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Watch this tutorial on Linear Programming in Data Science! Linear Programming is a unique case of mathematical programming wherein the best outcome to a mathematical problem is achieved whose requirements depend on linear relationships. Also known as linear optimization, linear programming is one of the easiest methods to carry out optimization. Its applications include shelf-space optimization, supply chain optimization, optimization of delivery routes, and its use in Machine Learning.

Great Learning brings you this tutorial on Linear Programming in Data Science to help you understand everything you need to know about this topic and getting started on the journey to learn about it well. This video starts off with an introduction to linear programming, followed by understanding the graphical method and working on an LP problem in the excel solver. We will learn about sensitivity analysis and assumptions in linear programming. We will also be looking at multiple case studies to implement our learning. This video explains Linear Progression for Data Science with a variety of demonstrations & examples to help you learn this topic effectively and efficiently.

0:00:00 Introduction
0:02:51 Agenda
0:04:17 Introduction to Linear Programming
0:07:04 Applications of Linear Programming
0:11:02 Linear Programming - Approach
0:14:21 Graphical Method
0:38:21 LP Problem in Excel Solver
1:06:57 Sensitivity Analysis
1:22:19 Case Study - Investment Problem
1:44:10 Case Study - Portfolio Optimization
2:05:12 Assumptions in Linear Programming
2:14:23 Case Study - Recruitment Planning

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