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]

maiscelo/Machine-learning-Practice-and-Artificial-Inteligence-Algorithms

Portifolio de Algoritmos