1 00:00:04,210 --> 00:00:09,390 And now, I want to introduce other lecturers of this course. 2 00:00:09,390 --> 00:00:13,190 Alexander, Dmitry, Mikhail, and Marios. 3 00:00:13,190 --> 00:00:15,520 Mikhail is aka Cassanova, 4 00:00:15,520 --> 00:00:20,180 the person who reached the very top of competitive data science. 5 00:00:20,180 --> 00:00:22,925 I will tell you a couple of thoughts about the origins of the course. 6 00:00:22,925 --> 00:00:27,660 In year 2014, we started our win in data science by joining competitions. 7 00:00:27,660 --> 00:00:30,880 We've been meeting every week and discussing the past competitions, solutions, 8 00:00:30,880 --> 00:00:33,885 ideas and tweaks what worked and what did not, 9 00:00:33,885 --> 00:00:36,760 this exchange of knowledge and experience helped us 10 00:00:36,760 --> 00:00:39,915 to learn quickly from each other and improve our skills. 11 00:00:39,915 --> 00:00:41,680 Initially our community was small, 12 00:00:41,680 --> 00:00:44,545 but over time more and more people were joining. 13 00:00:44,545 --> 00:00:47,230 From the format of groups of discussion. 14 00:00:47,230 --> 00:00:49,550 We moved on to the format of well organized meetings. 15 00:00:49,550 --> 00:00:54,185 Where a speaker makes an overview of his approach and ideas in front of 50 people. 16 00:00:54,185 --> 00:00:56,585 These meetings are called machine learning trainings. 17 00:00:56,585 --> 00:01:00,110 Now with the help and support of Yandex and get a hundred of participants. 18 00:01:00,110 --> 00:01:06,720 Thus we started from zero and learned everything by hard work and collaboration. 19 00:01:06,720 --> 00:01:08,240 We had an excellent teacher, 20 00:01:08,240 --> 00:01:11,010 Alexander D'yakonov who was top one on Kaggle, 21 00:01:11,010 --> 00:01:13,870 he took the course on critical data analysis. 22 00:01:13,870 --> 00:01:18,235 In Moscow state university and there we're grateful to him. 23 00:01:18,235 --> 00:01:21,160 At some point we started to share our knowledge with 24 00:01:21,160 --> 00:01:25,925 other people and some of us even started to read lectures at the university. 25 00:01:25,925 --> 00:01:31,630 So now we have decided to summarize everything and make it available for everyone. 26 00:01:31,630 --> 00:01:35,835 Together. We've finished and procesed in about 20 different competitions 27 00:01:35,835 --> 00:01:40,585 only on Kaggle and just as many on other not so famous platforms. 28 00:01:40,585 --> 00:01:44,050 All of us have a tremendous amount of skill and experience in 29 00:01:44,050 --> 00:01:48,250 competitive data science and now we want to share this experience with you. 30 00:01:48,250 --> 00:01:49,500 For all of us, 31 00:01:49,500 --> 00:01:52,555 competitive data science opened a number of opportunities 32 00:01:52,555 --> 00:01:56,745 as the competitions we took part were dedicated to a large variety of tasks. 33 00:01:56,745 --> 00:01:59,065 Mikhail works in e-commerce. 34 00:01:59,065 --> 00:02:02,140 Alexander builds predictive model for taxi services, 35 00:02:02,140 --> 00:02:04,180 Dmitri works with financial data, 36 00:02:04,180 --> 00:02:08,725 Mario develops machinery learning frameworks and I am a deep learning researcher. 37 00:02:08,725 --> 00:02:10,660 Competitions, without a doubt, 38 00:02:10,660 --> 00:02:14,140 became a stepping stone for our careers and believe me, 39 00:02:14,140 --> 00:02:18,040 good comparative record will bring success to you as well. 40 00:02:18,040 --> 00:02:23,330 We hope you will find something interesting in this course and wish you good luck.