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.

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

• > 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

• > Unsupervised Learning Algorithms

* 1. > Clustering Algorithms * K-Means Clustering * Heirarchical Clustering * 2. > Association Rule Learning * Frequent Itemset Mining / Apriori * Eclat

• > Reinforcement Learning

* > Multi-Armed Bandit * UCB (Upper Confidence Bound) * Thompson Sampling

• > Natural Language Processing

* Simple Sentiment Analysis using NLTK

• > Deep Learning

* Simple Artificial Neural Networks using Keras * Convolutional Neural Networks using Keras

• > 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

• > Model selection, Bagging and Boosting

* Grid Search * K-Fold cross validation * XGBoost