Відео

Explanation was to the point 🤩👍

21:45 i think it should be f2=3f1 as f2 is 150 and f1 is 50 that's why

worst teacher i have ever seen yet

padaana chhod do sir apke bas ka nahi hai

Wow!!! Enjoyed the lecture thoroughly. Thanks sir!

Well explained, thanks!

I don't wanna disrespect you but i don't understand your way of teaching i saw 18 lacture till now but not getting one of them , in my pov you are worst teacher teacher i have met

Wonderful lecture. Thank you so much sir.

literally made no sense

Great 👍🏻

oh this is so clearly explain. thank you sir

easiest explanation to understand i have ever seen, shukria!

thank u sir for giving some base to visulize morpholgy concept

Thank you so much sir

star wars*

Ek number ka faltu lecture, kuch smj ni pada.

🙏

Info Extraction - Intro

Entity Linking Part 1

Entity Linking Part 2

Doubt here is that , in question it'd given for any word given its a noun or verb values But he takes for a given noun and value as not matching which is simply not true. Can anyone please answer my doubt

The official english translation gives subtitles in tamil?? only auto-translated is available and it doesn't give accurate translation.

Sir assignments tell me sir

Gerry was wrong Now we are concerned about future of AI

What the hell, I tried to learn this thing multiple sources but couldn't understand but the way you explained, I will never forget this

Very Nice

Abe thodi to tez bol bc

This is one of the best n gram videos I've seen. Straight up no cap

Amazing leture Sir Thank You So Much!!

I made her Duck. Interpret it Ppl

I have my exam tomorrow and I could not find anyone who could explain me these topics in such a thorough manner. Thank you sir!

Thank you so much for this video

i will forever be grateful to you.

Pls dont take this course.... You are better off with any other course on NLP .

This course is so bad.... The lecturer is not even trying to explain the basics. He is just continuing on his pace. The material is good but the audio and this teacher's teaching skills are zero.

Worst teacher ever seen in my life. He don't even know English properly. His vocabulary is worse. These kind of professors should be fired out immediately of IITs. They are polluting the teaching process....

Those buffaloes blew my mind😂😂 Enjoyed the lecture to the fullest.

was these courses created in 2010 ? why arent these updated ????

sir video is not working plzzz upload again

Thankyou you simplified it

merci

One of the Best Explanation on the Topic. Thank you prof. Pawan Goyal

In electrical engineering, statistical computing and bioinformatics, the Baum-Welch algorithm is a special case of the EM algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.

A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm).

A Hidden Markov Model (HMM) is a statistical model which is also used in machine learning. It can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process - call it - with unobservable ("hidden") states. As part of the definition, HMM requires that there be an observable process whose outcomes are "influenced" by the outcomes of in a known way.

In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts.

Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.

Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.

Stochastic (Probabilistic) tagging: A stochastic approach includes frequency, probability or statistics. The simplest stochastic approach finds out the most frequently used tag for a specific word in the annotated training data and uses this information to tag that word in the unannotated text.