"🔥Caltech Post Graduate Program In AI And Machine Learning - www.simplilearn.com/artificial-intelligence-masters-program-training-course?FI9rgwfU&Comments&KZbin 🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?FI9rgwfU&Comments&KZbin 🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?FI9rgwfU&Comments&KZbin 🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?FI9rgwfU&Comments&KZbin 🔥Caltech - AI & Machine Learning Bootcamp (US Only) - www.simplilearn.com/ai-machine-learning-bootcamp?FI9rgwfU&Comments&KZbin"
@shifabegum51422 жыл бұрын
G.
@Abdullah-mg5zl5 жыл бұрын
**Summary**: - machine learning is the general term for when computers learn from data - there are lots of different ways ("algorithms") that machines can learn - the algorithms can be grouped into supervised, unsupervised, and reinforcement algorithms* - the data that you feed to a machine learning algorithm can be input-output pairs or just inputs - supervised learning algorithms require input-output pairs (i.e. they require the output) - unsupervised learning requires only the input data (not the outputs) - here is how, in general, supervised algorithms work: - you feed it an example input, then the associated output - you repeat the above step many many times - eventually, the algorithm picks up a pattern between the inputs and outputs - now, you can feed it a brand new input, and it will predict the output for you - here is how, in general, unsupervised algorithms work: - you feed it an example input (without the associated output) - you repeat the above step many times - eventually, the algorithm clusters your inputs into groups - now, you can feed it a brand new input, and the algorithm will predict which cluster it belongs with * the first example in this video used the k-nearest neighbor algorithm, which is a supervised machine learning algorithm Hope that was useful to someone! Thanks for the video, really enjoyed it!! :)
@SimplilearnOfficial5 жыл бұрын
Wow! This is one of the best summaries! Thanks for the valuable input! Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
@Abdullah-mg5zl5 жыл бұрын
@@SimplilearnOfficial Thank you! Definitely will, I love you guys' videos! :) Great job and keep it up!
@SimplilearnOfficial5 жыл бұрын
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :)
@NoFluffReviews015 жыл бұрын
i need help
@SimplilearnOfficial5 жыл бұрын
Yes, what could we do for you?
@theeagleeyeexplorer41116 жыл бұрын
Quite great. An Amazing one explaining the ML basis.!! 1. Supervised learning. 2. Supervised learning after Feedback (Rein inforced learning) 3. Unsupervised learning.
@SimplilearnOfficial6 жыл бұрын
Wow! You got all the answers right. Thanks for your kind comment as well. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@moiedmajaaz1669 Жыл бұрын
Labeled =supervised Unlabeled= Un-supervised And finally Enforcement Learning = Learning from results and upgrading . Tq for the explanation
@SimplilearnOfficial Жыл бұрын
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
@misterpueblo263 жыл бұрын
wow! this is my first time actually researching this topic being a computer science student. i have got to say, this really brightened my mood and brought some light to my day/mind regarding my major! :) awesome stuff!
@SimplilearnOfficial3 жыл бұрын
Glad you enjoyed it! Thank you for watching!
@kaustavsen79585 жыл бұрын
youtube recommended videos are the biggest example of machine learning , bcoz it recommends us videos on the basis of our history. AM I CORRECT?
@SimplilearnOfficial5 жыл бұрын
Yes, you are absolutely correct. Search engine uses Machine learning algorithm to do the recommendation system. Thanks.
@festuskapkea81504 жыл бұрын
And that is what machine learning does
@hidgik5 жыл бұрын
I am from a health care background, but I could effortlessly understand everything she said. Excellent introduction.
@SimplilearnOfficial5 жыл бұрын
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
@soumitrachakrabartee_lazyCoder4 жыл бұрын
Well explained by this video :) Scenario 1: Supervised Learning. Scenario 2: Supervised Learning. Scenario 3: Unsupervised Learning.
@SimplilearnOfficial4 жыл бұрын
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@anoopdwivedi12033 жыл бұрын
1st & 2nd -supervised learning 3rd is Reinforced learning. Thanku , you teach us great 🙏
@SimplilearnOfficial3 жыл бұрын
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@nrd103 жыл бұрын
Literally learnt more from you than 4 years in college
@SimplilearnOfficial3 жыл бұрын
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
@vinaymotwani89463 жыл бұрын
Is machine learning this much interesting in college also
@suprecam98803 жыл бұрын
…yet you still misspelled ‘learned,’ if only there was a video for that…
@sunithacatherinjoseph24113 жыл бұрын
😁👍
@drmohitkashyap16613 жыл бұрын
/
@kshitizshrestha93984 жыл бұрын
The recommended videos which we are getting in the KZbin PAGE is one of the live examples of machine learning !!
@SimplilearnOfficial4 жыл бұрын
You are right about that!
@AdnanKhan-iz9zb4 жыл бұрын
I'm impressed by the way you taught. Teacher should to be like you.
@SimplilearnOfficial4 жыл бұрын
We are glad you found our video helpful, Adnan. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
@AdnanKhan-iz9zb4 жыл бұрын
@@SimplilearnOfficial yes, already did. Thanks.🙏
@anuproy88553 жыл бұрын
@@AdnanKhan-iz9zb e3
@anuproy88553 жыл бұрын
@@AdnanKhan-iz9zb e3
@prasadchiluka55093 жыл бұрын
@@SimplilearnOfficial re Jo inIn
@SimplilearnOfficial4 жыл бұрын
Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin
@zabiansari92824 жыл бұрын
zhtzabi@gmail.com
@zabiansari92824 жыл бұрын
Hi
@vigneshwaran74214 жыл бұрын
vignesh_waran@live.com
@kazimohammadshafiuddin26014 жыл бұрын
Simplilearn I want to expertise on machine learning and succeed in this field. Email : kazis.shafi@gmail.com
@SimplilearnOfficial4 жыл бұрын
Hi, thanks for watching out video. We have sent the Machine Learning guide to your inbox. Do subscribe to our channel and stay tuned for more.
@dipendrayadav11135 жыл бұрын
You guys at Simplilearn are doing great service by making these educational videos. It helps me a lot.
@SimplilearnOfficial5 жыл бұрын
Hey Dipendra, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@yasasviupadrastaqbjhdjhkmy72703 жыл бұрын
1,2 are supervised learning. 3 is reinforcement learning in Quiz.. Video was good, understanding the concepts.. Thank you..
@SimplilearnOfficial3 жыл бұрын
You are welcome
@sreeyaaladanda3 жыл бұрын
youtube itself is the best example of machine learning ..because it automatically recommends the videos based on our past history!!!
@SimplilearnOfficial3 жыл бұрын
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@avijeetbiswal84216 жыл бұрын
Loved the video..it's very informative and insightful under 8 mins.. Quiz Answers: 1st and 2nd are supervised while 3rd is unsupervised
@SimplilearnOfficial6 жыл бұрын
Hi Avijeet, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@poojanawle63375 жыл бұрын
Amazing video!! Thanks for sharing the knowledge. The answers are : 1.Supervised 2.Supervised 3.Unsupervised, right?
@SimplilearnOfficial5 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@slahmadi4 жыл бұрын
@@SimplilearnOfficial If you use the decision tree by using existing features to classify a transaction as fraud (1) and no-fraud (0) than you are using a supervised learning based on classification. Right?
@SimplilearnOfficial3 жыл бұрын
Yes, a decision tree is a supervised learning algorithm and is it used for classification problems."
@mihirthakkar91903 жыл бұрын
1) Facebook photo recognition based on tags in an example of supervised learning 2) NetFlix Movie recommendation is an example of unsupervised learning 3) Bank Fraud Detection is an example of reinforcement learning
@SimplilearnOfficial3 жыл бұрын
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@manuabaraham36533 жыл бұрын
I am reading 21 lessons for 21st century ..these words are often coming ...it really helpful
@SimplilearnOfficial3 жыл бұрын
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
@jasiribrahim9176 жыл бұрын
Wonderful editing and we can understand easily. Answers: 1: supervised 2: supervised 3: unsupervised
@SimplilearnOfficial6 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@sancharichatterjee563 жыл бұрын
Amazing video. Thank you Simplilearn. Example where I see application of machine learning could be KZbin itself. Once I watch a video on cooking, all recommendations on cooking video starts popping up!
@SimplilearnOfficial3 жыл бұрын
Glad you enjoyed it!
@jaisonj66885 жыл бұрын
I got impressed by this tutorial and interested to learn Machine Learning.. Can you guide me..
@SimplilearnOfficial5 жыл бұрын
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: kzbin.info/www/bejne/q5zdd3xvp8yqnLc This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@poojaritulasi76804 жыл бұрын
What is the use of machine learning .iam looking for good soft ware
@SimplilearnOfficial4 жыл бұрын
@@poojaritulasi7680 Hi Poojari, machine learning is used in the various fields now. We recommend you check out the below link to know about Machine Learning and why it matters a lot: www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article.
@MennaAMoataz4 жыл бұрын
This video is quiet frankly down to point. I was even excited when I begun this field and the different things you could indulge in and improve for a business. It really is helping me and my career. I am even starting my own channel to breakdown some of the concepts that I found hard to understand about different algorithms and how they work. Check it out and for any starters, do tell me what you find hard at first to grasp when begging into the field ☺️
@SimplilearnOfficial4 жыл бұрын
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
@mr__atiur4 жыл бұрын
I have been trying to understand this concept for 3 days. Fortunately got your video and thanks for video.
@SimplilearnOfficial4 жыл бұрын
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
@subhasis99232 жыл бұрын
you Defined the besics of mechine learning a very simple way amazing video
@SimplilearnOfficial2 жыл бұрын
Glad you think so!
@maheshshendge98962 жыл бұрын
@Simplilearn , wonderful and fantastic tutorial! It's really helpful 1,2 are supervised learning and 3 one is unsupervised
@SimplilearnOfficial2 жыл бұрын
Glad it was helpful!
@naturelover53715 жыл бұрын
Well! First of all thanks for this wonderful and informative video. The answer to the questions in the video might be 1.supeervised 2. supervised 3 . unsupervised Am I correct?
@SimplilearnOfficial5 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@GoodGuy-ck3bv5 жыл бұрын
Mudit Goyal Dumbass , 1 is supervised not supeervised
@sanjeevmalhi43366 жыл бұрын
It's very easy to understand how ML algorithms work. Thanks for it.
@SimplilearnOfficial6 жыл бұрын
Hey Sanjeev, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@arificialintelligence3 жыл бұрын
Excellent summary. I have shared this with all my linkedin connections.
@SimplilearnOfficial3 жыл бұрын
Much appreciated!
@jatinchenani98444 жыл бұрын
I liked your video. Now youtube will recommend me your other videos without actually searching for them. This is awesome. This is Machine Learning.
@SimplilearnOfficial4 жыл бұрын
Great to hear it. This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@poojagupta8306 жыл бұрын
Amazing amazing video! I have shared with many friends over WhatsApp, can't thank you enough. Quiz answer - scenario 1 is supervised, scenario 2 is supervised, and scenario 3 is unsupervised?
@SimplilearnOfficial6 жыл бұрын
Hi Pooja, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@plakshminarayana24713 жыл бұрын
Thank you pooja for your answers it helped me to understand
@kirubababu71275 жыл бұрын
In KZbin, It can display the videos as per our frequent past search.
@SimplilearnOfficial5 жыл бұрын
Exactly! Search engines work based on Machine Learning concepts. Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: kzbin.info/www/bejne/q5zdd3xvp8yqnLc This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@William_Clinton_Muguai3 жыл бұрын
Or your likes or dislikes after watching them.
@HostDotPromo5 жыл бұрын
Machine learning is a game changer 📈
@SimplilearnOfficial5 жыл бұрын
Yes, it is indeed a game changer. Check out our Machine learning playlist to know about the fundamentals courses and algorithms: kzbin.info/www/bejne/q5zdd3xvp8yqnLc. For rest of the course, you need to sign up for our Machine learning Certification Training Course: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@rashmi1kanta15 жыл бұрын
Want to Enroll & Get Certified ,, Who are best institute in NCR with affordable Price with high placement
@SimplilearnOfficial5 жыл бұрын
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: kzbin.info/www/bejne/q5zdd3xvp8yqnLc This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@juandalepringle50552 жыл бұрын
This was very helpful, It has been hard grasping the idea we have managed to create machines, or scripts, that run mostly off of numbers and organization, to "learn"
@SimplilearnOfficial2 жыл бұрын
Hello thank you for watching our video .We are glad that we could help you in your learning !
@ResearchifyPhD3 жыл бұрын
I admire your teaching skill. The reason why simplilear is the first choice of the learner.
@SimplilearnOfficial3 жыл бұрын
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
@StevonStevons5 жыл бұрын
"Hey Siri, can you remind me to book a cab at 6 pm today?" "Here's what i found on the web for Keanu Reeves' Sixteenth Birthday" 😐
@ganeshakhil8794 жыл бұрын
😂😂
@terrancejfry4974 жыл бұрын
Lol
@whenmathsmeetcoding18364 жыл бұрын
😂😂
@rehmanchughtai4 жыл бұрын
:)
@whenmathsmeetcoding18364 жыл бұрын
Ha ha I think kelvin plank law...
@malvichaudhary3 жыл бұрын
Awesome, I am glad to watch this video about Machine Learning. Such a simple and clear explanation. Thank you!
@SimplilearnOfficial3 жыл бұрын
Glad it was helpful!
@VickyMei5 жыл бұрын
these examples are so helpful, thanks for making this video! YOU ROCK!
@SimplilearnOfficial5 жыл бұрын
Hi Victoria, we are glad you found our video helpful. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
@aryadas77862 жыл бұрын
Hey I got really impressed by your explanation... Thank u!!!
@SimplilearnOfficial2 жыл бұрын
Hope you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
@sarthakdehadray10595 жыл бұрын
I found this machine learning series because of "Machine Learning". So thank you "Machine Learning" and of course thank you Simplilearn.
@SimplilearnOfficial5 жыл бұрын
Hi Sarthak, thanks for appreciating our work and for the wonderful comment. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
@pratibhalilhare30605 жыл бұрын
yeah wow!!! you explained so nice...😍😍 ans is 1. super 2. super 3.unsuper am i correct???
@SimplilearnOfficial5 жыл бұрын
Hi Pratibha, you got all the answers correct. Kudos. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@wheeloftime29084 жыл бұрын
@@SimplilearnOfficial I am a massive fan of visual aids and numerous example driven content and interesting narratives in learning and kudos to SL I love the headfirst set of books which heavily uses stories and visual aids I have a question.I am looking to sign up for a course in AI AND ML. My question is if lectures n SL will be heavily based on visual narrations and interesting examples throughout the course ? IF SO,that would be truly wonderful and clutter breaking
@SimplilearnOfficial4 жыл бұрын
That's great to hear it. Our courses do have visual narrations with 15+ real life industry projects. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
@tejashwinirangam82162 жыл бұрын
I have exam tomorrow, and this just one video boosted my confidence to write the exam well with your easy explanations...😊
@SimplilearnOfficial2 жыл бұрын
Hello thank you for watching our video .We are glad that we could help you in your learning !
@parvanator5 жыл бұрын
I used supervised learning to decide: 1. Supervised. 2. Supervised. 3. Unsupervised.
@SimplilearnOfficial5 жыл бұрын
Hi, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@deanlonagan14754 жыл бұрын
..humans do learn from past experiences but that alone stifles innovation and problem solving..we are good at learning about the right now too..
@SimplilearnOfficial4 жыл бұрын
Humans can do it because of our cognitive ability and sixth sense.
@CSwithAbdul4 жыл бұрын
Superb way of teaching as well as your voice.
@SimplilearnOfficial4 жыл бұрын
Thanks a lot!
@mallikonduri2 жыл бұрын
@Simplilearn Thank you for this video! Shows the power of simplicity and your ability to simplify things. And asking people to comment on the 3 scenarios, great engagement strategy! 🙂
@SimplilearnOfficial2 жыл бұрын
Glad it was helpful!
@amilcarc.dasilva56655 жыл бұрын
wonderful and fantastic tutorial! It's really helpful. The explanation is so clear. thumb up to the tutor.
@SimplilearnOfficial5 жыл бұрын
Hi Amilcar, we are glad that you found our video helpful and informative. Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).
@jssivakumar3 жыл бұрын
Great Video . Thanks Much. Quiz answers 1. Supervised - Naivebayes algorithm with tagged images (or) can be Reinforcement too due to images which will be a very expensive algorithm 2. Supervised - K-nearest neighbors -alogrithm- 3. Unsupervised -
@SimplilearnOfficial3 жыл бұрын
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@amitpatel78123 жыл бұрын
I appreciate how you teach with examples
@SimplilearnOfficial3 жыл бұрын
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
@Anish_Deshmukh4 жыл бұрын
Answers Scenario 1 - Supervised Scenario 2 - Supervised Scenario 3 - Unsupervised Please tell me if I am correct or not. Thank Simplilearn !
@SimplilearnOfficial4 жыл бұрын
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@arockiadass174 жыл бұрын
A real life problem which may need AI and ML: Examination Paper Evaluation/Correction which has descriptive questions. Two things : The accuracy level of earlier answers can be used to predict the confidence of accuracy of later answers. 2. Based on the other answers, a answer can be evaluated.
@SimplilearnOfficial4 жыл бұрын
It is certainly a good use case for Machine Learning.
@AllDefinition4 жыл бұрын
Scenario-1: supervised Scenario-2: supervised Scenario-2: unsupervised Am i correct,mam?
@SimplilearnOfficial4 жыл бұрын
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@angelflyinghigh13004 жыл бұрын
@@SimplilearnOfficial Why is scenario 3 unsupervised learning? How does the system know that sth is "fraud" without being fed in previous cases which were called "fraud"? Like it has to know the features that make sth "fraud" before it can identify sth as "fraud"
@karandhanjal10794 жыл бұрын
Simplilearn 🙌🏻
@antoniosiu14264 жыл бұрын
what i thought too
@Stinow4 жыл бұрын
@@angelflyinghigh1300 Hi Lucia, I would recon it (for example) compares properties of many transactions and puts the common ones in groups and thus sees which properties are anomalies (like, really big transaction amounts, or a never used bank account located far away, or many many small transactions with unclear description). But, that's just my two cents, I'm far from knowledgeable of Machine learning :)
@mainiyale17736 жыл бұрын
Great video, very easy to understand. Thanks Simplilearn....
@SimplilearnOfficial6 жыл бұрын
We are glad you found our video helpful, Maini. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
@joaomarcosrecovery Жыл бұрын
K-nearest neighbors algorithm example really opened my mind to understand how it works
@Maths4SHS3 жыл бұрын
i understood the concept of machine learning in less than 10 mins. thank you.
@SimplilearnOfficial3 жыл бұрын
Glad it helped!
@MeryKate5 жыл бұрын
Thank you for such a good explanation!
@SimplilearnOfficial5 жыл бұрын
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning Videos - kzbin.info/www/bejne/q5zdd3xvp8yqnLc
@manasikailas5 жыл бұрын
A great gratitude towards simplilearn...really informative video...☺
@SimplilearnOfficial5 жыл бұрын
Hey Manasi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@ballukiduniya62146 жыл бұрын
Wonderful video, it's made in such a way that a layman can also understand this..thanks a ton.. please share the answer of that quiz
@SimplilearnOfficial6 жыл бұрын
Hi Bhawna, we are glad that you like our videos! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Here are the answers to the quiz with the explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@LearnWithArjun3 жыл бұрын
Hello Simplilearn , Im 9 yrs old and very interested in machine learning. This video is very cool.
@SimplilearnOfficial3 жыл бұрын
Glad you liked it! We have a ton more videos like this on our channel. We hope you will join our community!
@bangbang28764 жыл бұрын
Based on ML, SimpliLearn models those videos which could cater Huge Knowledge and important numerous Subscribers😃
@SimplilearnOfficial4 жыл бұрын
Thanks for watching our video. Cheers!
@j.williamssteven18434 жыл бұрын
Scenario-1: supervised Scenario-2: supervised Scenario-2: unsupervised Am i correct,mam? Awesome summary. Loved it.
@SimplilearnOfficial4 жыл бұрын
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@sweety23.7894 жыл бұрын
Respected ma'am, the video was highly informative. Thank you ma'am for teaching so many concepts about machines😄😄
@SimplilearnOfficial4 жыл бұрын
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
@mehrsalaudeen91014 жыл бұрын
Please help me to learn more ...My Email Id is salaudeen03041969@gmail.com
@gvsmchaithanya28476 жыл бұрын
In my point of view 1- Scenario will be using the reinforcement learning. the reason is in the reinforcement example which is explained based on that only i am telling. 2 - scenario will be using the supervised learning. 3 - scenario will be using the unsupervised learning. If it's wrong please correct me. Thanks Simplilearn
@SimplilearnOfficial6 жыл бұрын
Hi Chaithanya, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Thanks for replying to the quiz Chaitanya. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@gvsmchaithanya28476 жыл бұрын
Thanks for your answers and correcting me where did some mistake in quiz but I learned it thank you so much simplilearn
@SimplilearnOfficial6 жыл бұрын
You are very welcome Chaitanya. Do subscribe to the channel and stay tuned.
@divyansharekar7534 жыл бұрын
It was a wonderful video which make me to Learn it very easy
@SimplilearnOfficial4 жыл бұрын
Glad you liked it!
@shahidbud38622 жыл бұрын
Really a great and oversimplified video.
@SimplilearnOfficial2 жыл бұрын
Glad it helped!
@anjaneyupadhyay13066 жыл бұрын
1 - Unsupervised because FB checks your friends face using image recognition 2 - Supervised 3 - Unsupervised Is this right?
@SimplilearnOfficial6 жыл бұрын
Hi Anjaney, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Hi Anjaney, you almost got everything right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@bharathsistk6 жыл бұрын
@@SimplilearnOfficial In scenario 3, if you say the suspicious transactions are not defined. Does that means the system might know the valid transaction.?
@SimplilearnOfficial5 жыл бұрын
This means that the model will study the pattern, evaluate whether the transaction done is normal as per the customer history and hence detect a suspicious transaction.
@heliocunha47915 жыл бұрын
@@SimplilearnOfficial There is a mistake on the answer, Netflix uses AutoEnconders, and it is unsupervised learning...
@pratikzade5 жыл бұрын
I recently join your team,because i lovet it. Excellent work
@SimplilearnOfficial5 жыл бұрын
WoW! we are glad you joined our community. Thanks for your love and support!
@yr50965 жыл бұрын
You cleared my chart doubts in a single video
@SimplilearnOfficial5 жыл бұрын
We are glad in clarifying your doubts. Do subscribe to our channel and do not forget to hit the bell icon for never miss another update. Cheers :)
@sujay65563 жыл бұрын
After watching ur video I got interest in learning machine learning Such an crystal clear explanation 🙂
@SimplilearnOfficial3 жыл бұрын
Glad to hear that
@sagessemusic53913 жыл бұрын
Then you must download the Anaconda package and start coding
@Round2Bermuda Жыл бұрын
Abnormally simplified topic on machine learning. Great job! Well done!
@sagarsrivastava75735 жыл бұрын
1->supervised 2->supervised 3->unsupervised
@SimplilearnOfficial5 жыл бұрын
Hi Sagar, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@sancharichatterjee563 жыл бұрын
1. FB case: Supervised scenario (photo tags become labels) 2. Netflix case: Supervised scenario (like and dislike of a movie/show become the label) 3. Bank fraud case: Unsupervised scenario
@SimplilearnOfficial3 жыл бұрын
Thank you for watching our video!
@nuwanweeraratne69023 жыл бұрын
Truly a REMARKABLE explanation. Can you do a video about active learning too?
@SimplilearnOfficial3 жыл бұрын
Great suggestion! We will share it with our team.
@Sujit3119882 жыл бұрын
Excellent video - short and yet very resourceful.
@SimplilearnOfficial2 жыл бұрын
Glad it was helpful!
@priyanshibhattacharjee37893 жыл бұрын
This is the first video of yours I'm watching, and it's so good that I subscribed right away 💯
@SimplilearnOfficial3 жыл бұрын
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@sagessemusic53913 жыл бұрын
Your first step to machine learning is you downloading the Anaconda package. It is free to download and contains software like Jupyter Notebook and spyder
@RANDOMCHILD20105 жыл бұрын
The video was quite interesting and informative. I would like to be your part of learning ML. It's very easy to understand how ML algorithms work. Thanks for it.
@SimplilearnOfficial5 жыл бұрын
Hey Natalie, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@plainwhitekebaya2 жыл бұрын
Amazing video! I was getting headache learning the same topic from a coding site, I guess there is more than one ways if understanding things. Thank you!
@SimplilearnOfficial2 жыл бұрын
Glad you liked it!
@sakshipawar58373 жыл бұрын
Thank you for such great video. I hope my all concepts will be cleared through this sessions🙌
@SimplilearnOfficial3 жыл бұрын
We are glad you found our video helpful, Sakshi. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
@sakshisoundrajan98973 жыл бұрын
I liked you're videos it's interesting and i can understand it better thanks for the video
@SimplilearnOfficial3 жыл бұрын
Glad it was helpful!
@mounikamusala99603 жыл бұрын
Thank you so much. I gain lot of knowledge from this video
@SimplilearnOfficial3 жыл бұрын
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
Hi Isha, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Hi Isha, you almost got everything right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@mainakdasgupta2685 жыл бұрын
The video was quite interesting and informative. I would like to be your part of learning ML.
@SimplilearnOfficial5 жыл бұрын
Hi Mainak, we are glad you found our video helpful and informative. Do show your love by subscribing our channel using this link: kzbin.info and don't forget to hit the like button as well. Cheers!
@PushpendraKumar-hy5pd4 жыл бұрын
Given superb knowledge of Machine Learning to understand the basic with giving examples ! You are really good !
@SimplilearnOfficial4 жыл бұрын
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@aravindmuthusamy53835 жыл бұрын
KZbin recommends and shows the type of videos based on which we watched before.Which type of learning is happening here?Can anyone explain?
@SimplilearnOfficial5 жыл бұрын
Deep Neural network concepts have been implemented for KZbin recommendation. For more detailed explanation, go through this blog: towardsdatascience.com/how-youtube-recommends-videos-b6e003a5ab2f
@kr4k3nn3 жыл бұрын
Great teacher. Great teaching skills. Try to add quiz question after explaining a concept on your upcoming videos, it really helps us to test our understanding on that topic. By d way great explanation =.
@SimplilearnOfficial3 жыл бұрын
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
@parth30594 жыл бұрын
The most fine examples and what a great understanding I request not to stop this
@SimplilearnOfficial4 жыл бұрын
Hey Parth, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@bec_Divyansh3 жыл бұрын
Thanks for the video , i understood the crux of ML with such ease!
@SimplilearnOfficial3 жыл бұрын
Glad it helped!
@mohammadnazmussakib82025 жыл бұрын
Awesome summary. Loved it.
@SimplilearnOfficial5 жыл бұрын
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).
@ramsharma8323 жыл бұрын
nice summary
@ramsharma8323 жыл бұрын
Like It
@mustafabohra20705 жыл бұрын
Facebook face recognition with tagged data - Supervised learning Movie recommendation - Unsupervised Fraud detection - Unsupervised
@SimplilearnOfficial5 жыл бұрын
Thanks for replying to the quiz, Mustafa. You almost got the right answer. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@sitaramsahoo54916 жыл бұрын
Facebook face recognition : supervised , netflex movie choice: reinforced , fraud detection : reinforced
@SimplilearnOfficial6 жыл бұрын
Hi Sitaram, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Hi Sitaram, thanks for replying to the quiz. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@sitaramsahoo54916 жыл бұрын
@@SimplilearnOfficial thank you for the beautiful explanations!!
@SimplilearnOfficial6 жыл бұрын
You are very welcome! Do subscribe to our channel and stay tuned!
@shanmukharaobudumuru44715 жыл бұрын
@@@SimplilearnOfficial fraud transactions to be reinforcement learning right ( as it gives a negative feedback when some enters their data incorrectly )
@ashisd57055 жыл бұрын
Excellent explanation in simplest way. Great video !!!
@SimplilearnOfficial5 жыл бұрын
Hey Ashis, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
@pratikgade1125 Жыл бұрын
Scenario 1 : supervised Scenario 2 : unsupervised Scenario 3 : unsupervised Is it correct?
@apekshakapoor1976 жыл бұрын
Umm 1st is supervised, 2nd also supervised, 3rd is unsupervised. Am i correct? Great video though, loved it!!
@SimplilearnOfficial6 жыл бұрын
Hi Apeksha, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
@SimplilearnOfficial6 жыл бұрын
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@researchitechindia5 жыл бұрын
Scenario 1 is supervised earning because machine know the data(both friend photo and their name). 2. Netflix is same as person identify song (high intensity high tempo ) 3. Fraud is unsupervised I guess. By the way video is good. It's wow in one word
@SimplilearnOfficial5 жыл бұрын
Hi Amogha, you are absolutely right about your answer and explanation. We really appreciate your kind comment. Do show your love by subscribing our channel using this link: kzbin.info and don't forget to hit the like button as well. Cheers!
@keshavcharan5 жыл бұрын
@@SimplilearnOfficial Hi a quick question, should the 3rd one be case of reinforcement learning because transactions are very important and there needs to be a feedback mechanism to recorrect if there is a false positive or false negative ?
@logicalrishi5 жыл бұрын
To me the 3 scenarios looks like 1. Supervised 2. Supervised 3. Unsupervised
@SimplilearnOfficial5 жыл бұрын
Hi Nitesh, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@omkarwhaval735 жыл бұрын
Why sir scenario one has supervised lwarning
@SimplilearnOfficial5 жыл бұрын
Hi Onkar, Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Hi Neha, Below are the right answers and explanation for the quiz. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'
@surajkumar16512 жыл бұрын
This was simply awesome and quiet easy to learn.. Thank you