Machine Learning Practice

Under this repository, I want to share my journey with Machine Learning Algorithms. The goal is to develop ready-to-use templates for Data Science Projects and present them to other developers. The topics will be shared here:

• 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
• Dimensionality Reduction
  • Principal Component Analysis
    • Customer Segmentation of Wine data with PCA and Classification Models
  • Linear Discriminant Analysis
    • Customer Segmentation of Wine data with LDA and Classification Models
  • Kernel PCA
    • Customer Segmentation of Wine data with Kernel PCA and Classification Models
• Natural Language Processing
  • Bag of words
    • Sentiment Analysis with Classification Models
    • Sentiment Analysis with LigthGBM | XGBoost and Hyperparameter Tuning
• 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: TensorFlow | PyTorch
  • Self Organizing Maps
    • Credit Card Fraud Detection with SOM and ANN
  • Boltzmann Machine
    • Binary Movie Recommendation Model with Restricted Boltzmann Machine
  • Auto-Encoders
    • Stacked Auto-Encoder model for Movie Rating Prediction
• Computer Vision
  • Viola-Jones Algorithm
    • Face and Smile Detection
  • Single-Shot Detector
    • Object Detection with Pytorch
  • General Adversarial Networks
    • Deep Convolutional GANs with CIFAR10 and CelebA datasets