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 | Thanks for the great tutorial. Since in real life we have only the data, how can we find the best non-linear equation that fits to our data? Комментарий от : @senolkurt7864 |
 | Hello...Can anyone provide any resources for how to perform "nonparametric quantile regression" in Python? Комментарий от : @JJGhostHunters |
 | Thank you so much for posting this. Ques: If there is a high covariance between the parameters why is this bad? thanx! Комментарий от : @tonycardinal413 |
 | thanks for the help this was a long long journey it took me like 3-5 hours to translate my german function name in its actual english equivalent since the direct translation is miles miles away :D and finaly i got a model that should help me solve my enzyme activity measures Комментарий от : @OnlyfansCustomerSupport |
 | Hello...Are you aware of a Python library that can perform "nonparametric quantile regression" as described in the following paper? cs.stanford.edu/~quocle/jmlrq.pdf Комментарий от : @jamescarmichael7683 |
 | very good tutorial, however my doubt is what if you have a matrix of parameters? how do you make the objective function. eg: Ax = b. I need to find A. how do you frame the problem? Комментарий от : @erenyeager4452 |
 | How to actually find that correlation function is there a way? Комментарий от : @besttom8823 |
 | Very helpful and valuable too. Thank you. Комментарий от : @asifraj321 |
 | How did you determine the values of the constants Комментарий от : @benedictodhiambo4107 |
 | 6:26 whats the formula really that you have used in that bpm fn ? i didnt understand that part. usually what I know is that therea re various non-linear methods that one can choose for non-linear reg types such as polynomial fits , lograthmic fits and so on . correct me if I am wrong pls Комментарий от : @NawtieBoy96 |
 | is there iany way to solve equations in a python list [eqn1,eqn2,eqn3.........] with adition constainsts in form of equations e.g. a+bX=90, a*X^2+b*X=100, like in regression we have a model, if we substitute data points in it we will get many equations of model parameters along with it we add additional constaraints please help me Комментарий от : @KarthikeyanMmmm |
 | thank you for sharing! Комментарий от : @datastako156 |
 | cool video! Комментарий от : @bastianian2939 |
 | This is really good content! Thank you very much for sharing your knowledge. Im still blown away by the quality and how you explained the material Комментарий от : @gomezest |
| hello sir , is there any similar package in python as curve fit , in which we can able to impose some constraints ( just like during regression at a particular point slope should be a specific value for continuity ) during regression, OR is there any posiblity in curve_fit to impose some constraints Комментарий от : @KarthikeyanMmmm |
 | Does this curve fitting function sometimes return wrong answer? Комментарий от : @oxydol3456 |
 | Thank you! Комментарий от : @alvaro_gavilan_rojas |
 | how can I know the chi square? Комментарий от : @zartadavid2900 |
 | Thank you very much for your video! By the way, is anybody know how to solve the same issue, but when you have more than one variable. For example Y = a * x1^b + c*x2^d. We have dataset with x1,x2, Y. And how we can optimize a,b,c,d? Комментарий от : @ruslanruslan338 |
 | This was exactly what I needed, thank you! Комментарий от : @SuperCamProductions |
 | Oh really useful!! Thanks a lot Комментарий от : @eaglepaul87 |
 | Hi, I am trying to fit my data with SIR model. Is there any suggest for fitting the data with ODE equations with two coefficients (reaction rate constant)? Thanks in advance. Комментарий от : @user-kai516 |
 | Hello please. I have done everything in your video. It works every well. But how can I predict the heart rate for the time out of the range? I mean what is now the prediction function I have to call??? Please help me for this task. Комментарий от : @basichack6974 |
 | Thanks for the demo, very useful for a beginner like me! managed to do something similar following the steps. I wanted to ask something. My formula in stead of having a single variable ( time), y have 3 input variables, i have to compute p1[i], p2[i], p3[i] to be able to calculate the y value. I manage to adapt my code to that and plot it, but i got stucked in the curve_fit. In the curve_fit uses only 1 xdata if i am correct. What variation can i use? I hope you know, thanks ! Комментарий от : @agustingambaretto2260 |
 | Sorry, but I can't access the data. Can anybody help? Комментарий от : @cccloud3256 |
 | Is this non parametric regression? Комментарий от : @HealthyFoodBae_ |
 | Thanks for the very clear video. However, it doesn't seem to work for my specific function: after defining variables x and y using x.values as in the video at 10:56 from a datafile, I try doing curve fit and it tells me that "only size-1 arrays can be converted to python scalars", which I am unable to fix. All the other steps are the same as in the video This is the function I am trying to fit: y = c_1 * x^{c_2} Комментарий от : @giocanox97 |
 | Hello, I am newbie in ML algorithms and wanted to Thank you for the explanation in video. I had a question that, will it be easy to use the following code instead : github.com/darsh-008/Machine-Learning-Algorithms/blob/main/Regression/Non-Linear%20Regression.ipynb Thanks in advance ! Комментарий от : @darshbhatt3795 |
 | thank u X 10^10 . U r a great professional and teacher ! Комментарий от : @DiegoFriasSuarez |
 | Excellent video Комментарий от : @Thahid |
 | So for non linear regression we have to assume/guess which curve equation will fit the data and unlike linear regression in PYTHON which predicts itself?? Комментарий от : @KR-uy6or |
 | Hi! Great video! A couple of questions: 1) What if you had multiple people's heart rate? Is it possible to create a curve that would fit all of them? 2) What if we do not have the BPM equation? Thank you! Комментарий от : @stevethach3340 |
 | thank you Комментарий от : @kareemsakr41 |
 | Hi Mr. ,What criteria does it take to choose the values for the initial conjecture of the parameters?
I will be grateful for the answer Комментарий от : @guerreirodaluzgmailcom |
 | Nice Комментарий от : @certifiedcriticmedia1532 |
 | Hi, that was awesome and very helpful! Just one question, when you entered c, cov where was the cov used? Only c was printed out, right? If I enter just c in place of c,cov would there be any difference? If you can attach a link for the same, that would be great. Thanks a bunch Комментарий от : @shreeniketjoshi |
 | Hi, this was very helpful. However, if you had a dataset that comprises of multiple exponentially decaying curves, how would this work out? I can relatively do this for a single exponential decay curve but unsure on how to deal with multiple exp. decay curves. Комментарий от : @obinnaizima9387 |
 | hello sir, I did not understand , how you get initial guess.If you have time can you please explain me.I am master student in France. Комментарий от : @sobersabin |
 | Super! So easily explained, thank you. Комментарий от : @arushijain4457 |
 | Hey, great content and pretty clear explanations! I have a question though. Is it easy to add some constraints to your variables (e.g. 0<g[0]+g[1]<1)? Thanks in advance!! Комментарий от : @margalanis |
 | Just the fact that you provide this invaluable content for free, when others are selling low quality courses for a lot of money, proves how a great human being you are. You are a true Master. Комментарий от : @alibastami8626 |
 | I'm a newbie to python. can you please explain me why use choose the particular equation for your curve fitting? Комментарий от : @skkumarish |
 | Does principle is gauss-newton or grading descent ? And I want to learn step by step , no use modules THX😭 Комментарий от : @user-hk2uj7jl4g |
 | Is this Levenberg-Marquardt algorithm?? Комментарий от : @hibadu251 |
 | thanks a lot ! :) Комментарий от : @lukas-santopuglisi668 |
 | How to estimate parameters of a differential equation by fitting to experimental data? Комментарий от : @vchamilka |
 | How to find t-statistics, F-statistics and p-values for this regression? Using Python Комментарий от : @MaddingAlex |
 | if i know the predicted curve z_pred = x*y after some matrix transformations and i know the actual value z_actual then if i find MSE(z_actual - z_pred) then how can i reduce this error?
Anyone can answer Комментарий от : @nutakkipradeep2708 |
 | Thank you very much. Your channel is very helpful for me :) Комментарий от : @badinhbk |
 | Perfect! Thank you so much for the great video. Комментарий от : @arcesarino |
