After looking for a good introduction on HMM (including letures from prestigious universities worldwide), this is BY FAR the best material out there! Kudos, Derek! And thank you SO much!

thank you so much. this video clarified so many questions i had. cannot thank you enough.

great presentation. But it does sound like there's a refuelling fighter jet in the background.

is there a machine learning model that can erase white noise? it will be great to make this video more joyful :D

I have a question ... How much is the percent of winrate of this model ( for example if we have 100 days how many days can we predict correctly ? THX. and thanks for this speech it's very valuable for me.

Derek you're a God amongst men

Please reduce noices. Its annoying and tends to turn to another videos.

12:38 is the correlations referring to the irregular/noise component?

You Rock Dude, I bet you play a mean bassoon.

Man I wish the audio quality was nice that I can watch at 2X. Increasing the volume makes the noise unbearable :|

Too much noise in the video. Though I want to watch. I can't continue

Poor quality of audio recording.

Great delivery but the audio is not great. Meanwhile, the breastcancer file is not attached, can you please share.

Excellent work and very helpful.

Great video

There is so much background noise sir

Trending... Trending... Visit and follow if you find it interested. medium.com/@techin3.min/predictive-analytics-understanding-the-value-of-the-data-ea59ec719703

Good explanation and examples

One of the best videos on Hidden Markov Models on KZbin!

Hey Derek, thanks for the video, Can you explain how to calculate the probability of the States(9.20).? please!

thank you derek

Well presented information and simple explanations of complex topics.

great presentation, one suggestion. at 41:00 you have a graph with unlabeled x axis unlabeled y axis and no legend to indicate differentiation of colors. I was puzzled looking at this graph that has only a title.

"Staff Data Scientist - Image and Video Analytics" opportunity at Seagate Technology Location: Colorado, USA seagate.wd1.myworkdayjobs.com/EXT/job/Longmont-United-States/Staff-Data-Scientist---Image-and-Video-Analytics_200082

Great explanation! By any chance have you tried using depmixS4 package ?

Great Video sir.. i love this lecture.. thanks a lot sir.

Thanks a lot keep uploading

Hi Derek ,can i get your whatsapp number for talking some deal with you ,regarding the courses!

8:30 Heisenberg from Breaking Bad lol

what a genetic algorithm could be forecasting/prediction ?

Super clear and high level! Good stuff

Thanks for the number of examples you explain! Ive got two questions: At 1:09:00 you choose arima over holtwinters because de irreguler component had values of 3500 and 4500. Why? And when you use this arima to forecast the next few time steps, it predicts the seasonality nicely. How did the arima model find the seasonality?

Thanks

Hi there, Great video! I was wondering however if the last modeling procedure (GLM) requires differencing strongly trended time series?

This is a very good lecture that gives me a clue about GA. If possible it could be good to add some lecture videos on an application of GA in Water Resources Engineering like Reservoir Operation & Establishing optimal Rule Curve and also on optimization of Lake water resources for multiple objectives like agriculture and hydro-power especially when these development objectives are computing to the same reservoir resources. Please give examples on the coding practices of such problems.

do you have courses that actually get into evaluation of model like (chi-sq, fisher exact test, lift/gain chart and so on)? And how to interpret those model? Greatly appreciated!

you can also do a logistic regression for the last example given

Hi Derek , would you kindly do a video even short one regarding Hierarchical version of such hmm, and if possible how can I model / fit the emissions probabilities which follow skewed- t distribution , and and how can we deal in practice with the time- Independence assumption of such type of models ? Best Regards

Nice Lectures! thanks!

Thank you so much,this is really amazing

Nice session! Thanks Derek, could have been even great if included R programming codes practice as well simultaneously

thank you .input it explained about the method of adabtv lasso

Top one!

Hi there, great video. This may be a stupid question but I'm just wondering what the advantage of using neural networks to predict the subseries rather than just extrapolating forward the results of the FFT are? Surely a neural network with no drivers other than a time series can never be more accurate than the extrapolated recomposed waveform?

Thanks a lot for the clear explanation! I was wondering if for the trained dataset you need to have the difference between de open and close price? Or is the open price enough?

Its not quite audible

really usefull

Very helpful, thank you!

Hey Derek, a comprehensive video on fft for time series. Just wanted to know if you have a github repo for this project? If not could you share the source code, privately?

While I appreciate all the efforts in putting up this presentation, I hopefully wish that you explained the methodology of all these concepts with some practical explanations / algorithms to help us better understanding on the applications.