02-07-2024, 10:44 PM
[center]![[Image: maxresdefault.jpg]](https://i.postimg.cc/RF31K4N2/maxresdefault.jpg)
Published 1/2024
Created by Bruno Leite
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 65 Lectures ( 4h 30m ) | Size: 2.1 GB
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[center]
Formulate real-world problems as mathematical optimization models and solve them using Python
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What you'll learn:
Gain proficiency in solving optimization problems using popular solvers, and learn to interpret and implement their results effectively
Learn and apply useful modeling techniques to classical operations research problems
Identify and formulate real-world problems as numerical optimization models
Complete a case study on how to combine operations research and software engineering to build powerful solutions
Requirements:
Basic programming
No previous experience with optimization solvers is required
Student might have a better understanding of some sections if familiar with discrete mathematics and linear algebra
Description:
Numerical Optimization and Operations Research in PythonLearn how to use data efficiently to support decision-making by using numerical optimization and operations research with this comprehensive course. It successfully combines theoretical foundations and practical applications, designed to empower you with the skills needed to tackle complex optimization problems in a professional or academic context.You will learn:Theory
rinciples of Mathematical OptimizationLinear programming (LP)Integer and Mixed-integer linear programming (MILP)Handle infeasible scenariosMulti-objective hierarchical (lexicographic) formulationsConstructive Heuristics and Local SearchSoftwareyomoGoogle OR-ToolsHiGHSStreamlitProblems:KnapsackProduct-MixTransportationLot-SizingJob-Shop SchedulingFacility DispersionTraveling SalesmanCapacitated Vehicle Routing ProblemIndustry-Grade Skills: By the end of this course, you'll have the skills to formulate and solve your own optimization problems, a highly sought-after competency in industries ranging from logistics to finance. You'll also be able to convert your models into scalable applications for your company or team even though they are not familiar with optimization.Who is this course for?Data scientists and engineers who want to add optimization skills to their toolkit.Professionals in logistics, supply chain management, or finance, who are looking to leverage optimization for decision-making.Academics and students seeking a practical application of operations research and optimization theories.Course Features:More than 4 hours of comprehensive video lectures explaining concepts in a clear and engaging manner.13+ Interactive Python notebooks for hands-on practice (and corresponding solutions).Carefully selected articles and external references to improve your learning experience.Access to a community forum for discussion and networking with fellow learners.Lifetime access to course materials, including future updates.Embark on this journey to master decision-making using optimization in Python. Whether you aim to advance your career, academically explore operations research, or simply enjoy the thrill of solving complex problems, this course is your gateway to new possibilities.
Who this course is for:
Professionals in pursuit of essential quantitative decision-making skills
Academics eager to learn practical software skills to apply optimization theory
Homepage
Published 1/2024
Created by Bruno Leite
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 65 Lectures ( 4h 30m ) | Size: 2.1 GB
[/center]
[center]
Formulate real-world problems as mathematical optimization models and solve them using Python
[/center]
What you'll learn:
Gain proficiency in solving optimization problems using popular solvers, and learn to interpret and implement their results effectively
Learn and apply useful modeling techniques to classical operations research problems
Identify and formulate real-world problems as numerical optimization models
Complete a case study on how to combine operations research and software engineering to build powerful solutions
Requirements:
Basic programming
No previous experience with optimization solvers is required
Student might have a better understanding of some sections if familiar with discrete mathematics and linear algebra
Description:
Numerical Optimization and Operations Research in PythonLearn how to use data efficiently to support decision-making by using numerical optimization and operations research with this comprehensive course. It successfully combines theoretical foundations and practical applications, designed to empower you with the skills needed to tackle complex optimization problems in a professional or academic context.You will learn:Theory
rinciples of Mathematical OptimizationLinear programming (LP)Integer and Mixed-integer linear programming (MILP)Handle infeasible scenariosMulti-objective hierarchical (lexicographic) formulationsConstructive Heuristics and Local SearchSoftwareyomoGoogle OR-ToolsHiGHSStreamlitProblems:KnapsackProduct-MixTransportationLot-SizingJob-Shop SchedulingFacility DispersionTraveling SalesmanCapacitated Vehicle Routing ProblemIndustry-Grade Skills: By the end of this course, you'll have the skills to formulate and solve your own optimization problems, a highly sought-after competency in industries ranging from logistics to finance. You'll also be able to convert your models into scalable applications for your company or team even though they are not familiar with optimization.Who is this course for?Data scientists and engineers who want to add optimization skills to their toolkit.Professionals in logistics, supply chain management, or finance, who are looking to leverage optimization for decision-making.Academics and students seeking a practical application of operations research and optimization theories.Course Features:More than 4 hours of comprehensive video lectures explaining concepts in a clear and engaging manner.13+ Interactive Python notebooks for hands-on practice (and corresponding solutions).Carefully selected articles and external references to improve your learning experience.Access to a community forum for discussion and networking with fellow learners.Lifetime access to course materials, including future updates.Embark on this journey to master decision-making using optimization in Python. Whether you aim to advance your career, academically explore operations research, or simply enjoy the thrill of solving complex problems, this course is your gateway to new possibilities.Who this course is for:
Professionals in pursuit of essential quantitative decision-making skills
Academics eager to learn practical software skills to apply optimization theory
Homepage
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