This repository contains all the practicals of ML which are performed during labs.

01. Introduction to Machine Learning Libraries in Python

Objective : To introduce different machine learning libraries available in python.

02. Data Preprocessing and Feature Selection

Objective : To perform various pre-processing operations on data.

03. NB Classifier Weather

Objective : Implement Naive Bayes classifier : Weather Example

04. Linear Regression

Objective : Implement Linear Regression Algorithm on the given dataset

05. Decision Tree Learning

Objective : To implement Decision Tree Algorithm using Scikit Learn Library and understand its working.

06. Tweet Sentiment Analysis using Bayesian Network

Objective : Perform Sentiment Analysis on Twitter Dataset using Bayesian Network

07. Logistic Regression

Objective : Implement Logistic Regression Algorithm on the given dataset

08. Ensemble Learning

Objective : Implement Ensemble Learning Algorithms on the given dataset

09. KMeans Clustering

Objective : Implement K Means Clustering Algorithm on the given dataset

10. Eigenfaces PCA SVM

Objective : chaining PCA and Naive Bayes Clas-sifier (SVMs)

11. Support Vector Machine

Objective : To implement SVM using scikit-learn library and train it to classify the given dataset.

12. Artificial Neural Network

Objective : To build an ANN model for performing classification on a given dataset.