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What you will learn

  • Understand the theory and practice of the main bio-inspired artificial intelligence algorithms
  • Solve real-world optimization problems using bio-inspired algorithms
  • Minimize the price of airline tickets using Genetic Algorithms
  • Create custom menus using Differential Evolution
  • Classify handwritten digits using Artificial Neural Networks
  • Adapt antibodies and antigens with the Clonal Selection algorithm, applied in digit recognition
  • Optimize course schedules using Particle Swarm Optimization
  • Solve shortest paths problems using Ant Colony Optimization

Requirements

  • Programming logic
  • Basic Python programmin

Description

Nature offers a wide range of inspirations for biological processes to be incorporated into technology and computing. Some of these processes and patterns have been inspiring the development of algorithms that can be used to solve real-world problems. They are called bio-inspired algorithms, whose inspiration in nature allows for applications in various optimization and classification problems.

In this course, you will learn the theoretical and mainly the practical implementation of the main and mostly used bio-inspired algorithms! By the end of the course you will have all the tools you need to build artificial intelligence solutions that can be applied to your own problems! The course is divided into six sections that cover different algorithms applied in real-world case studies. See below the projects that will be implemented step by step:

  1. Genetic Algorithms (GA): It is one of the most used and well-known bio-inspired algorithm to solve optimization problems. It is based on biological evolution in which populations of individuals evolve over generations through mutation, selection, and crossing over. We will solve the flight schedule problem and the goal is to minimize the price of air line tickets and the time spend waiting at the airport.
  2. Differential Evolution (DE): It is also inspired in biological evolution and the case study we will solve step by step is the creation of menus, correctly balancing the amount of carbohydrates, proteins and fats.
  3. Neural Networks (ANN): It is based on how biological neurons work and is considered one of the most modern techniques to solve complex problems, such as: chatbots, automatic translators, self driving cars, voice recognition, among many others. The case study will be the creation of a neural network for image classification.
  4. Clonal Selection Algorithm (CSA): It is based on the functioning of the optimization of the antibody response against an antigen, resembling the process of biological evolution. These concepts will be used in practice for digit identification and digit generation.
  5. Particle Swarm Optimization (PSO): It relies on the social behavior of animals, in which the swarm tries to find the best solution to a specific problem. The problem to be solved will be the timetable: there is a course, people who want to take it and different timetables. In the end, the algorithm will indicate the best times for each class to take the course.
  6. Ant Colony Optimization (ACO): It is based on concepts of how ants search for food in nature. The case study will be one of the most classic in the area, which is the choice of the shortest path.

Each type of problem requires different techniques for its solution. When you understand the intuition and implementation of bio-inspired algorithms, it is easier to identify which techniques are the best to be applied in each scenario. During the course, all the code will be implemented step by step using the Python programming language! We are going to use Google Colab, so you do not have to worry about installing libraries on your machine, as everything will be developed online using Google’s GPUs!

Who this course is for

  • People interested in how nature can provide inspiration for Computer Science problems
  • People interested in artificial intelligence algorithms, especially those inspired in Biology
  • Developers who want to solve real optimization and classification problems
  • Data Scientists who want to increase their portfolio

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Ratings and Reviews

4.7
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27 Ratings
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Review posted on Udemy
Posted 1 month ago
by Steven Dry

Very interesting course. Haven't had course material using this type of approach previously -- very refreshing. Great presentation. Highly recommend.

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Review posted on Udemy
Posted 2 months ago
by Eda Özer

I bought this course to learn how to implement optimization algoritms to problems. So, i think it provide this idea to me. Also in my opinion the tutor who teach genetic algorithms better than other tutor.

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Review posted on Udemy
Posted 6 months ago
by Angel Pavon

Good course, with different algorithms I like: - different algorithms - good explanation and process step by step I don't like: - code, can be improved a lot, sometimes are too complicated - antibodies explanation can be better, but it's ok The Ultimate Beginners Guide to Genetic Algorithms in Python course it's a good extension for this one

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Review posted on Udemy
Posted 8 months ago
by Carsten Baeumchen

good

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Review posted on Udemy
Posted 9 months ago
by Nilson Singh

done

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Review posted on Udemy
Posted 9 months ago
by Abas mhamad Qadir

Very good course

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Review posted on Udemy
Posted 10 months ago
by Alberto Nacher

Good course, English spoken well, all details are explained by lines of code. The Genetic Algorithms and Artificial Neural Networks are seen in details. Some hints also to real world problems to be faced with these techniques.

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Review posted on Udemy
Posted 1 year ago
by Adisorn Owatsiriwong

This is another excellent course in optimization algorithms. The explanation is so simple and concise. The examples are straightforward to follow.

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Review posted on Udemy
Posted 1 year ago
by Imene ayat

C'est bien expliqué

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Review posted on Udemy
Posted 1 year ago
by Carlos Andrés Campo González

Haven't seen this algorithms explained in such clear way anywhere else! Good implementations (both real life and and papers on which they are used) would be amazing and next level. But thanks for the theory and simple examples.

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