Portifolio de Algoritmos

Aqui estão alguns dos meus programas em inteligência artificial, machine learning e deep learning.

A idéia é apresentar meus trabalhos e construção de algoritmos|aplicações utilizando as linguagens python. Alguns são escritos da forma mais simples e sem o uso de bibliotecas pronta. Em geral de datasets públicos, e com uso de bibliotecas como Numpy, Pandas, Scikit-learn, CNTK, Keras e Pytorch. É possível ajudar a quem quer obter insights e desenvolver projetos nestas áreas.

• Supervised Learning

  • [Regression Models]
    • Linear Regression
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
  • [Classification Models]
    • Logistic Regression
    • K-Nearest Neighbors
    • Support Vector Machine
    • Kernel Support Vector Machine
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification [(Hyperparameter Tuning)]
    • [LightGBM Classification with Feature Selection and Hyperparameter Tuning]
    • Credit Card Fraud studies
      • [Classification Models results]
      • [LightGBM results with SMOTE]

• Unsupervised Learning

  • Clustering
    • [K-Means Clustering with Principal Component Analysis (PCA) visualization]
    • [Hierarchical Clustering with Dendrogram]
  • Association Rule Learning
    • [Apriori]
    • [Eclat]

• Reinforcement Learning

  • [Upper Confidence Bound]
  • [Thompson Sampling]

• Deep Learning

  • Artifical Neural Network
    • Classification
      • [Geographic Segmentation of Bank data: ANN, Classification Models and HP Tuning]
    • Regression
      • [Combined Cycle Power Plant Data: ANN and Regression Models comparison]
  • Convolutional Neural Network
    • [Cat or Dog: a CNN training]
  • Recurrent Neural Network
    • [Prediction of Financial Stock data with Long-Short Term Memory (LSTM) model]