Project Description
Machine Learning
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
Continued research into deep learning and AI is increasingly focused on developing more general applications. Today’s AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future, different tasks.This internship provides an overview to understand Machine learning and how it is developing with time. The guide aims at introducing the fundamentals of Machine Learning its practical applications and working. The student will gain knowledge through handson session, under the direction of Industry expert Trainers.
 What is Machine Learning
 Install Python and Anaconda.
 Installing packages: numpy, pandas, matplotlib, sklearn)
 Introduction to Python
 Flow Control (If, for, while) Statements
 Data Structures
 Numbers
 Lists
 Tuples
 Dictionary
 Strings
 Functions and classes in Python
 Ndarray Object
 Data Types in Numpy
 Array Attributes and Manipulation in Numpy
 Indexing & Slicing

 Regression
 Simple Linear Regression
 Multiple Linear Regression
 Support Vector Regression
 Decision Tree Regression
 Random Forest Regression
 Classification
 Support Vector Classification(SVM)
 K – Nearest Neighbour Algorithm(KNN)
 Naive Bayes Classification
 Decision Tree Classification
 Random forest Classification
 Clustering
 Kmeans Clustering Algorithm
 Hierarchical clustering
 Regression