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Machine Learning
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This repo contains the example work of the following:

• Supervised Learning
  • Regression

    • Simple Linear Regression

    • Multiple Linear Regression

    • Polynomial Regression

    • Support Vector Regression (SVR)

    • Decision Tree Regression

    • Random Forest Regression

    • Regression Comparison
      

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• Classification

  • Logistic Regression

  • K-Nearest Neighbors (K-NN)

  • Support Vector Machine (SVM)

  • Kernel SVM

  • Naive Bayes

  • Decision Tree Classification

  • Random Forest Classification

    Classification Comparison
    

    Classification Decision Boundary Comparison
    

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• Unsupervised Learning
  

  • Clustering
    • K-Means Clustering
    • Hierarchical Clustering

  Clustering Comparison
  

  Optimal Comparison
  

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• Association Rule Learning
  
  • Apriori
  • Eclat

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• Reinforcement Learning
  
  • Upper Confidence Bound (UCB)
  • Thompson Sampling

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UCB vs Thompson at iteration of 500

UCB vs Thompson at iteration of 10000

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• Natural Language Processing
  

  • Bag of words (CountVectorizer, Regression)

• CountVectorizer & Prediction(Regression)
  

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Dimensionality Reduction

• Supervised

  • Linear Discriminant Analysis (LDA)

• Unsupervised

  • Principal Component Analysis (PCA)
  • Kernel PCA

Decision Boundary Comparison (Training Set)

Confusion Matrix Comparison & Accuracy Score (Test Set)

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• XGBoost
  
  • XGBoost Classifier

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• Model Validation
  
  • k-Fold Cross Validation
  • Grid Search

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Deep Learning
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• Supervised
  

  • Artificial Neural Networks (ANN)

    • Binary Classification
      

    • Model Performance Scores
      

    • Multiple Classification - Experiments over learning rate parameters
      

    • Overall Classification Performance (Softmax) - Experiments over learning rate parameters
      

    • Experiments over hidden layers parameters
      

    • Experiments over number of units per hidden layer parameters
      

    • Overall Classification Accuracy
      

    • Data Inspection
      

    • Result Correlation
      

  • Fully-connected Feedforward Neural Network (FFN)

    • MNIST Image Data
      

    • Data Inspection
      

    • How data looks like
      

    • Model's Weights Distribution
      

    • Model's Performance Scores
      

  • Convolutional Neural Networks (CNN)
    

    • Feature maps
      1st Convolution layer
      2nd Convolution layer
      

    • Feature maps[occlusion sample]
      Original
      1st Convolution layer
      2nd Convolution layer
      

    • Denoise
      

    • Parameters Predictions
      

    • EMNIST
      

      • EMNIST Predictions
        

    • CIFAR10
      

      • EMNIST Predictions
        

  • Recurrent Neural Networks (RNN)

  • Generative Adversarial Network (GAN)

    • Generated Image
      • FMNIST
        
      • MNIST
        

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• Unsupervised
  

  • Self Organizing Maps
  • Boltzmann Machines
  • AutoEncoders

    • Denoised
      

    • Occlusion
      

    • Occlusion Effect
      

    • Latent Activation
      

    • Latent Values Histogram
      

    • Latent Values PCA
      

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• Math Function
  

  • Sigmoid
    

  • Softmax
    

  • Mean Sample
    

  • Gradient Descent 1D Animated
    

  • Gradient Descent 2D Animated