Deep Learning

  1. Tipos de redes de Deep Learning

  2. Redes de classificação

  3. Redes de detecção

  4. Redes de segmentação

  5. Aplicação de Deep Learning

Módulo 6
Deep Learning Frameworks

  1. Bibliotecas de Machine Learning

  2. Bibliotecas Pandas e Scikit-Learning

  3. Frameworks de Machine Learning

  4. Desenvolvimento de algoritmos no TensorFlow

  5. Desenvolvimento de algoritmos no Keras

  6. Ambientes de projetos colaborativos

Módulo 7
Processamento de imagens

  1. Introdução ao processamento de imagens

  2. Métodos de processamentos de imagens

  3. Segmentação de imagens

  4. Programação com OpenCV

  5. Filtros de detecção de bordas

  6. Filtros de eliminação de ruídos

Módulo 8
Visão computacional com Machine Learning

  1. Introdução à visão computacional

  2. Algoritmos de rastreamento de objetos

  3. Algoritmos de reconhecimento de objetos

  4. Algoritmos de visão em imagens 2D e 3D

  5. Implementação e testes de algoritmos de visão

Aprendizado por reforço

Algoritmos determinísticos

Algoritmos heurísticos

Lógica booleana

Lógica difusa (fuzzy)

Algoritmos de colônia de formigas ou abelhas

Roteamento em robótica

Roteamento em redes

Algoritmo supervisionado

Algoritmo de classificação

Algoritmo de detecção

Segmentação de imagens