Hidden Markov Model : Data Science Concepts

All about the Hidden Markov Model in data science / machine learning

[@totomo1976]

Thank you so much for your clear explanation!!! Look forward to learning more machine-learning related math.

[@rssamarth099]

This helped me at the best time possible!! I didn't know jack about the math a while ago, but now I have a general grasp of the concept and was able to chart down my own problem as you were explaining the example. Thank you so much!!

[@stevengreidinger8295]

You gave the clearest explanation of this important topic I've ever seen! Thank you!

[@chadwinters4285]

I have to say you have an underrated way of providing intuition and making difficult to understand concepts really easy.

[@13_yashbhanushali40]

Unbelievable Explanation!! I have referred to more than 10 videos where basic working flow of this model was explained but I must say that rather I'm sure that this is the most easiest explanation one can ever find on youtube , the way of explanation considering the practical approach was much needed and you did exactly that Thanks a ton man !

[@user-xj1pi5ec6x]

True experts always make it easy.

[@ashortstorey-hy9ns]

You're really good at explaining these topics. Thanks for sharing!

[@coupmd]

Wonderful explanation. I hand calculated a couple of sequences and then coded up a brute force solution for this small problem. This helped a lot! Really appreciate the video!

[@pinkymotta4527]

Crystal-clear explanation. Didn't have to pause video or go back at any point of video. Would definitely recommend to my students.

[@beyerch]

Really great explanation of this in an easy to understand format. Slightly criminal to not at least walk through the math on the problem, though.

[@froh_do4431]

really good work on the simple explanation of a rather complicated topic 👌🏼💪🏼 thank you very much

[@mohammadmoslemuddin7274]

Glad I found your videos. Whenever I need some explanation for hard things in Machine Learning, I come to your channel. And you always explain things so simply. Great work man. Keep it up.

[@ritvikmath]

Glad to help!

[@linguipster1744]

oooh I get it now! Thank you so much :-) you have an excellent way of explaining things and I didn’t feel like there was 1 word too much (or too little)!

[@spp626]

Such a great explanation! Thank you sir.

[@VascoDaGamaOtRupcha]

You explain very well!

[@louisc2016]

I really like the way you explain something, and it helps me a lot! Thx bro!!!!

[@beckyb8929]

beautiful! Thank you for making this understandable

[@hichamsabah31]

Very insightful. Keep up the good work.

[@zishiwu7757]

Thank you for explaining how HMM model works. You are a grade saver and explained this more clearly than a professor.

[@ritvikmath]

Glad it was helpful!

[@srijanshovit844]

Awesome explanation I understood in 1 go!!

[@songweimai6411]

Really appreciate your work. Much better than the professor in my class who has a pppppphhhhdddd degree.

[@paulbrown5839]

To get to the probabilities in the top right of the board, you keep applying P(A,B)=P(A|B).P(B) ... eg. A=C3, B=C2 x C1 x M3 x M2 x M1 ... keep applying P(A,B)=P(A|B).P(B) and you will end up with same probabilities as shown on the whiteboard top right of screen for the viewer. Great video!

[@ritvikmath]

Thanks for that!

[@ummerabab8297]

Sorry, but I still don't get the calculation at the end. The whole video was explained flawlessly but the calculation was left out. I don't understand. If you can please further help. Thankyou.

[@toyomicho]

@@ummerabab8297 Here is some code in python showing the calculations in the output, you'll see that the hidden sequence s->s->h has the highest probability (0.018) ##### code #################### def get_most_likely(): starting_probs={'h' :.4, 's':.6} transition_probs={'hh':.7, 'hs':.3, 'sh':.5, 'ss':.5, } emission_probs = {'hr':.8, 'hg':.1,'hb':.1, 'sr':.2, 'sg':.3, 'sb':.5} mood={1:'h', 0:'s'} # for generating all 8 possible choices using BitMasking observed_clothes = 'gbr' def calc_prob(hidden_states:str)->int: res = starting_probs[hidden_states[:1]] # Prob(m1) res *= transition_probs[hidden_states[:2]] # Prob(m2|m2) res *= transition_probs[hidden_states[1:3]] # Prob(m3|m2) res *= emission_probs[hidden_states[0]+observed_clothes[0]] # Prob(c1|m1) res *= emission_probs[hidden_states[1]+observed_clothes[1]] # Prob(c2|m2) res *= emission_probs[hidden_states[2]+observed_clothes[2]] # Prob(c2|m3) return res #Use BitMasking to generate all possible combinations of hidden states 's' and 'h' for i in range(8): hidden_states = [] binary = i for _ in range(3): hidden_states.append(mood[binary&1]) binary //=2 hidden_states = "".join(hidden_states) print(hidden_states, round(calc_prob(hidden_states),5)) ##### Output ###### sss 0.0045 hss 0.0006 shs 0.00054 hhs 0.000168 ssh 0.018 hsh 0.0024 shh 0.00504 hhh 0.001568

[@AakashOnKeys]

@@toyomicho I had the same doubt. Thanks for the code! Would be better if author pins this.

[@mirasan2007]

Dear ritvik, I watch your videos and I like the way you explain. Regarding this HMM, the stationary vector π is [0.625, 0.375] for the states [happy, sad] respectively. You can check the correct stationary vector by multiplying it with the transpose of the Transition probability Matrix, then it should result the same stationary vector as result: import numpy as np B = np.array([[0.7, 0.3], [0.5, 0.5]]) pi_B = np.array([0.625, 0.375]) np.matmul(B.T, pi_B) array([0.625, 0.375])

[@jinbowang8814]

Really nice explanation! easy and understandable.

[@skyt-csgo376]

You're such a great teacher!

[@silverstar6905]

verry nice explanation. looking forward to seeing something about quantile regression

[@mengxiaoh9048]

thanks for the video! I've watched two other videos but this one is the easiest to understand HMM and I also like that you added the real-life application NLP example at the end

[@ritvikmath]

Glad it was helpful!

[@awalehmohamed6958]

Instant subscription, you deserve millions of followers

[@alecvan7143]

Very insightful, thank you!

[@Molaga]

A great video. I am glad I discovered your channel today.

[@ritvikmath]

Welcome aboard!

[@juanjopiconcossio3146]

Great great explanation. Thank you!!

[@Dima-rj7bv]

I really enjoyed this explanation. Very nice, very straightforward, and consistent. It helped me to understand the concept very fast.

[@ritvikmath]

Glad it was helpful!

[@Justin-General]

Thank you, please keep making content Mr. Ritvik.

[@laurelpegnose7911]

Great video to get an intuition for HMMs. Two minor notes: 1. There might be an ambiguity of the state sad (S) and the start symbol (S), which might have been resolved by renaming one or the other 2. About the example configuration of hidden states which maximizes P: I think this should be written as a tuple (s, s, h) rather than a set {s, s, h} since the order is relevant? Keep up the good work! :-)

[@1243576891]

This explanation is concise and clear. Thanks a lot!

[@ritvikmath]

Of course!

[@qiushiyann]

Thank you for this explanation!

[@mia23]

Thank you. That was a very impressive and clear explanation!

[@ritvikmath]

Glad it was helpful!

[@clauzone03]

You are great! Subscribed with notification after only the first 5 minutes listening to you! :-)

[@ritvikmath]

Aw thank you !!

[@srinivasuluyerra7849]

Great video, nicely explained

[@ananya___1625]

As usual awesome explanation...After referring to tons of videos, I understood it clearly only after this video...Thank you for your efforts and time

[@ritvikmath]

You are most welcome

[@user-or7ji5hv8y]

This is really great explanation

[@nathanielfernandes8916]

I have 2 questions: 1. The Markov assumption seems VERY strong. How can we guarantee the current state only depends on the previous state? (e.g., person has an outfit for the day of the week instead of based on yesterday) 2. How do we collect the transition/emission probabilities if the state is hidden?

[@jirasakburanathawornsom1911]

Im continually amazed by how well and easy to understand you can teach, you are indeed an amazing teacher

[@ahokai]

I don't know why I had paid for my course and then came here to learn. Great explanation, thank you!

[@SPeeDKiLL45]

Great Video Bro ! Thanks

[@mihirbhatia9658]

I wish you went through Bayes Nets before coming to HMM. That would make the conditional probabilities so much more easier to understand for HMMs. Great explanation though !! :)

[@jijie133]

Great video!

[@mansikumari4954]

This is great!!!!!

[@deter3]

amazing explanation !!!

[@minapagliaro7607]

Great explanation ❤️

[@wendyqi4727]

I love your videos so much! Could you please make one video about POMDP?

[@Aoi_Hikari]

i had to rewind the videos a few times, but eventually i understood it, thanks

[@NickVinckier]

This was great. Thank you!

[@ritvikmath]

Glad you enjoyed it!

[@kiran10110]

Damn - what a perfect explanation! Thanks so much! 🙌

[@ritvikmath]

Of course!

[@arungorur3305]

Ritvik, great videos.. I have learnt a lot.. thx. A quick Q re: HMM. How does one create transition matrix for hidden states when in fact you don't know the states.. thx!

[@slanglabadang]

I feel like this is a great model to use to understand how time exists inside our minds

[@Sasha-ub7pz]

Thanks, amazing explanation. I was looking for such video but unfortunately, those authors have bad audio.

[@PF-vn4qz]

Thank you!

[@mousatat7392]

amazing keep up very cool explenation

[@ritvikmath]

Thanks!

[@otixavi8882]

Great video, however I was wondering if the hidden state transitioning probabilities are unknown, is there a way to compute/calculate them based on the observations?

[@Aquaeflavie81]

Great !!

[@b7Z8Sjd]

Thank you for this video

[@kristiapamungkas697]

You are a great teacher!

[@ritvikmath]

Thank you! 😃

[@GarageGotting]

Fantastic explanation. Thanks a lot

[@ritvikmath]

Most welcome!

[@chia-chiyu7288]

Very helpful!! Thanks!

[@ritvikmath]

Glad it was helpful!

[@kalpanasharma5672]

AMAZING.

[@ResilientFighter]

Ritvik, it might be helpful if you add some practice problems in the description

[@ingoverhulst]

Great work! I really enjoy your content.

[@shahabansari5201]

Very good explanation of HMM!

[@ritvikmath]

Glad it was helpful!

[@seansanyal1895]

hey Ritvik, nice quarantine haircut! thanks for the video, great explanation as always. stay safe

[@ritvikmath]

thank you! please stay safe also

[@gopinsk]

I agree Teaching is an art. You have mastered it. Application to real world scenarios are really helpful. Really feel so confident after watching your videos. Question, How did we get the probabilities to start with? are those arbitrary or followed any scientific method to arrive at those numbers?

[@OskarBienko]

I'm curious too. Did you figure it out?

[@user-or7ji5hv8y]

Cool. Have you done a video on how to get those probabilities from observed data? Is it using MCMC?

[@caspahlidiema4027]

The best ever explanation on HMM

[@ritvikmath]

thanks!

[@souravdey1227]

Really crisp explanation. I just have a query. When you say that the mood on a given day "only" depends on the mood the previous day, this statement seems to come with a caveat. Because if it "only" depended on the previous day's mood, then the Markov chain will be trivial. I think what you mean is that the dependence is a conditional probability on the previous day's mood: meaning, given today's mood, there is a "this percent" chance that tomorrow's mood will be this and a "that percent" chance that tomorrow's mood will be that. "this percent" and "that percent" summing up to 1, obviously. The word "only" somehow conveyed a probability of one. I hope I am able to clearly explain.

[@zacharyzheng3610]

Brilliant explanation

[@ritvikmath]

Thanks!

[@jaivratsingh9966]

Nice!

[@kanhabansal524]

best explanation over internet

[@ritvikmath]

Thanks!

[@StreetArtist360]

Thanks.

[@user-or7ji5hv8y]

Great video

[@ritvikmath]

thanks !

[@hex9219]

awesome

[@5602KK]

Incredible. All of the other videos I have watched have me feeling quite over whelmed.

[@ritvikmath]

glad to help!

[@mango-strawberry]

brilliant explanation

[@ritvikmath]

Glad you think so!

[@dhirgajbhiye06]

Cool bro!

[@shaoxiongsun4682]

Thanks a lot for sharing. It is very clearly explained. Just wondering why the objective we want to optimize is not the conditional probability P(M=m | C = c).

[@user-ri7uz9il1v]

Tanx a LOT

[@yasminemohamed5157]

You‘re awesome

[@montheral5073]

thank you..

[@gnkk6002]

Wonderful explanation 👌

[@ritvikmath]

Thank you 🙂

[@ls09405]

Great Video. But how did you calculate {SSH} is maximum?

[@yuliiashaparenko6623]

bravo!

[@MegaJohnwesly]

oh man. Thanks alot :). I tried to understand here and there by reading..But I didn't get it. But this video is gold

[@ritvikmath]

Glad it helped!

[@barhum5765]

God bless your soul man

[@SuperMtheory]

Great video. Perhaps a follow up will be the actual calculation of {S, S, H}

[@ritvikmath]

thanks for the suggestion!

[@shubhamjha5738]

Nice one

[@ritvikmath]

Thanks 🔥

[@anand_dudi]

thanks

[@Infaviored]

If there is a concept I did not understand from my lectures, an i see there is a video by this channel, i know I will understand it afterwards.

[@ritvikmath]

thanks!

[@Infaviored]

@@ritvikmath no, thank you! Ever thought of teaching at an university?

[@nicolas12189]

Hey in future videos could you provide an unobstructed view of the board, either at the beginning or end of the video, just for a few seconds? Sometimes it’s helpful to screenshot your notes

[@PeteThomason]

Thank you, that was a very clear introduction. They key thing I don't get is where the transition and emission probabilities come from. In a real-world problem, how do you get at those?

[@jordanblatter1595]

In the case of the NLP example with part of speech tagging, the model would need data consisting of sentences that are assigned tags by humans. The problem is that there isn't much of that data lying around.

[@claytonwohl7092]

At 2:13, the lecturer says, "it's not random" whether the professor wears a red/green/blue shirt. Not true. It is random. It's random but dependent on the happy/sad state of the professor. Sorry to nitpick. I definitely enjoyed this video :)

[@ritvikmath]

Fair point !! Thanks :)

[@froh_do4431]

Is it possible to describe in a few words, how we can calculate/compute the transition- and emission probabilities?

[@anna-mm4nk]

appreciate that the professor was a 'she' took me by surprise and made me smile :) also great explanation, made me remember that learning is actually fun when you understand what the fuck is going on