Відео

Incredible and amazing explanation! Thanks so much for such great content!

12:54 Wow, I didn't know about moments - except for "moment-generating functions".

11:29 What about the median? Which is better - mean or median? Could you do a video on this? Thanks!

I first learned about kurtosis in my high school research class - it was a stat we looked at for our project, but I really didn't know what it was aside from being a weird word... Thank you for the explanation. This is a great, well-needed video!

9:16 But you said that the standard deviations of both distributions is the same, correct? How is that possible that the number of outliers in both distribution differs, YET they still both have the same standard deviation? Thanks!

Thank you for a good explanation of a seemingly weird looking formula. It would be hard to forget this formula now that I got it from here.

Hey @ritvikmath, I tried using ADF and KPSS on 3 sample datasets, similar to the ones in your video. One dataset violates the constant mean, the other thd constant variance, and lastly one with seasonality. However, it seems that both the ADF and KPSS are returning the datasets to be stationary for both non-constsnt deviation and the seasonality dataset. It accurately tests non-constant mean datasets. Any thoughts as to why that would happen?

great video. thanks !

Is the change of coordinates from cartesian to polar related to the so called 'kernel trick'?

So help me understand this, anyone. Lets suppose I want the perceptron to classify spam and not spam mail. Then, by a point (on the plot) do we mean a single email? Also, does the update of parameters happen after each mail is classified or after the entire training set of emails is classified? From the video, I get the impression that update happens only after all emails are classified. How do we update params if there are multiple wrongly classified points?

Hey Ritvik, thanks for this video! I was wondering how will the parameters be updated if we have multiple points that are wrongly classified?

thanks a lot

How do you do it for Panel data? Like i have price for 1000 parts in an dataframe. And for each part, I have 20 years for price information along with other variables. I have to predict the prices for each component for next 5 years. It is impossible to look at trends for each part. How does one work with Panel format data for forecasting? Can I use this model?

nicely articulated. ty!

I guess using probability algorithm and key fact you can sometimes come up with a pretty accurate guesstimate... Cool !

8:47 but what about when you have more than 1 moitake and some should be in the upper and some in the lower?

great explanation!!!

The fact that the nazis were OCD was their downfall

Your videos stand out because you truly put yourself in the shoes of the student, understanding their concerns. This approach makes your content more than just a plain talk about a topic-it's actual teaching.

This is really great!

Cool video, I think bending might go beyond Mendelian inheritance though. I remember an episode that had identical twins, one was an earth bender, the other one wasn't.

As always Ritvik never disappoints when it comes to breaking down a concept without relying on mathematical equations, and still giving the best overview of a concept in the most generalized way possible. Thank you!

you saved my life, I will watch all your videos before my exam on machine learning

Epic explanation

Is there a markov chain model for this area calculation that converges quicker. Something that actively samples the inside of the circle preferentially.

The shear quantity of -1's in that formula makes me sad. Especially when the numbers you need to input are all +1 of the quantity you'd expect. Like this could easily be (alpha + k - 1) ! / product(alpha_i!) * p_i ^ alpha_i! where alpha is the total population, alpha_i is each population and k is the number of groups.

what if the similarities are negative??

My man- u r my savior and a legend.❤

Many thanks 🎉❤

Takeaway for myself: ARIMA is the model applied for the time series data, where there is time dependence. It has a more step if transforming from crrelation of x and time to the correlation of x and x(t-1) (it's precedence). And from the formular of linear regressiin, the diff of x and x(t-1) is const (slope). So it doesn't depend on time. The 3 critiera for a series that can be applied ARMA (stationary): constant mean, constant variance, no seasonality.

not bad

I can't thank you enough. Although I understood the concept in my class, still I wasn't able to visualize how this is working until I saw your video. You are helping thousands of us students. Thank you sooo much.

Thank you, this video really helps to look at probabilities in a way that makes it more understandable.

Awesome video

Thank you so much 😊

Will there be a continuation of this?

very helpful. Thanks for explaining this with simplicity.

i make more percentage gains using simple technical analysis...this mumbo jumbo is making things only complicated

I started this playlist out of curiosity to get an introduction to time series analysis since it's a term I have been hearing for a while now. Now I feel like I will finish this playlist in no time. You explain things really well, and you have just showed me awesomely how to do the code of time series analysis which I understood very well. Great job, man!! We very much appreciate it!

Thorough and great explanation of this subject matter. Thanks so much

Didn't understand a think. plz someone tell me anything I have missed out before this video , although i have watched the previous 4 videos

how is it the coefs b is 1 and 1? I'm a little confused🥹

I think it's worth pointing out that this assumption that you only ever depend on the previous state is a "weak" assumption. You can get around it by expanding your state space to include the previous value. The bigger limitation on markov chains is that the probability of transition is independent of state. That is to say it can be constructed as a linear expression x_t = A*x_t-1, which is the heart of the simplification here.

Wath an amazing tutorial you managed to give a detailed yet simple explanation, gained a subscriber

Thank you so much. This is amazing!!

11:35 useful to think about the same "ratio", thank you boss

Great explanations: very easy to understand!

great video

just love this simplicity!

why this accent though?😑