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Optimization Masterclass : Python, Julia, Matlab, R
Rating: 3.4 out of 5(6 ratings)
84 students

Optimization Masterclass : Python, Julia, Matlab, R

Master Optimization Algorithms with Python, Julia, MATLAB & R – Linear, Integer, Nonlinear & Metaheuristic Methods
Created byAdvancedor Academy
Last updated 11/2025
English

What you'll learn

  • nderstand fundamental optimization techniques, including Linear Programming (LP), Integer Programming (IP), and Nonlinear Programming
  • Develop practical coding skills by implementing optimization algorithms in Python, Julia, MATLAB, and R to solve complex decision-making problems
  • Explore and apply metaheuristic optimization methods such as Particle Swarm Optimization (PSO), Simulated Annealing, and Ant Colony Optimization
  • Integrate optimization techniques with machine learning and stochastic methods to enhance decision-making processes in industries such as finance, logistics

Course content

17 sections • 69 lectures • 16h 7m total length

  • Introduction3:39

    Explore optimization algorithms from linear and integer programming to metaheuristics and stochastic methods, with hands-on coding in Python, Julia, Matlab, and R, applied to real world scenarios.

  • Before The Course5:15
  • Ratings2:54

    This lecture urges you to withhold course ratings until you’ve completed half the content to judge it fairly. It also explains the slower, deliberate delivery for an international audience.

Requirements

  • A basic understanding of programming concepts will be helpful but is not required.
  • Familiarity with basic mathematics and linear algebra will make it easier to grasp optimization concepts, but I will explain everything in a way that is accessible to all learners.
  • No prior knowledge of optimization is necessary—you’ll learn everything step by step.

Description

Optimization is at the core of decision-making in engineering, business, finance, artificial intelligence, and operations research. If you want to solve complex problems efficiently, understanding optimization algorithms is essential.

This course provides a thorough understanding of optimization techniques, from fundamental methods like Linear Programming (LP) and Integer Programming (IP) to advanced metaheuristic algorithms such as Particle Swarm Optimization (PSO), Simulated Annealing, and Ant Colony Optimization. We will implement these techniques using Python, Julia, MATLAB, and R, ensuring you can apply them across different platforms.

Throughout the course, we will work with real-world optimization problems, covering essential topics like the Traveling Salesman Problem, Portfolio Optimization, Job Shop Scheduling, and more. You will gain hands-on experience with numerical optimization, stochastic optimization, and machine learning-based approaches.

We will also explore key mathematical concepts behind optimization and discuss how these methods are applied across different industries. Whether you are an engineer, data scientist, researcher, or analyst, this course will provide the practical skills needed to optimize solutions effectively.

No prior experience with optimization is required; we’ll start from the basics and gradually move into advanced topics. By the end of this course, you’ll be able to confidently apply optimization techniques in real-world applications.

Join now and start learning!

Who this course is for:

  • This course is designed for engineers, data scientists, researchers, and business analysts who want to apply optimization techniques to real-world problems.

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