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These are the user uploaded subtitles that are being translated: 1 00:00:01,260 --> 00:00:11,040 Congratulations you just got hired at Keiko Corp. they're the latest Silicon Valley startup to hit the 2 00:00:11,040 --> 00:00:12,000 scene. 3 00:00:12,090 --> 00:00:18,940 They help analyze data of large companies and consult on what companies should do with their data they 4 00:00:18,940 --> 00:00:19,320 do. 5 00:00:19,570 --> 00:00:23,080 Autonomous vehicles security cameras. 6 00:00:23,080 --> 00:00:26,710 They have an online dating app and Web site. 7 00:00:26,710 --> 00:00:34,200 They even have drones and they also offer their services like Amazon to other big companies or startups. 8 00:00:35,540 --> 00:00:37,730 And you just got hired. 9 00:00:38,120 --> 00:00:39,020 Awesome. 10 00:00:39,130 --> 00:00:39,350 Wait. 11 00:00:39,740 --> 00:00:41,320 What is your role. 12 00:00:41,330 --> 00:00:45,500 Well you're the new machine learning and data science expert. 13 00:00:45,500 --> 00:00:51,070 Well this is a little awkward because you don't really know anything about that do you. 14 00:00:52,430 --> 00:00:58,730 Maybe a little bit maybe you've heard it in the news but this is gonna be awkward because it's too late 15 00:00:58,950 --> 00:01:06,270 you're already hired and people want you working right away so you entered the office and Oh there's 16 00:01:06,270 --> 00:01:07,860 your boss right there. 17 00:01:07,860 --> 00:01:14,040 This guy over here with the perfect hair that's Bruno he just got promoted and he's really excited to 18 00:01:14,040 --> 00:01:20,730 make an impact with Keiko caught in their data division and he just hired you to lead some of their 19 00:01:20,730 --> 00:01:22,500 initiatives. 20 00:01:22,570 --> 00:01:28,480 Now he seems nice on the surface but I have a feeling that he's going to be a tough cookie to work with. 21 00:01:28,690 --> 00:01:29,540 But you know what. 22 00:01:29,560 --> 00:01:35,650 We'll get through this now if your memory is fuzzy of how you ended up here out Keiko Corp let's not 23 00:01:35,650 --> 00:01:36,900 worry about that. 24 00:01:36,970 --> 00:01:38,600 It's your first day on the job. 25 00:01:38,770 --> 00:01:44,980 All you know is that you're the new machine learning and data science expert now because this is your 26 00:01:44,980 --> 00:01:46,630 first day as you do. 27 00:01:46,630 --> 00:01:53,170 You go out for lunch with your co-workers and luckily they don't ask you too many questions and they 28 00:01:53,170 --> 00:01:53,890 seem to like you. 29 00:01:53,890 --> 00:01:55,270 They seem to be nice. 30 00:01:55,480 --> 00:02:03,040 But then during lunch comes the question hey we've heard about this machine learning thing and Bruno 31 00:02:03,040 --> 00:02:06,670 said that you're the expert on the topic and we're excited to have you on the team. 32 00:02:06,700 --> 00:02:14,290 But what is machine learning and you think to yourself oh boy what should I say here. 33 00:02:14,290 --> 00:02:17,310 Well you know what guys I have to go back to work. 34 00:02:17,320 --> 00:02:18,180 Don't have much time. 35 00:02:18,190 --> 00:02:19,720 I'll explain it to you tomorrow. 36 00:02:19,720 --> 00:02:26,590 So you run away still confused how you got here but luckily the day ends with you not revealing that 37 00:02:26,590 --> 00:02:31,360 you know absolutely nothing and you have no idea how you just got hired. 38 00:02:31,360 --> 00:02:34,630 You survived the first day but we have a problem right. 39 00:02:34,640 --> 00:02:40,030 Tamar you have to go into work and you're gonna have to explain this to your colleagues hopefully can 40 00:02:40,030 --> 00:02:44,630 survive throughout the next couple of weeks and let people know that you're not an imposter. 41 00:02:44,650 --> 00:02:47,320 But what should we do here. 42 00:02:47,320 --> 00:02:55,510 Well luckily for you you remember your old childhood friends their name Andre and Daniel. 43 00:02:55,510 --> 00:03:02,500 Look how cute they were as a baby you call them up and you say hey somehow I landed this job in Silicon 44 00:03:02,500 --> 00:03:03,480 Valley. 45 00:03:03,490 --> 00:03:04,590 It's really well-paying. 46 00:03:04,600 --> 00:03:06,790 But I have no idea what I'm talking about. 47 00:03:06,970 --> 00:03:08,780 And luckily for you they know. 48 00:03:08,860 --> 00:03:14,770 So they're going to help you throughout this journey to make sure that Bruno doesn't fire you and you 49 00:03:14,770 --> 00:03:16,750 impress all your colleagues. 50 00:03:16,900 --> 00:03:22,240 What we're going to do is each day at work you're gonna get some tasks and then you're gonna go back 51 00:03:22,240 --> 00:03:22,680 home. 52 00:03:23,020 --> 00:03:32,940 And Andre and Daniel are going to help you so that while you don't get fired here we are all of us at 53 00:03:32,940 --> 00:03:37,290 home trying to decide how we're going to tackle this first problem. 54 00:03:37,290 --> 00:03:40,110 That is what is machine learning. 55 00:03:40,170 --> 00:03:43,780 We have to go into work tomorrow and explain it to our colleagues. 56 00:03:43,830 --> 00:03:47,620 Let's get to it what is machine learning. 5394

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