1 00:00:02,000 --> 00:00:06,900 Hi! I'm Ian Witten from the beautiful University of Waikato in New Zealand, 2 00:00:06,900 --> 00:00:13,460 and I'd like to tell you about our new online course More Data Mining with Weka. 3 00:00:13,460 --> 00:00:16,730 It's an advanced version of Data Mining with Weka, 4 00:00:16,730 --> 00:00:17,820 and if you liked that, 5 00:00:17,820 --> 00:00:19,289 you'll love the new course. 6 00:00:19,289 --> 00:00:21,890 It's the same format, the same software, 7 00:00:21,890 --> 00:00:23,330 the same learning by doing. 8 00:00:23,330 --> 00:00:28,939 The aim is the same, as well, to enable you to use advanced techniques of data mining 9 00:00:28,939 --> 00:00:33,980 to process your own data and understand what you're doing. 10 00:00:33,980 --> 00:00:38,380 You don't need to have actually completed the old course in order to embark on the new one, 11 00:00:38,380 --> 00:00:40,110 but we won't be covering things again, 12 00:00:40,110 --> 00:00:45,800 so you will need to know something about data mining and the Weka machine learning workbench. 13 00:00:45,800 --> 00:00:49,640 The course has short, 5-10 minute video lessons. 14 00:00:49,640 --> 00:00:52,460 Slides and captions are available, as well, 15 00:00:52,460 --> 00:00:56,920 along with optional readings from the data mining text book that the publisher 16 00:00:56,920 --> 00:01:00,230 has agreed to make available for free. 17 00:01:00,230 --> 00:01:05,500 There is a mid-course assessment and an end-of-course assessment, and if you do well in these, 18 00:01:05,500 --> 00:01:09,820 you'll get a signed Statement of Completion from the University of Waikato. 19 00:01:09,820 --> 00:01:15,510 As before, Weka will be a laboratory for you to learn the practice and the principles of 20 00:01:15,510 --> 00:01:18,020 advanced data mining. 21 00:01:18,020 --> 00:01:24,040 Each lesson is followed by a carefully designed activity that reinforces what you learned 22 00:01:24,040 --> 00:01:26,479 in the lesson. 23 00:01:26,479 --> 00:01:29,049 You're going to do most of your learning actually doing the activities. 24 00:01:29,049 --> 00:01:32,829 You won't learn by listening to me talking or watching me do things, you'll learn by 25 00:01:32,829 --> 00:01:34,250 doing stuff yourself. 26 00:01:34,250 --> 00:01:39,930 There won't be any programming in this course, but a little bit of high school mathematics 27 00:01:39,930 --> 00:01:41,890 might come in handy. 28 00:01:41,890 --> 00:01:46,049 The topics were suggested by students who completed the early course. 29 00:01:46,049 --> 00:01:51,770 We're going to start by looking at how to set up large scale experiments to compare 30 00:01:51,770 --> 00:01:56,000 different learning and filtering techniques on your own data and different versions of 31 00:01:56,000 --> 00:01:57,000 the dataset. 32 00:01:57,500 --> 00:01:59,500 Then you'll get to experience big data. 33 00:01:59,500 --> 00:02:03,659 You'll be working with datasets containing many millions of instances, 34 00:02:03,659 --> 00:02:08,530 and I'll show you how to use Weka to process even larger, 35 00:02:08,530 --> 00:02:12,200 effectively unlimited datasets, as well. 36 00:02:12,200 --> 00:02:15,030 Then we'll look at document classification. 37 00:02:15,030 --> 00:02:18,840 Text mining is a very popular application of machine learning. 38 00:02:18,840 --> 00:02:22,370 We'll look at association rules and clustering. 39 00:02:22,370 --> 00:02:27,020 We'll look at attribute selection and how to use cost models of your problem 40 00:02:27,020 --> 00:02:30,069 to optimize the cost. 41 00:02:30,069 --> 00:02:36,620 You'll even get to set up your own neural network in the Weka toolkit. 42 00:02:36,620 --> 00:02:37,620 Of course, you'll need a computer, 43 00:02:37,620 --> 00:02:40,650 because you'll be installing Weka on your own machine. 44 00:02:40,650 --> 00:02:41,590 You need an internet connection, 45 00:02:41,590 --> 00:02:43,150 and you'll need a Google account, 46 00:02:43,150 --> 00:02:46,400 because we're using the Google infrastructure again for the MOOC. 47 00:02:46,400 --> 00:02:49,989 You'll need plenty of motivation, and you'll need a bit of time, as well. 48 00:02:49,989 --> 00:02:53,099 A few hours each week for the 5-week duration of the course. 49 00:02:53,099 --> 00:02:56,599 You should allow a little bit more for this course than the previous one, 50 00:02:56,599 --> 00:03:00,680 because the activities are more advanced and a little bit more demanding. 51 00:03:00,680 --> 00:03:02,470 By the way, we've got new music, as well. 52 00:03:02,470 --> 00:03:04,879 We're playing something written by a friend of mine. 53 00:03:04,879 --> 00:03:05,690 You'll love it. 54 00:03:05,690 --> 00:03:10,099 In fact, the music alone I think is worth the price of admission, 55 00:03:10,099 --> 00:03:10,989 which is zero, 56 00:03:10,989 --> 00:03:11,720 by the way. 57 00:03:11,720 --> 00:03:14,370 This course is completely free. 58 00:03:14,370 --> 00:03:16,250 More Data Mining with Weka, 59 00:03:16,250 --> 00:03:18,450 coming soon to a computer near you! 60 00:03:18,450 --> 00:03:19,470 Hope to see you there!