Artificial Intelligence: Optimization Algorithms in Python

Current Status

Not Enrolled

Price

20

Get Started

What you will learn

  • Learn the theory and implement optimization algorithms from scratch for solving real problems
  • Implement step by step the following algorithms in Python: random search, hill climb, simulated annealing, and genetic algorithms
  • Solve real problems for optimizing flight calendars and dormitory room optimization (limited resources)
  • Implement optimization algorithms using predefined libraries

Requirements

  • Programming logic (if, while and for statements)
  • Basic Python programming
  • No prior knowledge about Artificial Intelligence

Description

What would an ā€œoptimal worldā€ look like to you? Would people get along better? Would transport run faster? Would we take better care of our environment?

Many data scientists choose to optimize by using pre-built machine learning libraries.  But we think that this kind of ‘plug-and-play’ study hinders your learning. That’s why this course gets you to build an optimization algorithm from the ground up.

In Artificial Intelligence: Optimization Algorithms in Python, you’ll get to learn all the logic and math behind optimization algorithms. With two highly practical case studies, you’ll also find out how to apply them to solve real-world problems.

In the first case study, we’ll optimize travel plans for six friends who want to fly out from the same airport. In the second case study, we’ll optimize the way university administrators allocate dorm rooms to new students.

On the way, we’ll learn what optimization algorithms are. We’ll find out how they can be applied to daily business practice. And we’ll see how they can learn by themselves.

This course introduces you to four types of optimization algorithms:

– random search

– hill climb

– simulated annealing, and

– genetic

Don’t worry if you’re not yet sure what any of these are. We’ll go through each one in detail, and you’ll find out how to build each of them in our two case studies.”

Who this course is for

  • Beginners who are starting to learn about Artificial Intelligence
  • People interested in the theory of optimization algorithms
  • Undergraduate students who are studying subjects related to Artificial Intelligence
  • People interested in solving real problems using optimisation algorithms
  • Anyone interested in Artificial Intelligence

Course Content

Introduction 3 Topics
Random search 3 Topics
Lesson Content
0% Complete 0/3 Steps
Hill climb 7 Topics
Simulated annealing 7 Topics
Maximizing profit – transport of products 6 Topics
Library for optimization algorithms 5 Topics
Bonus 2 Topics
Final remarks 1 Topic
Lesson Content
0% Complete 0/1 Steps

Ratings and Reviews

4.8
Avg. Rating
130 Ratings
5
101
4
27
3
2
2
0
1
0
What's your experience? We'd love to know!
Review posted on Udemy
Posted 1 month ago
by Abhilash Pannuru

good exp

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Anurag Nandy

Amazing Course

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Vinutha Muralikumar

Good

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Angu S

good

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Mamatha Donthagani

good

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Yash Jhunjhunwala

Good

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Naresh Rayapureddy

good

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Kaviti Kalyani Jyothi

NA

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Anmol Anmol

excellent

×
Preview Image
Review posted on Udemy
Posted 2 months ago
by Sri Gubbala

good

×
Preview Image
Show more reviews
What's your experience? We'd love to know!
Scroll to Top