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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,880 --> 00:00:07,780 Now if you've looked up machine learning before you know there's a lot out there some resources recommend 2 00:00:07,780 --> 00:00:12,640 learning mathematics statistics probability and even more. 3 00:00:12,640 --> 00:00:19,030 Before getting started with data science and machine learning and while these these topics they're important 4 00:00:19,350 --> 00:00:26,470 trying to learn them all before getting started getting hands on is like trying to boil the ocean. 5 00:00:26,470 --> 00:00:33,520 Instead what we're going to be doing is focusing on building practical solutions and writing machine 6 00:00:33,520 --> 00:00:37,060 learning code to get insights out of data. 7 00:00:37,060 --> 00:00:42,850 If you're a programmer now and had some experience with python by the end of this course you'll be able 8 00:00:42,850 --> 00:00:47,680 to use your programming skills to build predictive machine learning models. 9 00:00:49,260 --> 00:00:56,180 Machine learning comes in three parts data collection data modeling and deployment where you might take 10 00:00:56,180 --> 00:00:59,160 a machine learning model after you've gone through these steps here. 11 00:00:59,160 --> 00:01:02,580 Don't worry we're gonna cover this framework in depth shortly. 12 00:01:02,760 --> 00:01:08,340 You might take this machine learning model and deploy it to users through your application or through 13 00:01:08,340 --> 00:01:10,150 an API or some sort. 14 00:01:10,830 --> 00:01:12,330 This is what we're going to cover. 15 00:01:12,660 --> 00:01:18,630 We're going to cover data modeling which means you'll be able to take a dataset and apply machine learning 16 00:01:18,630 --> 00:01:25,160 algorithms to find insights on that dataset the steps we're going to cover. 17 00:01:25,170 --> 00:01:26,690 Number one create a framework. 18 00:01:26,700 --> 00:01:29,630 You saw a little bit of an outline in that in the previous slide. 19 00:01:29,790 --> 00:01:36,060 Number two were going to match that framework once we've gone through it to available data science and 20 00:01:36,060 --> 00:01:37,880 machine learning tools. 21 00:01:37,890 --> 00:01:42,390 This means that people have already had similar problems to what we're going to try and solve in the 22 00:01:42,390 --> 00:01:43,230 future. 23 00:01:43,230 --> 00:01:46,610 In the past and so they've created tools for those problems. 24 00:01:46,650 --> 00:01:53,250 So we're gonna learn what tools are used for what machine learning projects and then to learn all of 25 00:01:53,250 --> 00:01:53,770 this. 26 00:01:53,790 --> 00:02:01,080 We're going to learn by doing by going through projects which involve step one and step two to build 27 00:02:01,080 --> 00:02:10,150 a portfolio to show off your work and your skills the utmost care has been taken to focus on what matters. 28 00:02:10,150 --> 00:02:15,030 When I started learning machine learning I found myself confused about what to do. 29 00:02:15,100 --> 00:02:18,040 Too often I'd spend too much time thinking about something. 30 00:02:18,130 --> 00:02:20,460 Instead of writing code and taking action. 31 00:02:20,560 --> 00:02:22,820 So we've designed this course to avoid that. 32 00:02:22,900 --> 00:02:28,780 Instead of doing anything and everything from scratch we're going to be using what works to build practical 33 00:02:28,780 --> 00:02:29,650 solutions. 34 00:02:29,650 --> 00:02:34,450 And at the same time learning about machine learning and data science. 35 00:02:35,080 --> 00:02:37,690 Let's take a deeper look at the framework we're going to be using. 3790

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