Applied Machine Learning in Cyber Security

Okay, yeah, you know, you know, hello, I know. Oh, do you know? Oh, do you know? Besides my domain topic, I already apply machine learning. Never that the Howard. Yeah. Oh, it's not the combat. I met. Which are what apply as well. What he got behind the machine. I don't know. I don't know. I'm going to do. I don't know. The introduction of the study point in the problem. How do you got to know the majority of the machine learning scientists? Yes, I just got you. Well, now that you can try to get the idea to send you by now, which I'll take them. You know, I was happy that the I was teaching my city library to get the idea of the little one that I had to do. I was like, oh, yeah, but you EGI artificial general intelligence, so our policy. The EGI, the other EGI, the other solution, or the EGI, the other decision tax, you know, we'll be in the program or EGI local, which has to also more, of your, So let's see, thread detection in socks, we don't have to go down to get flashed at the top notch. So my socks are very random. So the machine and the majority of the population is not the majority of the population. I'm already doing it. Same same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same. Same So, the. or decision decision making. I want to go to the pretty low. But he. No, they are going to do it. I think I will be I'm a Tia, you're a bad guy. I don't see a video. in the air, she gave me that she did. I'm a computer associate. I got to go. The machine learning. I got to go. Oh, yeah. The machine learning. and computing and learning deep learning. I don't know. The history was all Detroit. The EMI, the developer, the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI, the development of the EMI I think we know who is he doing me. I said, who not by not the, you are the, you are the, you are the, you are all I can go to the, I'm sorry. Okay. Okay. The . uh, AM was also machine learning, but the cluster of learning, the high gaming, nobody, And the analysis case of the title, I know what I had to do. I had to see not what I got. Jody, uh, sorry, but I don't see my don't see ID data. That's the idea. I'll tell you, I'll tell you about kids. I like that. I'm going to know what the LSDM. I know what the majority. I'm not going to see. You know, I see. I know what the data is. You know, I don't want to know that. I like the idea that I be sure I like the idea about the idea about the idea of the new thing that I can do is to go to the new thing. I think the idea of the new thing that I know. Oh yeah, I have a lot of the. This line are just to try to be done. The SOS and it's well as any minute. And the BTC and the other one. The other one. The other one. The other one. So. Hello. Hello. Yeah. Yeah. Okay. Yeah. Yeah. Okay. Oh, probably. The data set is the first generation machine learning machine learning. The machine learning learning is supervised by the lab link. The lab link is the same. The data set is the But today, I think that's the problem. I got to go to go to say, I got to and the joint probability, I've got the given, and how condition of probability, and the real relation also my formula. I am a marginal sub your formula, right? So the Y type of the BC or end of your plan, basically, you have to, The formula is the formula. The formula is the evaluation of the data. So I know the law will be like me to give him a circle on low immediate side on be like me, a house where a side on spent a lot of the internet, I made a know I how did you know, so you know, we're trying to do that. I pay actually less or. We had beat it, we like this. I spent a lot of the internet, so yeah, spent it as a colonial general, the expanded it's like a lonely, I put it as I know. train train, train, train, train, train, train, train, train, train, train, train, train, train, train, train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train train, train, train, train, train, train, train, train and now that the probability, the probability, the probability, the probability, even if you are independent, so you have to be naive, given from the formula, the nine weeks, so they made the machine learning on some of the elements, so, you know, the anniversary of the course, and the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal, the goal The first time I Okay. You know, the module, you know, so in the minute, I know, but not already, or if you are, you know, but I'm all the pilot, so the module, you know, so it's, you know, you have a programmer. You know, the final year, you are finally, it is a size data and run a month, you know, and to the final year, but I should have finally, she, and I find you, you know, you know, immediately, immediately, I mean, you should have a immediate, you know, you find readable, a fine, we find real, I don't know, but the release or the ACO bullet, but directly, but we look at a little less, you know, I love it to my mother, no, be higher, you have five, be higher, I find the dietary benefit, I'm on the, I find this to go, you know, reload like that, reload like that. So, you know, The data, so I put it on the camera. I mean, I did a crazy. I know people are losing a good message. I know. I mean, I spent a few people. So, you know, I'm not going to go to me. I got to go to me. I got to go to me. uh, the mother to lower our channel, but only so my image had a real dinosaur. Today, I'm a second. I'm a grandma. I don't immediately. I mean, finally, I don't have to be one. I mean, if Massey-de-jee-la-en-pa-lo-me-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-e-wa-wa-e-wa-e-wa-wa-e-wa-wa-e-wa- I saw the only side loan you was the who did the adage to know what was like alone the loan in the you know what listener table they have they me issue like it all the muscle and ham was by what's of your she lay me my forward a well see a call low the my teller a dot today number of how well got the lossy is but what I lost you know to tell preparation of the has to know the treatment detail it is another treatment hammer Okay. How did you train? I know. and the Well, I have spent it a lot, but it's not being like a finite immediate, my person, the one I have to be been a lot of spent spent in my part. Yeah, it's for all your why. You are the other city done it. No, but I know I don't know. I know I have no more spent up my number. I'm gonna more spent. Who may I swing? The email to know I have to do it. I don't know why I had a little bit of a good answer. improve flow. I know. I've been told that I've been told that I've been able to do a lot of things. I've been told that I've been able to do a lot of things. I've been told that I've been able to do a lot of things. I've been able to do a lot of things. . So, I was paying a two day and I spent a lot of money spent in a probability. Yeah, even about the winning and look at this. So, when they do the power, they are. Oh, I'm not going to sign it. But also, I know I got to know, but I'm out. So I'm out of the way to go. Yeah, my DD formula. I'm not doing it. I don't know. I say I gave it that. Oh, I broke an M. I see your pin name. So, okay. So now, I'll take a video. What's got the Salon to Lone? What's immediate? So long to Lone? Train high did that a mom? No, I'm about to see you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you. See you I spent a year and I did not probably the last one. I'm a YB so I said, oh, I immediately about it. So the long, the long, the long, the. Yeah, probably the. Probably the. The product was you like the email. Hamness. The email. Hamness. The policy is famous. Plot shit. I have a very good name. I take a. Grandma. Yeah. I did. I had a. . Ma ham to get ham to mend the early on tabi like low a hammer that got the case when I don't see a well movie we are actually been but it's a person a bit shall I have it. It talks about it to do where collider has way I'm accurate and spending accuracy. Spend accuracy got all 99.35 so you know but my studies, yeah, got it. 98.00 who we are the dog on what accurate the draw on I look really I may have accuracy much no one nanny. I learned a bullet. Dima, so 86 and 76, but he had a QSG, a job, a pilot job, a lay so we have no, damn you know, okay, so you know, you know, you know, tabulated, you know, damn you know, how most about what you like, that hard day. Dima, so yeah, how much, uh, Daba, Hampton, I mean, as, yeah, this by me, that's very, yeah, I'm saying I need to go, so yeah, you are mine, it. The program in BCN, the numerical value of the maximum limitation is the minus 269. The value Oh my father, I'm alone. I know it's a my how to know what the child approach in a sub video. No, yeah, no, yeah, so. Oh, So one two three. So, we have to do a lot of things. So, I have probability to be the trial. Accuracy. Now, machine learning. Machine learning. Supervised learning. Supervised learning. You know, take care of the data. One movie video. I know bottomless. So, yeah, the answer was then. So, you know, what did I already doubt? Have you like that? I have my two. I know. So, Another step is to help the central government. So, we have by the USB or by the USB. And I'll go to the next one. I'll tell you, did I I see a bit of a shit out. I don't know. It is not a good story. I mean, so I don't know. But I'm a. But I'm a. But I'm a. Yeah, I see a bit. I said, you know, I did she did a. Oh, for a lot of crap for my team. So you know what? You know, I So, we have a number of values that are different. We have a number of values that are different. We have a number of values that are different. We have a number of values that are different. We have a number of values that are different. So, we have a number of values that are different. I'm a machine. I'll tell you in a time I also know did I did I clean the idea back to the operation of the app I'm happy war on my don't know what class I saw when I saw a little boy. I know I came in so I came in the middle of the dark the game. Yeah, I'm a model. Don't know what I was up. I was on a good know. No, you know, quite like I'm a a class up now who play a mess. I'm a a comma to know. Okay, so like the class I will buy me why I am a normal to come on the other day by today. I am a man. I am a little bit. I know be a little bit by and Sally Lama. I have a no official edition of my first year. I know my son. I know a whole color. But I have to master a new I have to go to the internet. No. It's a silly call. I say go she a little call. I say go to the So, we have to use 3D to 3D to be able to use. So, we have to use 3D to be able to use 3D to be able to use. me came me out told me to like the upward time me I do the result I don't know what did I follow you on the debate did I get a failure but young also out general very final doubt they were all a BC are the PCO told me all general visualize love later see my Dima visualize got you look at it and you didn't know I would I know a lot of what's out well Papa la be back on a be at the middle of my water to visualize the load. So, the glass iron loader, the mass of the body, the So, the original byte is the focus load. The original byte is the focus load. The original byte is the complement. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like, I don't know what I was doing. I was like Sorry. Why not at the hookah? You know, class, our, our class, our remote class area and supervised learning. And so, as you know, input, have you done? You know, a possible, impossible to be done. Yeah. Supervised learning, you know, I've got a new share linear regression. What? Classification linear regression. You know, I've taken it out of the video. Oh, my. So, I don't really lie. I'm a leader. I'm a leader. I said, I really like. I don't know what to do. But I don't know what to do. My classification, so it's the SVM. So that I'm sure don't know the. I don't know what I don't know. So, the feature of the feature is the same as the feature. So, the feature is the same as the feature. The feature is the same as the feature is the same as the feature. The feature is the same as the feature is the same as the feature. The I mean, I changed your name. I mean, I mean, I didn't know how to do it. I didn't know what you did. I did. I did. So I decided to organize the other time, my little bit, my HGP, you talk on a bottle, just actually I tell later, tell a little bit why did I leave it at the end of the wine? You know, the gone here, how are you got in my token? I know that I'm going to be why I am the model is fine. It's training, it's training, why I read the bullet to the malicious film of you. So I want to worry about the real kind of more real to the top of the one I will age I know accuracy will be why the model to me and I'll tell you to like on it accuracy at a lot. I don't know. That's more able to buy some. So did I go on the one to buy some that's more than in a time ago. I know I see I deal over. . The other thing I want to say is that the data is not a problem. I got singing you. I got singing you. I'm going to. I think you're going to. I go to school to say I go to industry, but I see it. I don't know And the flow flow flow flow, where like, and I do I did have a little bit about the, but I did that a little bit of the numerical value. Gonna see, you know, I had a no tell us a little bit of a guy. You know, and you know, I had to say, you take that. I can't think about the time to do minus one. I did on the end. You take it out. I draw the, you have to get it out. What are the, Oh, prediction of the same. I So, the first thing I was talking I don't know what you know about the other thing about it. I don't know what you know about the other thing about it. I will talk about the other way. I have to train the other way. I will talk about The classification. The classification. The classification. The precision. The precision. The Okay. . Okay. Okay, any Marabama Ramji.