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Mastering Optimization with Python

Gain insights into solving optimization problems in Python. Delve into essential mathematical tools, metaheuristic methods, and techniques for constrained optimization. Become proficient in efficient Python solutions.

Beginner

50 Lessons

12h

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

62 Playgrounds

5 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

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

Optimization theory seeks the best solution, which is pivotal for machine learning, cost-cutting in manufacturing, refining logi...Show More

What You'll Learn

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

An understanding of the mathematical foundations of optimization methods

Course Content

Introduction

6 Lessons

Get familiar with optimization basics, Python tools, problem-solving, and practical applications.

Introduction to the Course

Tools to Implement Optimization Algorithms

Getting Started with Optimization

Basic Optimization Problems

Solutions to Basic Optimization Problems

Conclusion

Derivatives and Gradients

8 Lessons

Walk through using derivatives and gradients for solving complex optimization problems in Python.

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

First Optimization Algorithms

9 Lessons

Work your way through optimization algorithms including binary search, gradient descent, and Newton's method.

Introduction

Binary Search

Binary Search Implementation

Gradient Descent

Newton's Method

Multiple Variables Using SciPy

Exercises

Solutions

Quiz: First Optimization Algorithms

Population Methods

11 Lessons

Enhance your skills in utilizing genetic algorithms and particle swarm optimization for complex problem-solving.

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

Adding Constraints

6 Lessons

Take a closer look at solving constrained optimization problems using various methods and practical exercises in Python.

Constrained Optimization

Removing Equality Constraints

Linear Constrained Optimization

7 Lessons

Tackle linear optimization problems using Python with SciPy and CVXPY, covering integer constraints and practical applications.

Introduction to Linear Optimization

Solving Linear Problems

Integer Programs

Solving Integer Problems with CVXPY

Exercises

Solutions

Quiz: Linear Constrained Optimization

Summary and Conclusion

2 Lessons

Master the steps to develop an optimization mindset and practical skills using Python.

Summary

Conclusion

Appendix

1 Lessons

Get familiar with installing essential tools for optimization in Python across OS.

Tools Installation

Course Author

José Jorge Rodríguez Salgado

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