WEBVTT 00:00.080 --> 00:00.920 Welcome back. 00:00.960 --> 00:05.520 In this video we're going to learn about real world examples of machine learning. 00:05.840 --> 00:11.320 The usage of ML is increasing with every passing day. 00:11.520 --> 00:19.960 This technology is spearheading into the future with exceptional possibilities and various use cases. 00:19.960 --> 00:25.560 The first field we can use machine learning is the facial recognition. 00:25.560 --> 00:30.320 Facial recognition is a big use case and benefit of ML. 00:30.480 --> 00:38.960 It has positively transformed various aspects of our everyday tasks and activities. 00:38.960 --> 00:47.360 This example paired with deep learning is leaving an impact on different industries and sectors. 00:47.600 --> 00:56.880 It aids in combating key social issues like detecting thieves, trafficking and smuggling in healthcare. 00:57.080 --> 01:05.760 It tracks the patient's history of drug abuse, medication and detects genetic diseases. 01:05.800 --> 01:10.450 The second use is self-driving technology. 01:10.490 --> 01:19.610 ML powers the self-driving technology, wherein sensors are used to collect data surrounding the car 01:19.610 --> 01:20.810 in real time. 01:21.050 --> 01:29.970 This real time data is employed to guide the car and navigate its response in varying situations. 01:30.250 --> 01:38.370 These could be an animal human crossing the road, red light, or another vehicle. 01:38.530 --> 01:43.010 The third use the natural language processing. 01:43.130 --> 01:51.050 Speech signals are supported and manipulated into text and commands by language models. 01:51.170 --> 02:02.050 This is pretty similar to how ML recognizes images, as software that is coded with AI has the potential 02:02.050 --> 02:06.810 to convert live and recorded speech into text files. 02:06.810 --> 02:15.980 So this is the usage of ML to transform the recorded speech or live speech into text files. 02:18.180 --> 02:23.740 Another ML usage is the social media optimization. 02:23.900 --> 02:33.260 All big platforms like Facebook, Twitter, and Instagram utilize AI, ML, and big data to enhance 02:33.260 --> 02:37.460 the user experience and offer better functionality. 02:37.620 --> 02:44.940 ML has played a big role where here in tackling cyberbullying and inappropriate content. 02:45.140 --> 02:55.140 Both there those risks the platform endangering its integrity and getting a bad image. 02:55.420 --> 03:03.860 When data is processed via deep neural network DNNs, these platforms get a better understanding of 03:03.900 --> 03:06.220 their user preferences. 03:06.500 --> 03:12.780 This leads to target advertising and content suggestions. 03:12.820 --> 03:15.940 Another usage spam filtering. 03:16.060 --> 03:22.950 Successful implementation of ML leads to email automation and spam filtering. 03:23.230 --> 03:31.830 This area is especially in focus for organizations that rely mostly on outbound leads. 03:32.030 --> 03:42.070 Spam filtering means getting a better grasp of the patterns that make the email content undesirable. 03:42.070 --> 03:45.470 Then we have the product recommendations. 03:45.590 --> 03:53.470 Targeted marketing in retail uses ML to learn more about user preferences. 03:53.750 --> 04:02.190 The third, demographic similarities, buying habits, and purchasing history are used to train the 04:02.190 --> 04:03.150 models. 04:03.150 --> 04:12.390 Depending on how good quality and accurate the data is, the predictions can either be bang or way off. 04:12.430 --> 04:19.710 Not buying or clicking through a product also creates a data point for future preference. 04:19.710 --> 04:25.150 Those are the most famous usage of machine learning.