Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2021 | Simplilearn

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This Machine Learning basics video will help you understand what Machine Learning is, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into self-learning mode without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results.
The below topics are explained in this Machine Learning basics video:
1. What is Machine Learning? ( 00:21 )
2. Types of Machine Learning ( 02:43 )
2. What is Supervised Learning? ( 02:53 )
3. What is Unsupervised Learning? ( 03:46 )
4. What is Reinforcement Learning? ( 04:37 )
5. Machine Learning applications ( 06:25 )
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🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?AugustTubebuddyExpPCPAIandML&Comments& 🔥AI Engineer Masters Program (Discount Code - YTBE15): www.simplilearn.com/masters-in-artificial-intelligence?SCE-AIMasters&Comments& 🔥AI & Machine Learning Bootcamp(US Only): www.simplilearn.com/ai-machine-learning-bootcamp?MachineLearningscribe&Comments& 🔥 Purdue Post Graduate Program In AI And Machine Learning: www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?MachineLearningscribe&Comments&

[@shifabegum5142]

G.

[@Abdullah-mg5zl]

**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!! :)

[@SimplilearnOfficial]

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-mg5zl]

@@SimplilearnOfficial Thank you! Definitely will, I love you guys' videos! :) Great job and keep it up!

[@SimplilearnOfficial]

Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :)

[@JSquare00]

i need help

[@SimplilearnOfficial]

Yes, what could we do for you?

[@theeagleeyeexplorer4111]

Quite great. An Amazing one explaining the ML basis.!! 1. Supervised learning. 2. Supervised learning after Feedback (Rein inforced learning) 3. Unsupervised learning.

[@SimplilearnOfficial]

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'.

[@misterpueblo26]

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!

[@SimplilearnOfficial]

Glad you enjoyed it! Thank you for watching!

[@hidgik]

I am from a health care background, but I could effortlessly understand everything she said. Excellent introduction.

[@SimplilearnOfficial]

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!

[@nrd10]

Literally learnt more from you than 4 years in college

[@SimplilearnOfficial]

We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!

[@vinaymotwani8946]

Is machine learning this much interesting in college also

[@suprecam9880]

…yet you still misspelled ‘learned,’ if only there was a video for that…

[@sunithacatherinjoseph2411]

😁👍

[@drmohitkashyap1661]

/

[@moiedmajaaz1669]

Labeled =supervised Unlabeled= Un-supervised And finally Enforcement Learning = Learning from results and upgrading . Tq for the explanation

[@SimplilearnOfficial]

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!

[@MennaAMoataz]

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 ☺️

[@SimplilearnOfficial]

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!

[@dipendrayadav1113]

You guys at Simplilearn are doing great service by making these educational videos. It helps me a lot.

[@SimplilearnOfficial]

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 :)

[@mallikonduri]

@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! 🙂

[@SimplilearnOfficial]

Glad it was helpful!

[@kshitizshrestha9398]

The recommended videos which we are getting in the KZbin PAGE is one of the live examples of machine learning !!

[@SimplilearnOfficial]

You are right about that!

[@amilcarc.dasilva5665]

wonderful and fantastic tutorial! It's really helpful. The explanation is so clear. thumb up to the tutor.

[@SimplilearnOfficial]

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 :).

[@AdnanKhan-iz9zb]

I'm impressed by the way you taught. Teacher should to be like you.

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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-iz9zb]

@@SimplilearnOfficial yes, already did. Thanks.🙏

[@anuproy8855]

@@AdnanKhan-iz9zb e3

[@anuproy8855]

@@AdnanKhan-iz9zb e3

[@prasadchiluka5509]

@@SimplilearnOfficial re Jo inIn

[@malvichaudhary]

Awesome, I am glad to watch this video about Machine Learning. Such a simple and clear explanation. Thank you!

[@SimplilearnOfficial]

Glad it was helpful!

[@HostDotPromo]

Machine learning is a game changer 📈

[@SimplilearnOfficial]

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.

[@rashmi1kanta1]

Want to Enroll & Get Certified ,, Who are best institute in NCR with affordable Price with high placement

[@SimplilearnOfficial]

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.

[@sabyasachibanerjee124]

Simple explanations work best. This one is a classic example. Better than the many others that I have looked at to have a basic understanding go Machine Learning.

[@SimplilearnOfficial]

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!

[@jssivakumar]

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 -

[@SimplilearnOfficial]

Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!

[@avijeetbiswal8421]

Loved the video..it's very informative and insightful under 8 mins.. Quiz Answers: 1st and 2nd are supervised while 3rd is unsupervised

[@SimplilearnOfficial]

Hi Avijeet, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@maheshshendge9896]

@Simplilearn , wonderful and fantastic tutorial! It's really helpful 1,2 are supervised learning and 3 one is unsupervised

[@SimplilearnOfficial]

Glad it was helpful!

[@sanjeevmalhi4336]

It's very easy to understand how ML algorithms work. Thanks for it.

[@SimplilearnOfficial]

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 :)

[@sancharichatterjee56]

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!

[@SimplilearnOfficial]

Glad you enjoyed it!

[@RANDOMCHILD2010]

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.

[@SimplilearnOfficial]

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 :)

[@kirubababu7127]

In KZbin, It can display the videos as per our frequent past search.

[@SimplilearnOfficial]

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_Muguai]

Or your likes or dislikes after watching them.

[@jaisonj6688]

I got impressed by this tutorial and interested to learn Machine Learning.. Can you guide me..

[@SimplilearnOfficial]

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.

[@poojaritulasi7680]

What is the use of machine learning .iam looking for good soft ware

[@SimplilearnOfficial]

@@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.

[@pratikzade]

I recently join your team,because i lovet it. Excellent work

[@SimplilearnOfficial]

WoW! we are glad you joined our community. Thanks for your love and support!

[@kaustavsen7958]

youtube recommended videos are the biggest example of machine learning , bcoz it recommends us videos on the basis of our history. AM I CORRECT?

[@SimplilearnOfficial]

Yes, you are absolutely correct. Search engine uses Machine learning algorithm to do the recommendation system. Thanks.

[@festuskapkea8150]

And that is what machine learning does

[@MeryKate]

Thank you for such a good explanation!

[@SimplilearnOfficial]

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

[@jatinchenani9844]

I liked your video. Now youtube will recommend me your other videos without actually searching for them. This is awesome. This is Machine Learning.

[@SimplilearnOfficial]

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.

[@gametechnician721]

Abnormally simplified topic on machine learning. Great job! Well done!

[@sweety23.789]

Respected ma'am, the video was highly informative. Thank you ma'am for teaching so many concepts about machines😄😄

[@SimplilearnOfficial]

Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@mehrsalaudeen9101]

Please help me to learn more ...My Email Id is [e-mail]

[@SimplilearnOfficial]

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

[@zabiansari9282]

[e-mail]

[@zabiansari9282]

Hi

[@vigneshwaran7421]

[e-mail]

[@kazimohammadshafiuddin2601]

Simplilearn I want to expertise on machine learning and succeed in this field. Email : [e-mail]

[@SimplilearnOfficial]

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.

[@sreeyaaladanda]

youtube itself is the best example of machine learning ..because it automatically recommends the videos based on our past history!!!

[@SimplilearnOfficial]

Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!

[@manuabaraham3653]

I am reading 21 lessons for 21st century ..these words are often coming ...it really helpful

[@SimplilearnOfficial]

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.

[@mainakdasgupta268]

The video was quite interesting and informative. I would like to be your part of learning ML.

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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!

[@arockiadass17]

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.

[@SimplilearnOfficial]

It is certainly a good use case for Machine Learning.

[@soumitrachakrabartee_lazyCoder]

Well explained by this video :) Scenario 1: Supervised Learning. Scenario 2: Supervised Learning. Scenario 3: Unsupervised Learning.

[@SimplilearnOfficial]

"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'."

[@jasiribrahim917]

Wonderful editing and we can understand easily. Answers: 1: supervised 2: supervised 3: unsupervised

[@SimplilearnOfficial]

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'.

[@anoopdwivedi1203]

1st & 2nd -supervised learning 3rd is Reinforced learning. Thanku , you teach us great 🙏

[@SimplilearnOfficial]

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[@VickyMei]

these examples are so helpful, thanks for making this video! YOU ROCK!

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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.

[@ma07info93]

Thank you mam 💕 I prepared my presentation about machin learning here and I was much appreciated Thanks a lot 💖

[@SimplilearnOfficial]

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!

[@ballukiduniya6214]

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

[@SimplilearnOfficial]

Hi Bhawna, we are glad that you like our videos! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@poojagupta830]

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?

[@SimplilearnOfficial]

Hi Pooja, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@plakshminarayana2471]

Thank you pooja for your answers it helped me to understand

[@hyo2109]

A day before exam and this video really helped with the concepts and queries, thanks!

[@SimplilearnOfficial]

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.

[@mihirthakkar9190]

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

[@SimplilearnOfficial]

Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!

[@kr4k3nn]

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 =.

[@SimplilearnOfficial]

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.

[@mainiyale1773]

Great video, very easy to understand. Thanks Simplilearn....

[@SimplilearnOfficial]

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!

[@anveshkumar581]

amazing description by short and ease of understanding.. Thank You :-)

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Hey Anvesh, 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 :)

[@muffledd]

thank you for making this! really opened my eyes to the world of how machine learning AI is like :)

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We are delighted to have been a part of your learning journey! If you want to continue honing your skills and keeping up-to-date with industry trends, check out our course offerings in the description box.

[@sakshipawar5837]

Thank you for such great video. I hope my all concepts will be cleared through this sessions🙌

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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!

[@StevonStevons]

"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" 😐

[@ganeshakhil879]

😂😂

[@terrancejfry497]

Lol

[@whenmathsmeetcoding1836]

😂😂

[@rehmanchughtai]

:)

[@whenmathsmeetcoding1836]

Ha ha I think kelvin plank law...

[@bijayarath4655]

Great video to start with machine learning . The explanation is really helpful for a beginner like me

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Hey Bijaya, 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 :)

[@amittodkari5351]

I liked this video so much as it explained me machine learning introduction very easily, now I am going to watch all the videos

[@SimplilearnOfficial]

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!

[@naturelover5371]

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?

[@SimplilearnOfficial]

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-ck3bv]

Mudit Goyal Dumbass , 1 is supervised not supeervised

[@sancharichatterjee56]

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

[@SimplilearnOfficial]

Thank you for watching our video!

[@PushpendraKumar-hy5pd]

Given superb knowledge of Machine Learning to understand the basic with giving examples ! You are really good !

[@SimplilearnOfficial]

Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@yasasviupadrastaqbjhdjhkmy7270]

1,2 are supervised learning. 3 is reinforcement learning in Quiz.. Video was good, understanding the concepts.. Thank you..

[@SimplilearnOfficial]

You are welcome

[@nuwanweeraratne6902]

Truly a REMARKABLE explanation. Can you do a video about active learning too?

[@SimplilearnOfficial]

Great suggestion! We will share it with our team.

[@poojanawle6337]

Amazing video!! Thanks for sharing the knowledge. The answers are : 1.Supervised 2.Supervised 3.Unsupervised, right?

[@SimplilearnOfficial]

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'.

[@slahmadi]

@@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?

[@SimplilearnOfficial]

Yes, a decision tree is a supervised learning algorithm and is it used for classification problems."

[@hemanttyagi8187]

Thank you so much @simplilearn for such a great explanation. You are just fantastic. Thanks again

[@SimplilearnOfficial]

Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@Maths4SHS]

i understood the concept of machine learning in less than 10 mins. thank you.

[@SimplilearnOfficial]

Glad it helped!

[@manasikailas]

A great gratitude towards simplilearn...really informative video...☺

[@SimplilearnOfficial]

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 :)

[@tejashwinirangam8216]

I have exam tomorrow, and this just one video boosted my confidence to write the exam well with your easy explanations...😊

[@SimplilearnOfficial]

Hello thank you for watching our video .We are glad that we could help you in your learning !

[@ResearchifyPhD]

I admire your teaching skill. The reason why simplilear is the first choice of the learner.

[@SimplilearnOfficial]

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.

[@juandalepringle5055]

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"

[@SimplilearnOfficial]

Hello thank you for watching our video .We are glad that we could help you in your learning !

[@pratibhalilhare3060]

yeah wow!!! you explained so nice...😍😍 ans is 1. super 2. super 3.unsuper am i correct???

[@SimplilearnOfficial]

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'.

[@wheeloftime2908]

@@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

[@SimplilearnOfficial]

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.

[@parvanator]

I used supervised learning to decide: 1. Supervised. 2. Supervised. 3. Unsupervised.

[@SimplilearnOfficial]

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'.

[@mr__atiur]

I have been trying to understand this concept for 3 days. Fortunately got your video and thanks for video.

[@SimplilearnOfficial]

Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

[@annas6516]

Examples of machine learning: customized ads on social media & other websites based on what you just googled, population growth prediction between census, estimated time of arrival & best routes on map apps etc.

[@SimplilearnOfficial]

Good examples, Anna!

[@gvsmchaithanya2847]

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

[@SimplilearnOfficial]

Hi Chaithanya, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@gvsmchaithanya2847]

Thanks for your answers and correcting me where did some mistake in quiz but I learned it thank you so much simplilearn

[@SimplilearnOfficial]

You are very welcome Chaitanya. Do subscribe to the channel and stay tuned.

[@yr5096]

You cleared my chart doubts in a single video

[@SimplilearnOfficial]

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 :)

[@tarunsraina7243]

Hands down to the best introduction to machine learning!!

[@SimplilearnOfficial]

Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

[@tamilarasisundar5293]

Superb!! Very informative and detailed explanation. Liked it and would like to learn more of ML

[@SimplilearnOfficial]

Awesome, thank you!

[@mohammadnazmussakib8202]

Awesome summary. Loved it.

[@SimplilearnOfficial]

Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).

[@ramsharma832]

nice summary

[@ramsharma832]

Like It

[@plainwhitekebaya]

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!

[@SimplilearnOfficial]

Glad you liked it!

[@bangbang2876]

Based on ML, SimpliLearn models those videos which could cater Huge Knowledge and important numerous Subscribers😃

[@SimplilearnOfficial]

Thanks for watching our video. Cheers!

[@parth3059]

The most fine examples and what a great understanding I request not to stop this

[@SimplilearnOfficial]

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 :)

[@sagarsrivastava7573]

1->supervised 2->supervised 3->unsupervised

[@SimplilearnOfficial]

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'.

[@pr00thmatic]

amazing video!! I love your voice... I can even imagine Ruby (from Steven Universe) explaining all of this, and that just makes learning about machine learning an awesome experience!!! anyway... i would have loved that the explanation about supervised and unsupervised machine learning to be longer... i didn't really get it

[@SimplilearnOfficial]

Hey Vengadora, 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 :)

[@abisoyeajayi1264]

I did not get it initially too but from the answer to the question and explanation/practical examples, I was able to understand accurately.

[@SimplilearnOfficial]

We are glad to hear it. Do subscribe to our channel and don't forget to hit the like button. Cheers!

[@joaomarcosrecovery]

K-nearest neighbors algorithm example really opened my mind to understand how it works

[@wheeloftime2908]

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

[@anjaneyupadhyay1306]

1 - Unsupervised because FB checks your friends face using image recognition 2 - Supervised 3 - Unsupervised Is this right?

[@SimplilearnOfficial]

Hi Anjaney, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@bharathsistk]

@@SimplilearnOfficial In scenario 3, if you say the suspicious transactions are not defined. Does that means the system might know the valid transaction.?

[@SimplilearnOfficial]

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.

[@heliocunha4791]

@@SimplilearnOfficial There is a mistake on the answer, Netflix uses AutoEnconders, and it is unsupervised learning...

[@ishagupta7592]

Scenario 1 supervised Scenario 2 reinforced Scenario 3 unsupervised

[@SimplilearnOfficial]

Hi Isha, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.

[@SimplilearnOfficial]

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'.

[@AshMenon]

This was a really good summary, thank you!

[@SimplilearnOfficial]

Glad you enjoyed it!

[@sonianaidu1437]

Thank you so much! This was very clear and informative. :)

[@SimplilearnOfficial]

Hey Sonuschka, we're glad you enjoyed this video! If you want to learn more, you can check out this playlist: kzbin.info/www/bejne/q5zdd3xvp8yqnLc And don't forget to like, share and subscribe to our channel! :)

[@mustafabohra2070]

Facebook face recognition with tagged data - Supervised learning Movie recommendation - Unsupervised Fraud detection - Unsupervised

[@SimplilearnOfficial]

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'.

[@mohdtaiyabkhan4186]

Scenario-1: supervised Scenario-2: supervised Scenario-2: unsupervised

[@SimplilearnOfficial]

"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'."

[@ashisd5705]

Excellent explanation in simplest way. Great video !!!

[@SimplilearnOfficial]

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 :)

[@priyanshibhattacharjee3789]

This is the first video of yours I'm watching, and it's so good that I subscribed right away 💯

[@SimplilearnOfficial]

Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!

[@sagessemusic5391]

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

[@logicalrishi]

To me the 3 scenarios looks like 1. Supervised 2. Supervised 3. Unsupervised

[@SimplilearnOfficial]

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'.

[@omkarwhaval73]

Why sir scenario one has supervised lwarning

[@SimplilearnOfficial]

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'.

[@VinodRS01]

And if photo is not tagged ..?

[@SimplilearnOfficial]

It will come under unsupervised learning.

[@aravindmuthusamy5383]

KZbin recommends and shows the type of videos based on which we watched before.Which type of learning is happening here?Can anyone explain?

[@SimplilearnOfficial]

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

[@sarthakdehadray1059]

I found this machine learning series because of "Machine Learning". So thank you "Machine Learning" and of course thank you Simplilearn.

[@SimplilearnOfficial]

Hi Sarthak, thanks for appreciating our work and for the wonderful comment. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!

[@sujay6556]

After watching ur video I got interest in learning machine learning Such an crystal clear explanation 🙂

[@SimplilearnOfficial]

Glad to hear that

[@sagessemusic5391]

Then you must download the Anaconda package and start coding

[@AllDefinition]

Scenario-1: supervised Scenario-2: supervised Scenario-2: unsupervised Am i correct,mam?

[@SimplilearnOfficial]

"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'."

[@angelflyinghigh1300]

@@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"

[@karandhanjal1079]

Simplilearn 🙌🏻

[@antoniosiu1426]

what i thought too

[@Stinow]

@@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 :)

[@RadioactiveChutney]

We can analyse the comments like machine learning to find answers 😁😁

[@amitpant3738]

You got it, what is machine learning hahaha.

[@pradnyaghodge2467]

Lol...

[@MukeshKumar-kb1mh]

Thanks to simplelearn pls make videos on all the subjects and topics ❤ and yeah the video helped ALOT ❤🎉

[@veeraiah]

Amazing explanation about the ML someone like me who is new to ML. Thank you!

[@SimplilearnOfficial]

Glad it was helpful!

[@shockey3084]

it's a great video

[@SimplilearnOfficial]

Hi Ashok, we appreciate the kind comment! enjoy!

[@snehakedambadi4114]

Scenario 1 - Supervised Learning, Scenario 2 - Reinforcement Learning, Scenario 3 - UnSupervised Learning

[@SimplilearnOfficial]

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'

[@dna1238]

great intro to the basics of machine learning , Thank You.

[@SimplilearnOfficial]

Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

[@subhasis9923]

you Defined the besics of mechine learning a very simple way amazing video

[@SimplilearnOfficial]

Glad you think so!