Machine-Learning-Practicals

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

some topics are listed below which are implemented 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.