Hidden Markov Model : Data Science Concepts

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

[_yashbhanushali]

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 !

[paulbrown]

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!

[mohammadmoslemuddin]

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.

[chadwinters]

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

[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.

[zishiwu]

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

[pinkymotta]

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

[stevengreidinger]

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

[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!

[mirasan]

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])

[songweimai]

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

[ahokai]

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

[nathanielfernandes]

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?

[jirasakburanathawornsom]

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

[Dima-rjbv]

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

[rssamarth]

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!!

[clauzone]

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

[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.

[claytonwohl]

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 :)

[seansanyal]

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