1 00:00:00,450 --> 00:00:08,100 Hello, guys, and welcome and welcome to another of my courses, they squiz topic would be data science 2 00:00:08,610 --> 00:00:11,940 as well as machine learning, but principal data science. 3 00:00:12,600 --> 00:00:17,550 So before we even start the course, I'd like to thank you all for joining this class. 4 00:00:17,840 --> 00:00:24,270 Mostly, I'm really glad to be your teacher and help you learn more about this subject. 5 00:00:25,230 --> 00:00:30,800 So basically, the way I created discourse, I tried to be the more interactive possible. 6 00:00:31,290 --> 00:00:37,260 I tried not to create a long course, but take everything that I knew about the topic and put it together 7 00:00:37,560 --> 00:00:43,050 and create the shortest course possible with the maximum amount of knowledge in it. 8 00:00:43,410 --> 00:00:48,020 So this way, the number of people who will complete the course will be very, very high. 9 00:00:48,060 --> 00:00:53,160 And for you guys, it's going to be way easier to complete a course that is a bit shorter like this 10 00:00:53,160 --> 00:00:53,340 one. 11 00:00:53,350 --> 00:00:55,200 Then, of course, that is wait longer. 12 00:00:57,240 --> 00:01:03,930 Honestly, I'll be honest, this is not an easy topic since we are talking about programming language. 13 00:01:03,930 --> 00:01:06,780 We're talking about the science as well as machine learning. 14 00:01:07,140 --> 00:01:13,440 But once again, I'm pretty convinced that this course will give you guys all the basics to have a complete 15 00:01:13,440 --> 00:01:14,630 introduction to this topic. 16 00:01:14,980 --> 00:01:17,790 Yes, by the end of this course, you guys will not be professionals. 17 00:01:17,790 --> 00:01:22,890 And yes, you will need a lot of practice to be able to understand completely this topic. 18 00:01:23,430 --> 00:01:30,690 But once again, when you will have completed this course, we'll have all the basic knowledge to be 19 00:01:30,690 --> 00:01:33,600 able to, let's say, start in the school. 20 00:01:34,080 --> 00:01:36,300 So what is the goal of this course? 21 00:01:37,350 --> 00:01:41,720 First of all, my goal will be to teach you the basics of data science. 22 00:01:42,330 --> 00:01:47,640 So in this course, we are going to talk about what is there a science, how it works, everything that 23 00:01:47,640 --> 00:01:50,610 is around data science as well as machine learning. 24 00:01:51,930 --> 00:01:58,760 So once again, I'll try to guide you step by step and really put putting everything together. 25 00:01:59,490 --> 00:02:05,070 So this way you guys will not be lost and understand why we use some techniques or why we use some tools 26 00:02:05,070 --> 00:02:05,930 and not others. 27 00:02:06,990 --> 00:02:09,950 We are going to learn to work with multiple tools. 28 00:02:11,310 --> 00:02:15,120 Well, in this course, you're going to see we going to learn multiple tools. 29 00:02:15,120 --> 00:02:17,970 And the main programming language in this course would be Python. 30 00:02:18,750 --> 00:02:24,930 Basically, the tools that we are going to work with could be multiple matplotlib, seabourne numpty, 31 00:02:24,930 --> 00:02:30,210 pandas and many others that we are going to see in this course. 32 00:02:30,480 --> 00:02:35,130 Those are all very interesting tools that we are going to learn in this course. 33 00:02:36,540 --> 00:02:42,150 So by the end of this course, once again, you guys will have all the knowledge of those tools, which 34 00:02:42,150 --> 00:02:42,960 is pretty cool. 35 00:02:43,850 --> 00:02:47,370 Um, you guys will as well understand the basics of machine learning. 36 00:02:47,370 --> 00:02:52,170 So this course is really here to help you understand what is machine learning and all the basics around. 37 00:02:52,170 --> 00:02:57,840 It is, of course, is a lot of about data science, but there is well, there is a link between data 38 00:02:57,840 --> 00:02:59,150 science and machine learning. 39 00:02:59,670 --> 00:03:00,660 It's really, really close. 40 00:03:00,870 --> 00:03:03,750 And we are going to talk about both topics. 41 00:03:05,520 --> 00:03:10,140 The course content, the way I divide it, it is pretty much, well, simple. 42 00:03:10,380 --> 00:03:15,780 First of all, we got to talk about the introduction and we'll have a small introduction to data science 43 00:03:15,780 --> 00:03:16,320 and everything. 44 00:03:16,980 --> 00:03:20,510 We are going to talk about the basic statistical knowledge. 45 00:03:20,760 --> 00:03:23,730 So in this case, we'll have a few classes about statistics. 46 00:03:24,090 --> 00:03:29,640 And while we talk about data, we're going to talk about statistics and different things that you guys 47 00:03:29,640 --> 00:03:38,040 will need to know to be able to, well, understand data science, then we are going to talk about different 48 00:03:38,040 --> 00:03:38,400 tools. 49 00:03:38,400 --> 00:03:45,450 As I said before, the tools that we are going to use here will be no Skype by matplotlib. 50 00:03:45,450 --> 00:03:48,230 It's hard to pronounce and Seabourne as well. 51 00:03:48,450 --> 00:03:54,330 So each of those tools we are going to get inside of it will give you guys a complete introduction to 52 00:03:54,330 --> 00:03:54,840 this tool. 53 00:03:55,140 --> 00:04:01,260 And we are going to talk really in-depth about each of those tools and practice some exercise on those. 54 00:04:03,060 --> 00:04:09,120 Then we are going to talk about mathematics because the data science is really, really made of mathematics. 55 00:04:09,120 --> 00:04:11,910 Mathematics are like the core of all this. 56 00:04:12,330 --> 00:04:17,400 So we are going to have a big part of the course that is really about mathematics. 57 00:04:17,410 --> 00:04:21,450 Once again, it's not a huge vote because this is just an introduction. 58 00:04:21,660 --> 00:04:26,910 But we are still going to talk about some mathematics, for example, the basic mathematics for machine 59 00:04:26,910 --> 00:04:32,220 learning, basics of algebra and some other calculations that we are going to talk about. 60 00:04:33,270 --> 00:04:36,030 So you will see it's not something that is impossible to do. 61 00:04:36,030 --> 00:04:42,570 But if you have a basic maths background, you guys should be good to go for this part of the class. 62 00:04:43,380 --> 00:04:45,150 Then we'll have an introduction to machine learning. 63 00:04:45,150 --> 00:04:49,640 So once again, we're going to talk about machine learning, about different algorithm algorithms, 64 00:04:49,640 --> 00:04:51,690 sorry, that exists in machine learning. 65 00:04:52,740 --> 00:04:57,300 We're going to talk about classification and many other things, which is pretty cool. 66 00:04:59,190 --> 00:04:59,880 We then will. 67 00:04:59,940 --> 00:05:01,110 Talk about regression. 68 00:05:01,380 --> 00:05:08,640 So what is regression when it's used types of regression and, well, for example, linear regression 69 00:05:09,030 --> 00:05:14,710 and some other methods that we can use, you'll see it's not something that is that complicated. 70 00:05:14,730 --> 00:05:20,390 Once again, you need to understand it to be able to go further in this field. 71 00:05:21,420 --> 00:05:23,840 Then we'll have introduction to some other tools. 72 00:05:23,850 --> 00:05:28,190 In this case, we'll talk about Carus and the Bispebjerg once again. 73 00:05:28,200 --> 00:05:34,290 I'll give you a complete introduction to those tools as well, and we will practice a little bit with 74 00:05:34,290 --> 00:05:34,540 them. 75 00:05:34,560 --> 00:05:36,690 So once again, as I said, discourse. 76 00:05:36,690 --> 00:05:37,650 Yes, it's a bit long. 77 00:05:37,980 --> 00:05:42,320 But once again, the goal here is really to give you a complete introduction of the subject. 78 00:05:42,330 --> 00:05:43,510 The subject is not easy. 79 00:05:43,860 --> 00:05:49,320 Once again, I think that with discourse, you guys will be able to understand at least the basics of 80 00:05:49,590 --> 00:05:52,170 data science as well as machine learning. 81 00:05:53,790 --> 00:05:56,380 So what will you be able to do by the end of this course? 82 00:05:56,970 --> 00:06:01,280 First of all, you will understand all the basics of the science, so you will understand what is the 83 00:06:01,380 --> 00:06:03,870 science, how it works and the principle. 84 00:06:03,960 --> 00:06:09,390 What are the main tools that you guys can use to be able to understand that science? 85 00:06:09,720 --> 00:06:14,970 And if you guys start practicing right through discourse, I'm pretty sure that you can create really, 86 00:06:14,970 --> 00:06:16,080 really great things. 87 00:06:16,890 --> 00:06:21,350 Um, next thing, we'll learn how to work with the python for the design. 88 00:06:21,380 --> 00:06:25,200 So, as I said, the main programming language for discourse would be in Python. 89 00:06:25,620 --> 00:06:27,060 So you guys will understand. 90 00:06:27,240 --> 00:06:33,780 Well, if you guys are beginners in Python, you will get a step further in this programming language 91 00:06:34,080 --> 00:06:39,240 and you will understand some different programs, small programs that you guys can write in Python to 92 00:06:39,240 --> 00:06:45,860 be able to use them for data science as well as machine learning, which is pretty cool in my opinion. 93 00:06:46,290 --> 00:06:49,650 And this way, you guys will be able to practice your programming as well. 94 00:06:50,460 --> 00:06:51,860 And also you will be able. 95 00:06:51,870 --> 00:06:55,430 Well, the last thing is that you will be able to work with different python tools. 96 00:06:55,710 --> 00:07:01,340 So, as I said, the many of the tools that we are going to learn in discourse works with Python. 97 00:07:01,890 --> 00:07:06,500 So you will be able to use all those tools to perform data science things. 98 00:07:06,510 --> 00:07:12,510 For example, if you want to make your research about a certain topic or anything that requires the 99 00:07:12,510 --> 00:07:18,780 use of data science, you will be able to do it by the end of the SCHAUS or at least have the basic 100 00:07:18,780 --> 00:07:23,720 knowledge to be able to create your plan and then do it by yourself. 101 00:07:24,930 --> 00:07:30,240 Not only this, for example, you have a personal project that you want to do in this field, in data 102 00:07:30,240 --> 00:07:31,400 science or machine learning. 103 00:07:31,620 --> 00:07:37,920 I'm pretty sure that this question really will really give you a strong basis to be able to start this 104 00:07:37,920 --> 00:07:41,960 little project that you guys want to do, subsets of that. 105 00:07:41,970 --> 00:07:45,840 I think this is what this course is all about. 106 00:07:46,230 --> 00:07:51,630 And once again, I'd like to thank you all another time for taking the course with me. 107 00:07:51,930 --> 00:07:53,910 I'll be really, really glad to teach you. 108 00:07:53,910 --> 00:07:58,260 Well, it's an honor for me, actually, to be able to teach you this topic. 109 00:07:58,260 --> 00:07:59,250 It's not easy. 110 00:07:59,520 --> 00:08:00,600 It's not an easy topic. 111 00:08:00,900 --> 00:08:06,390 Once again, you'll see by the end of this course, you should pretty much master all the basics of 112 00:08:06,390 --> 00:08:14,070 this topic and have a good basis to be able to launch your career or simply launch your hobby in this 113 00:08:14,220 --> 00:08:14,510 field. 114 00:08:14,970 --> 00:08:20,520 So that's it for this introduction, guys, into all in our first class.