Mastering Optimization with Python

INTERACTIVE COURSE

Mastering Optimization with Python

Beginner

50 Lessons

12h

Certificate of Completion

AI Explanations

AI Explanations

Mastering Optimization with Python

62 Playgrounds

5 Quizzes

58 Illustrations

Takeaway Skills

An understanding of the mathematical foundations of optimization methods

Familiarity with population-based metaheuristic optimization methods such as genetic algorithms and particle swarm optimization

Hands-on experience in formulating, implementing, and solving optimization problems using Python

A working knowledge of Python libraries such as SciPy, NumPy, and CVXPY for solving optimization problems

Course Overview

Optimization theory seeks the best solution, which is pivotal for machine learning, cost-cutting in manufacturing, refining logistics, and boosting finance profits. This course provides a detailed description of different optimization problems and the techniques used to solve them. You’ll begin with the formal definition of an optimization problem and an overview of essential mathematical tools: derivatives, gradients, and Hessian. With this knowledge, you’ll implement solutions for several optimization pr...Show More

Course Content

1

Introduction

Introduction to the Course Tools to Implement Optimization Algorithms Getting Started with Optimization Basic Optimization Problems Solutions to Basic Optimization Problems Conclusion

2

Derivatives and Gradients

Introduction Derivatives Derivatives in Multiple Dimensions Second-Order Derivatives and the Hessian Solving Problems with the Gradient and the Hessian Exercises Solutions Quiz: Derivatives and Gradients

3

First Optimization Algorithms

Introduction Binary Search Binary Search Implementation Gradient Descent Newton's Method Multiple Variables Using SciPy Exercises Solutions Quiz: First Optimization Algorithms

4

Population Methods

Introduction Genetic Algorithms Applying Genetic Algorithms Particle Swarm Optimization Other Methods Exercises Solution to Exercise 1: Using Both Algorithms Solution to Exercise 2: Assigning Tasks Solution to Exercise 3: Finding the Best Way to Travel Solution to Exercise 4: Finding the Best Connection Quiz: Population Methods

5

Adding Constraints

Constrained Optimization Removing Equality Constraints Penalty Methods Exercises Solutions Quiz: Constraints

6

Linear Constrained Optimization

7 Lessons

7

Summary and Conclusion

2 Lessons

8

Appendix

1 Lesson

COURSE AUTHOR

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