Hello and welcome to the cars, the ultimate beginner's guide to genetic algorithms in Python.
My name is John is going to lecture and I will teach all the lectures in this class.
First, I will answer a common question about this subject.
Why study genetic algorithms?
The answer is quite simple.
Nowadays, much has been said about deep learning and neural networks, and a lot of people are studying
these topics.
In other words, the focus is only on deep learning.
However, there are many other areas of artificial intelligence that can be explored to solve real world
problems.
The reason is that learning genetic algorithms can be considered a complementary topic for data.
Scientists and artificial intelligence are machine learning professionals, and the best part of it
is that you can solve real world problems and use it in decision making processes.
In this class, you will learn everything you need to enter the world of genetic algorithms.
What makes this class unique is that you will learn the basic intuition and especially the step-by-step
of implementation without using pre-built libraries.
In other words, we are going to implement genetic algorithms from scratch using Python.
If you have never heard about this subject at the end of the course, you will have all the theoretical
and practical basis to solve your own problems or the problems of the company you work for.
As its name suggests, this class is mainly suitable for beginners who are taking their first steps
in the area and want to start a career to work with artificial intelligence, and also for professionals
who would like to recap the main concepts about this subject.
It is very important that you have a basic knowledge about Python programming because all implementations
will be done from scratch.
Now I will briefly show you the contents of the Class E part number one.
We are going to implement a genetic algorithm from scratch to solve a very common problem that is related
to transfer.
They show off products.
Let's suppose we need to load some of these 14 products on the truck here, but we need to select the
most profitable products and also take into account that there is not enough space on the truck to load
them all so they go off.
The genetic algorithm will be to choose the best set of products to maximize the profits of the company
during the implementation.
You will learn the concepts about genetic algorithms such as fitness function, crossover mutation,
population, individual, selecting the best individuals and also as the ends.
We will integrate our algorithm with a database in my scale, so it will be easier to know how to deal
with commercial applications.
Then we will move on to the second part of the course after we will learn the whole intuition and implement
genetic algorithms from scratch.
It's time to learn how to work with libraries to solve the same problem.
In addition, should the case study of product transportation, we will also solve another problem that
is related to finding the lowest prices of airline tickets for people travelling in group.
We will solve both problems using shoe libraries.
The app just threw both its evolutionary algorithms in Python and am outros.
The interesting is that we will be able to compare the results of the libraries with the results of
our genetic algorithm implemented from scratch.
Here you can see the implementations.
And the list of topics that we will implement together step by step.
Almost everything will be done in Google CoLab online, meaning you don't have to waste time installing
in the managing libraries on your own machine.
We will also use by Shaam IEEE to connect our genetic algorithm with my skill.
And that's it for this first lecture.
We hope you have a great course and enjoy the content.
See you in the next lecture!