1 00:00:00,120 --> 00:00:01,110 Hi, welcome back. 2 00:00:01,350 --> 00:00:07,950 And this lecture, we will discuss the concept of Pisin automation, suppose we have repetitive. 3 00:00:09,070 --> 00:00:18,670 Tasks we do every day or every period of time, so you do it periodically so that you want to automate 4 00:00:18,670 --> 00:00:22,690 this task is to save time and effort. 5 00:00:24,200 --> 00:00:32,930 Like for us, the digital age we live and offers a smart way to do that, using programming languages 6 00:00:33,320 --> 00:00:34,730 as Python. 7 00:00:35,740 --> 00:00:44,560 So you can use Python to perform these repetitive tasks automatically, as a result, you will divide 8 00:00:44,560 --> 00:00:53,140 your workload into smaller tasks and write a code and bison's that can automate them, or at least some 9 00:00:53,140 --> 00:00:53,490 of them. 10 00:00:54,100 --> 00:01:00,220 But the question is why we use Python for automation of our work tasks. 11 00:01:00,400 --> 00:01:10,420 The answer is that Python offers great tools of libraries and python more approachable than other language. 12 00:01:11,810 --> 00:01:17,770 Also syntax of Python that similar to English exhibition. 13 00:01:17,890 --> 00:01:26,200 All of that make Python an excellent choice to use and desk to mention. 14 00:01:26,680 --> 00:01:30,610 Also, Python has an advantage over that. 15 00:01:30,610 --> 00:01:35,070 It let you create your own data structures using less. 16 00:01:35,130 --> 00:01:37,860 This dictionary sets and tables. 17 00:01:38,650 --> 00:01:44,930 This data structure well make you can install and exit data. 18 00:01:44,950 --> 00:01:50,380 Also, you can manage your data easily and efficiently. 19 00:01:50,620 --> 00:01:58,900 And if you use less useable data types and your programs, this will make your program or software run 20 00:01:58,900 --> 00:02:05,060 faster so that all of that will maintain the performance of your software. 21 00:02:05,440 --> 00:02:13,840 You can automate lots of tasks like sending emails, read and write from lots of file for me like C 22 00:02:13,840 --> 00:02:17,290 as V's and FS files. 23 00:02:19,180 --> 00:02:21,670 Daley is grab specific data. 24 00:02:23,110 --> 00:02:32,290 Like weather forecasts from specific weather forecasting website and currency or currencies change rate 25 00:02:32,290 --> 00:02:35,370 from the currency exchange rate websites. 26 00:02:36,370 --> 00:02:46,840 Bison has tons of laborer's like pandas used for management and analysis of data, the jungle used for 27 00:02:47,200 --> 00:02:53,620 web development, beautiful zoo and request is used for web scraping. 28 00:02:56,030 --> 00:03:03,410 Mirror and sockets used for Internet of Things, hadal or Hadoop. 29 00:03:04,760 --> 00:03:10,400 Used for big data, cycler, used for machine learning. 30 00:03:11,480 --> 00:03:22,520 Pygame used for games, turtle library used for drawing things like squares, circles and so on, Python 31 00:03:22,520 --> 00:03:31,910 has also a library called Take Enters, which used for guey or a graphical user interface. 32 00:03:32,630 --> 00:03:38,720 Python can be also used for image processing using offensive library. 33 00:03:39,470 --> 00:03:48,830 Some of these Python libraries or modules will need to be discussed in details in another sections or 34 00:03:49,400 --> 00:03:52,100 will be discussed in the next sections. 35 00:03:52,280 --> 00:04:01,280 Because the next projects section we will apply these concepts and try to use Python for task force 36 00:04:01,280 --> 00:04:02,600 automation or scripting. 37 00:04:02,900 --> 00:04:09,200 And we will do that through learning, by doing real world projects. 38 00:04:09,560 --> 00:04:18,470 And we can use NIAMBI and random modules or libraries for numbers manipulating and statistics and probabilities. 39 00:04:19,760 --> 00:04:25,690 Also, we can use python and bison is powerful in this section. 40 00:04:26,810 --> 00:04:27,770 Deep learning. 41 00:04:28,830 --> 00:04:31,800 By using tenths of law library. 42 00:04:33,270 --> 00:04:36,890 At this moment, we reach the end of this lecture. 43 00:04:37,000 --> 00:04:39,720 Hope you enjoyed this lecture and to get all of that. 44 00:04:40,480 --> 00:04:41,800 Thanks for being here. 45 00:04:43,680 --> 00:04:45,510 And thanks for watching. 46 00:04:45,870 --> 00:04:47,610 See you next lecture.