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Machine-Learning-Algorithms-with-Scikit-Learn-and-Keras

This repository contains various Machine Learning Algorithms implemented in Scikit-Learn.

Machine Learning Algorithms such as Supervised, Unsupervised, Simple Reinforcement Learning, Sentiment analysis in Natural-Language-Processing, Supervised simple Deep Learning Algorithms, Dimensionality Reduction, Bagging, Boosting etc. are implemented in Scikit-Learn and Keras.

  1. Numpy, Pandas, Matplotlib Tutorials Pdf's and implementation in Notebook files .

  2. Supervised Learning Algorithms

      1. Regression Algorithms

        • Linear Regression
        • Multivariate Linear Regression
        • Polynomial Regression
        • Support Vector Machines
        • Decision Trees
        • Random Forest
        • Evaluating Regression Models using Regularization
      1. Classification Algorithms

        • Logistic Regression
        • K-Nearest Neighbour
        • Support Vector Machines
        • Kernel Support Vector Machines
        • Naive Bayes
        • Decision Trees
        • Random Forest
        • Evaluating Classification Models
  3. Unsupervised Learning Algorithms

      1. Clustering Algorithms

        • K-Means Clustering
        • Heirarchical Clustering
      1. Association Rule Learning

        • Frequent Itemset Mining / Apriori
        • Eclat
  4. Reinforcement Learning

    • Multi-Armed Bandit

      • UCB (Upper Confidence Bound)
      • Thompson Sampling
  5. Natural Language Processing

    • Simple Sentiment Analysis using NLTK
  6. Deep Learning

    • Simple Artificial Neural Networks using Keras
    • Convolutional Neural Networks using Keras
  7. Dimensionality Reduction

    • t-SNE (Implemented in Section - 1 : Numpy, Pandas, Matplotlib and others.ipynb)
    • Principal Component Analysis (PCA)
    • Linear Discriminant Analysis (LDA)
    • Kernel Pricipal Component Analysis
  8. Model selection, Bagging and Boosting

    • Grid Search
    • K-Fold cross validation
    • XGBoost