Your online tutorials are really great. You demonstrate intimate understanding of the subject and deliver it so so well. It's just left for me to take it in. Congratulations on this series. Immense help to students.
Thanks Luis! This is the most concise introduction to those terminologies one could find on the web! Great job. You saved me tons of time! Thanks a lot!
@@SerranoAcademy Hey, could you please explain the difference between in-sample risk and training error? And how we can measure the in sample risk thus, we can compute the optimism of the dataset?
Your videos are without any doubt the most easy to follow and easy to understand out there! Thanks for explaning things in a simple way so it finally makes sense!
One of the best explanations on ML. Amazing clarity of thought. You have a super visual mind and thank you Luis for sharing what you see with us. Ever grateful 🙏
Incredible video! I always find remembering evaluation metrics difficult but this is a really great way to get my head around it in a memorable way! Will definitely be watching all the videos you put out - thanks in advance.
Thank you for pointing me here. Done watching the entire video and despite that English is not my primary language, I am hooked and fascinated how you make the explanation exciting! The visuals and your story telling is absolutely superb as well! Your channel is a God send!
Awesome! What a way to simplify the complex concepts! You validated once again that you are awesome and the hyperparameter used is your pedagogic style!
Increíble Luís, no hay nada similar ni en udemy ni en youtube. Felicidades por conseguir se tan eficaz a la hora de transmitir tus conocimientos y mil gracias por brindarnos este contenido. Te deseo muchos éxitos!
Thank you Luis! You and the material you use are really good for teaching. They increase my interest! I alteady bought your book "Grokking Machine Learning" in digital version. Can't wait to read it and work with it!
This just answered so many questions I had about ML accuracy scoring and HOW the background code in R-Studio (many built-in functions) are actually calculating accuracy percentages. Thank you for this video
I enjoyed all the videos you posted on ML. Great videos. They make me better understand a lot of concepts and terms that I head/read in my efforts to learn how AI works. What I would realy like to see and understand now, is how these concepts are translated into code (tenser flow or some other framework). Thank you for your videos.
Thanks Luis! This very well summarize the model selection in a very concise manner. Can you please do a video on various metrics to assess a machine learning models, like lift chart, ROC curve, confusion matrix etc. all combined and their use in different cases?
Your videos are awesome. That's my 3rd watched video today, made by you. This is really awesome since it clears the basics in an interesting and simple way. I wish I had watched it earlier. That'd have helped me in my previous vivas. :P
I have really appreciated this video. It helped me understanding I was facing overfitting in my project. Would you mind doing another video about regression problems? Thank you so much in advance.
Hi Luis , Many thanks for these interactive videos & very nice explanation of the complex topics on data science ...You are very clear about the fundas of the subject ...I request you to kindly help us with Random Forest techniques & SVM . Thanks in advance
Hello Luis, First of all your videos are great. Thanks a lot. In the video, the precision for credit card fraud detection is 0/0 right? how come it is 100%? Am I missing something here?
This is amazing!!! Thank you sir...I am trying to learn ML since a long time...after so many complicated tutorials I lost interest in ML and went in web development . Now I found this tutorial and could grasp all those complicated terms with ease. I went to your Udacity ML nano degree course link but its expensive :(
Hi Parigha! Thank you for your kind message. Check out this free deep course that I teach with other people Udacity: www.udacity.com/course/deep-learning-pytorch--ud188
Thanks, it is better if you can help to teach us on codes as well for some real Tensorflow and Keras example for image recognition. I like your style. Very clear as I had some basics but yours clarify my basics to better level.
Underfitting -> Over simplifying things -> Trying to kill dinosaur with trap -> Error due to High bias -> bad on the training set -> bad on testing set Overfitting -> over Complicating things -> Trying to kill bee with bazooka -> Error due to High Variance -> Great on the testing set -> bad on testing set I did struggle with this for a long time
sorry if this was asked or explained in video, what is cross validation, how did it take the quantity of the training set and the qualities of the testing data (seem so from the graphical depiction? And if it did take data from the testing set, isn't that still a conflict to the rule of not using testing data? Thanks, great videos, may take some classes online.
cross validation is a data which will be kept separately from the beginning in order to measure and tune the model. It is not part of either the training data or the testing data.
Your online tutorials are really great. You demonstrate intimate understanding of the subject and deliver it so so well. It's just left for me to take it in. Congratulations on this series. Immense help to students.
Thanks Luis! This is the most concise introduction to those terminologies one could find on the web! Great job. You saved me tons of time! Thanks a lot!
Thank you, Weiran, glad you liked it!
@@SerranoAcademy Hey, could you please explain the difference between in-sample risk and training error? And how we can measure the in sample risk thus, we can compute the optimism of the dataset?
Your videos are without any doubt the most easy to follow and easy to understand out there! Thanks for explaning things in a simple way so it finally makes sense!
One of the best explanations on ML. Amazing clarity of thought. You have a super visual mind and thank you Luis for sharing what you see with us. Ever grateful 🙏
Incredible video! I always find remembering evaluation metrics difficult but this is a really great way to get my head around it in a memorable way! Will definitely be watching all the videos you put out - thanks in advance.
Thanks Luis. This is the simplest and best ever tutorial in ML I have come across.
You are the best. You explain better than my Machine Learning Lecturer. The use of images, colors and explanation are 10/10.
Thank you for pointing me here. Done watching the entire video and despite that English is not my primary language, I am hooked and fascinated how you make the explanation exciting!
The visuals and your story telling is absolutely superb as well! Your channel is a God send!
Awesome! What a way to simplify the complex concepts! You validated once again that you are awesome and the hyperparameter used is your pedagogic style!
Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!
concise, vivid. one of the greatest tutorials I have come across.
I would thank you first. The way that you are simplifying and giving examples is really great. Well done :)
Your Videos are one of the best on the web, please keep it coming.. Thanks
Awesome Luis, it is the simplest and easiest tutorial for ML I have ever see from TH-cam, thanks again. Keep it up.
Love the way you teach and make complex things so simple. Thanks a lot Luis. Hoping to see more videos such.
Thanks Luis. Exceptional delivery. You really have an innate understanding of these concepts and algorithms. Thanks a lot
Video should get way more likes and views, very detailed but in a simplfied way. Thanks a lot for the great info!
Haven't watched the video, but I am pretty sure it would be amazing. You are doing amazing service to so many people. Thanks.
Increíble Luís, no hay nada similar ni en udemy ni en youtube. Felicidades por conseguir se tan eficaz a la hora de transmitir tus conocimientos y mil gracias por brindarnos este contenido. Te deseo muchos éxitos!
Muchas gracias Juan! Que lindo mensaje, me alegra que te guste el contenido. Abrazo!
I like the discussion of recall/precision. I was always confused by how to get an intuitive explanation of recall and precsion, now it's clear.
This is so easy to understand. Just the best tutorial ive seen for this topic!
Hey Luis, I saw your ML videos, and they are awsome. I understood the core and core of it. wow...
Thanks maaaan...
Thank you Luis! You and the material you use are really good for teaching. They increase my interest! I alteady bought your book "Grokking Machine Learning" in digital version. Can't wait to read it and work with it!
Thank you so much Georgina! I hope you enjoy it! :)
Best explanations I have ever seen regarding ML. Thanks a lot for Your effort
Thanks Luis for making, model evaluation attributes simpler
This just answered so many questions I had about ML accuracy scoring and HOW the background code in R-Studio (many built-in functions) are actually calculating accuracy percentages. Thank you for this video
Thanks for the great video. I really enjoyed all the videos you posted on ML. Hope more to come.
Thank you Chaoli! Just added a CNN video, check it out!
Thank you Luis.Machine learning seems more friendly due to your vivid explanation!
Brilliant explanation. Thanks for taking the time to make the video.!!
Nobody else explained things as clearly as you did.
This is extremely good, nicely made and presented. Thanks a lot.
I enjoyed all the videos you posted on ML. Great videos. They make me better understand a lot of concepts and terms that I head/read in my efforts to learn how AI works. What I would realy like to see and understand now, is how these concepts are translated into code (tenser flow or some other framework). Thank you for your videos.
Thanks Luis! This very well summarize the model selection in a very concise manner. Can you please do a video on various metrics to assess a machine learning models, like lift chart, ROC curve, confusion matrix etc. all combined and their use in different cases?
Wow..I don't think there is a better explanation than this
Awesome bite-sized videos Luis, really intuitive to understand! Great job!
Thanks Luis for this detailed explanation! Request you to upload videos on some other expansive metrics too such as AUC, ROC etc.
Your videos are awesome. That's my 3rd watched video today, made by you. This is really awesome since it clears the basics in an interesting and simple way. I wish I had watched it earlier. That'd have helped me in my previous vivas. :P
Gracias Luis ; excelente trabajo . Salu2 desde Argentina .
Luis ! Great Course ! best one out there . I will recommend the IBM QMS Quality Engineers to view this video! thanks !
Great video, great explanation!!
Thanks Luis!!
I have really appreciated this video. It helped me understanding I was facing overfitting in my project. Would you mind doing another video about regression problems? Thank you so much in advance.
Hi Luis , Many thanks for these interactive videos & very nice explanation of the complex topics on data science ...You are very clear about the fundas of the subject ...I request you to kindly help us with Random Forest techniques & SVM . Thanks in advance
Thank you so much! Another amazing tutorial!!!
Luis great another masterpiece "Machine Learning: Testing and Error Metrics" thanks
Very helpful. Great Explanation!!!!! Thanks a lot :)
Would love to have more videos giving clarity of statistics concepts used for machine learning .
Thank you very much for the easy and amazing explanation
Love the three golden rules. Rule #4: don't forget the first three
Great video! Going to check out the rest of them.
Hello Luis, First of all your videos are great. Thanks a lot.
In the video, the precision for credit card fraud detection is 0/0 right? how come it is 100%? Am I missing something here?
Thank you Luis. Well explained
Serrano. You are the G.O.A.T
Great video. Great explanation.
This is amazing!!! Thank you sir...I am trying to learn ML since a long time...after so many complicated tutorials I lost interest in ML and went in web development . Now I found this tutorial and could grasp all those complicated terms with ease. I went to your Udacity ML nano degree course link but its expensive :(
Hi Parigha! Thank you for your kind message. Check out this free deep course that I teach with other people Udacity: www.udacity.com/course/deep-learning-pytorch--ud188
I am very much thankful to you for so nice videos.
Thanks a lot, you made very easy to understand metrics
Awesome examples & explanation. TY
Thank you so much Luis, grt session 👍🏻
Thank you Luis! Really appreciate your works.
great series made easy to comprehend thank you
Thanks Luis, A very well presented explanation.
Great lecture Luis ! Thanks !!!
Thanks man !!!! This is so very helpful !!!!
Awesome intro on ML model evaluation
This is brilliant! Thank you!!
so clean explanation
Thanks Luis! I really like your videos. could you do videos on statistics also
Awesome explanation !
Wow! Brilliant session
Why do we get 100% precision score in Credit Card Fraudulent problem? Is that correct?
Luis you are the best man!!!!
Awesome ML training! Thank you, Luis!
Thanks, it is better if you can help to teach us on codes as well for some real Tensorflow and Keras example for image recognition. I like your style. Very clear as I had some basics but yours clarify my basics to better level.
Wooh great and detailed explanation thank you, it is easy to follow, I would love to know your thoughts on when to use AUC metrics for model testing
One of the best explications, please keep it up. Subscribed :)
Muchas Gracias, Luis!
Very easily explained
Thank you very much. Very informative. Going to try udacity courses now!
Awesome explanation. Congrats.
Very good and helpful. Easy to understand in the beginning
, at the end a little bit too fast about too much new things
.
Very helpful video, thank you very much!
TBH this is literally an amazing explanation of the problem i get till to 3 years
For k-fold. What model do you use? Since you trained on 4 different training sets, you have four models.
Wow... Simply Super ... F1 is 1
Underfitting -> Over simplifying things -> Trying to kill dinosaur with trap -> Error due to High bias -> bad on the training set -> bad on testing set
Overfitting -> over Complicating things -> Trying to kill bee with bazooka -> Error due to High Variance -> Great on the testing set -> bad on testing set
I did struggle with this for a long time
I Dont think there is a more PRECISE explanation for evaluation metrics than this, in Internet
Thanks for sharing this wonderful tutorial.
Great stuff Luis.
sorry if this was asked or explained in video, what is cross validation, how did it take the quantity of the training set and the qualities of the testing data (seem so from the graphical depiction? And if it did take data from the testing set, isn't that still a conflict to the rule of not using testing data? Thanks, great videos, may take some classes online.
cross validation is a data which will be kept separately from the beginning in order to measure and tune the model. It is not part of either the training data or the testing data.
Great explanation keep it up
Thanks Lusis. Really helpful!
Nice summary video
Great explanantion❤❤❤
Thanks, great explanation!
So awesome, thank you very much!
thanks Luis !
For the credit card example in 21:35, won't the precision be 0/0
since nothing is classified as bad?
Amazing video series!!
you should be a teacher, society is in real need for ppl like you )
You are awesome Luis.
Muy buen trabajo, mas videos en español por favor. GRacias :)