Mastering Optimization with Python

Learn to solve optimization problems in Python using essential math tools, metaheuristic methods, and constrained optimization techniques. Master efficient solutions for real-world applications.

Beginner

50 Lessons

12h

Certificate of Completion

Learn to solve optimization problems in Python using essential math tools, metaheuristic methods, and constrained optimization techniques. Master efficient solutions for real-world applications.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

62 Playgrounds
5 Quizzes

This course includes

62 Playgrounds
5 Quizzes

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

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

What You'll Learn

An understanding of the mathematical foundations of optimization methods

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Course Content

1.

Introduction

Get familiar with optimization basics, Python tools, problem-solving, and practical applications.
2.

Derivatives and Gradients

Walk through using derivatives and gradients for solving complex optimization problems in Python.
3.

First Optimization Algorithms

Work your way through optimization algorithms including binary search, gradient descent, and Newton's method.
4.

Population Methods

Enhance your skills in utilizing genetic algorithms and particle swarm optimization for complex problem-solving.
5.

Adding Constraints

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

Solving Sudoku and the 8-Queens Puzzle as Constraint Satisfaction

Project

6.

Linear Constrained Optimization

7 Lessons

Tackle linear optimization problems using Python with SciPy and CVXPY, covering integer constraints and practical applications.
7.

Summary and Conclusion

2 Lessons

Master the steps to develop an optimization mindset and practical skills using Python.
8.

Appendix

1 Lesson

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

Maze Solver Using the Ant Colony Optimization Algorithm

Project

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