aligeekk / ml-algorithms-on-scikit-and-keras

Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..

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

   • 2. 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

   • 2. 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