1 00:00:00,090 --> 00:00:07,170 Hello and welcome to the cars, the ultimate beginner's guide to genetic algorithms in Python. 2 00:00:07,530 --> 00:00:12,810 My name is John is going to lecture and I will teach all the lectures in this class. 3 00:00:13,320 --> 00:00:17,880 First, I will answer a common question about this subject. 4 00:00:18,250 --> 00:00:21,150 Why study genetic algorithms? 5 00:00:21,510 --> 00:00:23,520 The answer is quite simple. 6 00:00:23,820 --> 00:00:31,830 Nowadays, much has been said about deep learning and neural networks, and a lot of people are studying 7 00:00:31,830 --> 00:00:32,670 these topics. 8 00:00:32,970 --> 00:00:37,470 In other words, the focus is only on deep learning. 9 00:00:37,800 --> 00:00:47,190 However, there are many other areas of artificial intelligence that can be explored to solve real world 10 00:00:47,190 --> 00:00:47,940 problems. 11 00:00:48,540 --> 00:00:56,160 The reason is that learning genetic algorithms can be considered a complementary topic for data. 12 00:00:56,160 --> 00:01:03,690 Scientists and artificial intelligence are machine learning professionals, and the best part of it 13 00:01:03,900 --> 00:01:09,870 is that you can solve real world problems and use it in decision making processes. 14 00:01:10,200 --> 00:01:17,220 In this class, you will learn everything you need to enter the world of genetic algorithms. 15 00:01:17,640 --> 00:01:26,730 What makes this class unique is that you will learn the basic intuition and especially the step-by-step 16 00:01:26,730 --> 00:01:31,260 of implementation without using pre-built libraries. 17 00:01:31,500 --> 00:01:39,120 In other words, we are going to implement genetic algorithms from scratch using Python. 18 00:01:39,660 --> 00:01:47,790 If you have never heard about this subject at the end of the course, you will have all the theoretical 19 00:01:47,970 --> 00:01:56,070 and practical basis to solve your own problems or the problems of the company you work for. 20 00:01:56,310 --> 00:02:05,220 As its name suggests, this class is mainly suitable for beginners who are taking their first steps 21 00:02:05,220 --> 00:02:13,920 in the area and want to start a career to work with artificial intelligence, and also for professionals 22 00:02:13,920 --> 00:02:19,200 who would like to recap the main concepts about this subject. 23 00:02:19,560 --> 00:02:27,510 It is very important that you have a basic knowledge about Python programming because all implementations 24 00:02:27,510 --> 00:02:29,280 will be done from scratch. 25 00:02:29,730 --> 00:02:36,840 Now I will briefly show you the contents of the Class E part number one. 26 00:02:37,080 --> 00:02:46,800 We are going to implement a genetic algorithm from scratch to solve a very common problem that is related 27 00:02:46,800 --> 00:02:47,770 to transfer. 28 00:02:47,790 --> 00:02:49,950 They show off products. 29 00:02:50,430 --> 00:03:01,740 Let's suppose we need to load some of these 14 products on the truck here, but we need to select the 30 00:03:01,740 --> 00:03:12,270 most profitable products and also take into account that there is not enough space on the truck to load 31 00:03:12,270 --> 00:03:15,210 them all so they go off. 32 00:03:15,210 --> 00:03:25,110 The genetic algorithm will be to choose the best set of products to maximize the profits of the company 33 00:03:25,440 --> 00:03:27,060 during the implementation. 34 00:03:27,340 --> 00:03:36,300 You will learn the concepts about genetic algorithms such as fitness function, crossover mutation, 35 00:03:36,330 --> 00:03:44,790 population, individual, selecting the best individuals and also as the ends. 36 00:03:44,790 --> 00:03:54,720 We will integrate our algorithm with a database in my scale, so it will be easier to know how to deal 37 00:03:54,720 --> 00:03:56,880 with commercial applications. 38 00:03:57,420 --> 00:04:05,220 Then we will move on to the second part of the course after we will learn the whole intuition and implement 39 00:04:05,220 --> 00:04:07,800 genetic algorithms from scratch. 40 00:04:08,190 --> 00:04:14,190 It's time to learn how to work with libraries to solve the same problem. 41 00:04:14,580 --> 00:04:24,330 In addition, should the case study of product transportation, we will also solve another problem that 42 00:04:24,330 --> 00:04:32,730 is related to finding the lowest prices of airline tickets for people travelling in group. 43 00:04:33,180 --> 00:04:36,900 We will solve both problems using shoe libraries. 44 00:04:37,500 --> 00:04:44,520 The app just threw both its evolutionary algorithms in Python and am outros. 45 00:04:45,030 --> 00:04:52,710 The interesting is that we will be able to compare the results of the libraries with the results of 46 00:04:52,710 --> 00:04:56,760 our genetic algorithm implemented from scratch. 47 00:04:57,150 --> 00:04:59,730 Here you can see the implementations. 48 00:04:59,960 --> 00:05:06,620 And the list of topics that we will implement together step by step. 49 00:05:06,920 --> 00:05:16,280 Almost everything will be done in Google CoLab online, meaning you don't have to waste time installing 50 00:05:16,280 --> 00:05:19,460 in the managing libraries on your own machine. 51 00:05:19,880 --> 00:05:28,280 We will also use by Shaam IEEE to connect our genetic algorithm with my skill. 52 00:05:28,610 --> 00:05:31,100 And that's it for this first lecture. 53 00:05:31,400 --> 00:05:35,240 We hope you have a great course and enjoy the content. 54 00:05:35,660 --> 00:05:37,490 See you in the next lecture!