Thank you sir.I also completed your ml course in your previous videos in last year.Now i'm in interview process for getting job,your videos really helped me.I'm a house wife and learning from home (your videos).Thank you a lot.god bless you.
Dear Gurtej Kaur, I am glad that my content helped you. Wish you all the best for your future and always believe in yourself. Believe that you can do it. Good luck. 🙏😊
Hi Dhaval, your tutorials are very informative and very helpful for self learning. Kudos!!! Here is some of best score with optimal parameters for the exercise. 1 svm --> 0.992045 {'C': 10, 'gamma': 'scale', 'kernel': 'rbf', '... 2 random_forest --> 0.994435 {'criterion': 'gini', 'n_estimators': 100} 3 logistic_regression --> 0.980114 {'C': 1, 'multi_class': 'auto', 'penalty': 'l1... 4 GaussianNB--> 0.995225 {} 5 DecisionTreeClassifier --> 1.000000 {'criterion': 'gini', 'splitter': 'best'}
Thank you very much sir for teaching this wonderful Machine Learning course. Now I am able to enjoy the ML Models better than earlier. I have found SVM model as best model for digits datasets Here are the scores using GridSearchCV model best_score best_params svm 0.947697 {'C': 1, 'kernel': 'linear'} random_forest 0.903757 {'n_estimators': 10} logistic_regression 0.922114 {'C': 1} gaussian_nb 0.806928 {'var_smoothing': 1e-09} multinomial_nb 0.876476 {'alpha': 1000} decision_tree 0.732934 {'max_leaf_nodes': 20, 'min_samples_split': 2}
To be honest, I don't understand much the parameters' definition. But also gave it a try. Thanks a lot for this tutorial, sir!!! svm 0.973850 {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'} random_forest 0.908200 {'n_estimators': 10} logistic_regression 0.920446 {'C': 2, 'solver': 'liblinear'} GaussianNB 0.806928 {} MultinomialNB 0.870350 {} Decision_tree 0.784121 {'criterion': 'gini'}
Tbh this is the toughest tutorial in the playlist everything took me around 2-3 hrs a day but this taken around 2 days as we have to see all the parameters and do the tunning and selecting the best model I got SVM as the best model for the digits dataset Thank you for giving such a good playlist
wow....this is what I was looking for i.e how to compare results of different model with different Parameters....you have consolidated all and shown easy methods.... tks a lot sir....very helpful...
Hey Dhaval, My sincere thanks to you and the efforts you have taken. I do not have much background in AI and ML, your videos make me feel that even a novice can learn and excel in this field. I am consistently watching your videos and exploring things out. A must series for every learner. Best wishes to you.
I have doubt on the diff between cross val score & grid search cv for long time. you just solved it in a single video. Excellent work. Thank you very much.
Very Comprehensive Tutorial. I did feature scaling before training the models Best Model: SVM Parameters: {C=10.0, Gamma = 'scale', Kernel = 'rbf'} Accuracy Score: 0.9749628597957288, approx 97.5%
Thanks a lot, I got the Scores as SVM : {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'} ==> 0.990451 Logistic_Regression {'C': 1, 'max_iter': 5000, 'multi_class': 'auto' ==> 0.972143 SVM is the Best
Inspired by your teaching style sir i am frm india and just joined Intellipaat (IIT Roorkee University) for Data science and Ai , but since mentor is teaching style is not good i have to see your videos to learn even better thanks sir for providing such a valuable and free stuff on youTube i will also join your one of the course of Excel
Exercise solution: github.com/codebasics/py/blob/master/ML/15_gridsearch/Exercise/15_grid_search_cv_exercise.ipynb Step by step guide on how to learn data science for free: ruclips.net/video/Vn_mmOuQkSA/видео.html Machine learning tutorials with exercises: ruclips.net/video/gmvvaobm7eQ/видео.html
thank you, I learned a lot from your video. Only one correct the git code shared missing from sklearn.model_selection import cross_val_score. Thanks again for sharing your knowledge.
thank you so much for the tutorial... youve taken a complete novice like me and taught him quite a great deal about ML this will surely be useful in my professional life.. thank you so so much..! by the way, in the exercise, i got best model as SVM with parameters C=1, and kernel = rbf with score as 0.98
I think this video is the best among all as it tells which one is the most efficient among all models For the exercise part: model best_score best_params svm 0.973 {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'} Random_Forest 0.909870 {'criterion': 'entropy', 'max_features': ''sqrt', 'n_estimators':10} Logistic_Regression 0.928233 {'C': 1, 'penalty': 'l1', 'solver': 'liblinear'} GaussianNB 0.814157 {'var_smoothing': 2e-09} MultinomialNB 0.871464 {'alpha': 2.0} Decision_Tree 0.805263 {'criterion': 'entropy', 'splitter': 'best'}
Dhaval Patel @ codebasics. I am done with your tutorials till now. It's really great. Your videos shows your passion. Heartful thanks to you for providing an Initial step into ML. If you can upload more real life project then that would be awesome. I would like to request one thing, Please start creating videos for OpenCV. If people are learning from your videos then surely it will be worthy. Thanks again !!!
Wow, this video was incredibly informative! It not only answered all the questions I had, but also covered additional topics that I hadn’t even considered. The explanations are very easy to understand. Thank you for the great content and for going the extra mile to ensure thorough understanding. Looking forward to more videos like this! Just a quick question I see that when fitting the models which include the GridSearchCV object and later the RandomizedSearchCV object with the training data you used iris.data and iris.target, instead of X_train and y_train. Should it have been X_train and y_train?
for iris dataset svm and logistic_regression results are same for paramters : svm 0.980000 {'C': 1, 'kernel': 'rbf'} and logistic_regression 0.980000 {'C': 100} , for given exercise for digits dataset the best model is svm score and paramters are : 0.947697 {'C': 1, 'kernel': 'linear'} , second best model is : random_forest 0.941586 {'n_estimators': 192}
I have visited lots of channels and have bought 2-3 courses, but only this channel could make these concepts clear to me. Thankful to you! @codebasics🏆
Check out our premium machine learning course with 2 Industry projects: codebasics.io/courses/machine-learning-for-data-science-beginners-to-advanced
Thank you sir.I also completed your ml course in your previous videos in last year.Now i'm in interview process for getting job,your videos really helped me.I'm a house wife and learning from home (your videos).Thank you a lot.god bless you.
Dear Gurtej Kaur,
I am glad that my content helped you. Wish you all the best for your future and always believe in yourself. Believe that you can do it. Good luck. 🙏😊
hai gurtej ...glad to hear it...did you take any other course with it for certificate ? are you working now?
Inspirational
I learned more in last 5 mins of this video than I have in a week studying on my own
Awesome. I am glad :)
exactly
Thanks for your wonderful tutorials
Best Model = SVM
Best parameter = {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}
Score = 0.973850 accuracy (97.4%)
Can't really express how lucky I am to have found you. By far the best tutorial on machine learning
Hi Dhaval, your tutorials are very informative and very helpful for self learning. Kudos!!!
Here is some of best score with optimal parameters for the exercise.
1 svm --> 0.992045 {'C': 10, 'gamma': 'scale', 'kernel': 'rbf', '...
2 random_forest --> 0.994435 {'criterion': 'gini', 'n_estimators': 100}
3 logistic_regression --> 0.980114 {'C': 1, 'multi_class': 'auto', 'penalty': 'l1...
4 GaussianNB--> 0.995225 {}
5 DecisionTreeClassifier --> 1.000000 {'criterion': 'gini', 'splitter': 'best'}
Wow great score. Good job indeed 👍👏👏👏
Hi Udayashangar, can you share a link to your notebook?
You are a great professor of Deep Machine Learnings! Congrats!
Thank you very much sir for teaching this wonderful Machine Learning course. Now I am able to enjoy the ML Models better than earlier. I have found SVM model as best model for digits datasets
Here are the scores using GridSearchCV
model best_score best_params
svm 0.947697 {'C': 1, 'kernel': 'linear'}
random_forest 0.903757 {'n_estimators': 10}
logistic_regression 0.922114 {'C': 1}
gaussian_nb 0.806928 {'var_smoothing': 1e-09}
multinomial_nb 0.876476 {'alpha': 1000}
decision_tree 0.732934 {'max_leaf_nodes': 20, 'min_samples_split': 2}
You got me sir , i was roaming here and there but you covered almost everything in one video thats the spirit sir and salute sir .
Glad you liked it Basant Girl.
To be honest, I don't understand much the parameters' definition. But also gave it a try. Thanks a lot for this tutorial, sir!!!
svm 0.973850 {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}
random_forest 0.908200 {'n_estimators': 10}
logistic_regression 0.920446 {'C': 2, 'solver': 'liblinear'}
GaussianNB 0.806928 {}
MultinomialNB 0.870350 {}
Decision_tree 0.784121 {'criterion': 'gini'}
Thank You Sir!
This was my best model for the given exercise:
Support Vector Machine 0.986084 SVC(C = 1, gamma = 'auto', kernel = 'poly')
one line for you: "sophisticated code made simple" . Thank you. was really helpful .
Tbh this is the toughest tutorial in the playlist everything took me around 2-3 hrs a day but this taken around 2 days as we have to see all the parameters and do the tunning and selecting the best model I got SVM as the best model for the digits dataset
Thank you for giving such a good playlist
wow....this is what I was looking for i.e how to compare results of different model with different Parameters....you have consolidated all and shown easy methods.... tks a lot sir....very helpful...
😃👍
Hey Dhaval,
My sincere thanks to you and the efforts you have taken. I do not have much background in AI and ML, your videos make me feel that even a novice can learn and excel in this field. I am consistently watching your videos and exploring things out. A must series for every learner. Best wishes to you.
Very nice intro , really helped me when I was following an applied ML book and needed to get better understanding than presented in text.Thanks
I have doubt on the diff between cross val score & grid search cv for long time. you just solved it in a single video. Excellent work. Thank you very much.
John I am really happy that this helped you resolve your doubts 😊
The best tutorial I have ever watched... Thank u sir!!!!!
Glad it was helpful!
Really your videos are equal to learning lot of books
Thanks so much. I've learnt of GridSearch in a very clear way than I have ever come close to.
Wow...Loved the simple explanation....Crisp and Clear....Thanks a lot
Glad you liked it
This is very helpful for increasing accuracy. A good tutorial. Thank you :-)
Glad you liked it, did you feel out Google form ? I would like to call you buddy
@@codebasics Oh sorry I missed it. I will fill it now.
your all videos are very very very very very related to practical worlds. it always gives complete information.
Very Comprehensive Tutorial.
I did feature scaling before training the models
Best Model: SVM
Parameters: {C=10.0, Gamma = 'scale', Kernel = 'rbf'}
Accuracy Score: 0.9749628597957288, approx 97.5%
Thanks a lot, I got the Scores as
SVM : {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'} ==> 0.990451
Logistic_Regression {'C': 1, 'max_iter': 5000, 'multi_class': 'auto' ==> 0.972143
SVM is the Best
very good for beginners, Logical and classified
Inspired by your teaching style sir i am frm india and just joined Intellipaat (IIT Roorkee University) for Data science and Ai , but since mentor is teaching style is not good i have to see your videos to learn even better thanks sir for providing such a valuable and free stuff on youTube i will also join your one of the course of Excel
Good and nice teaching approach sir and keep uploading like this a good content videos sir
Your information is GOLD. Thank you for sharing your knowledge (and codes) to us!
Very simple and easy way to learn... thank you for your time..🙌🇮🇳
Great content for learning Machine Learning , Thank You so much sir .. 🙂
You make amazing content pls do more so that we can learn more during qurantine
It is crisp and clear to follow...
WOW This is the best explanation for GridSearch CV..Thank you very much SIR
👍😊😊
Very clear practical explanation.
Thanks for such a content, this was very interesting and helpful.
Exercise solution: github.com/codebasics/py/blob/master/ML/15_gridsearch/Exercise/15_grid_search_cv_exercise.ipynb
Step by step guide on how to learn data science for free: ruclips.net/video/Vn_mmOuQkSA/видео.html
Machine learning tutorials with exercises:
ruclips.net/video/gmvvaobm7eQ/видео.html
thanks for sharing, i finally understood the gridsearch!
It is really awesome explanation with examples
The way you explain the code is the best I've seen anyone do it online. Great teaching style! Thanks
Excellent Video
Excellent content
Thank you sir. Your videos really helped to understand the concepts well. Thnak you very much.
thank you, I learned a lot from your video. Only one correct the git code shared missing from sklearn.model_selection import cross_val_score. Thanks again for sharing your knowledge.
Awesome! You explained a pretty tricky topic very clearly, and also gave plenty of highly usable insight. This video is a gem, thanks for your work!
great.I respect you for your nice explanation
thank you so much for the tutorial... youve taken a complete novice like me and taught him quite a great deal about ML
this will surely be useful in my professional life..
thank you so so much..!
by the way, in the exercise, i got best model as SVM with parameters C=1, and kernel = rbf with score as 0.98
Great to hear! .98 is a great score. well done.
Thank you sir ! .Results :
model best_score best_params
0 svm 0.980000 {'C': 1, 'kernel': 'rbf'}
This has been an amazing series. Thank you so much!
Thank you so much sir, I have learned a lot from you.
Nice explanation sir. I like it. Concept is clear now.
Amazing videos Sir, Your selection of easy examples during teaching really helps understand the underlying working of these classes. Thank You.
Glad you liked it Avinash 😊👍
Looking forward to more amazing contents about Machine/Deep Learning in the future.
So much content in 16min😌..thank you Sir!!!
Glad it was helpful!
Thank you very much! A hug from Brazil!
Thankyou Sir for making such brilliant videos..........
Loved it..Got nice explanation of the topic..Thank you sir
Best_Model: svm
Score: 0.947697
params: {'C': 1, 'kernel': 'linear'}
Thnk you so much ,More complex series
I think this video is the best among all as it tells which one is the most efficient among all models
For the exercise part:
model best_score best_params
svm 0.973 {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
Random_Forest 0.909870 {'criterion': 'entropy', 'max_features': ''sqrt', 'n_estimators':10}
Logistic_Regression 0.928233 {'C': 1, 'penalty': 'l1', 'solver': 'liblinear'}
GaussianNB 0.814157 {'var_smoothing': 2e-09}
MultinomialNB 0.871464 {'alpha': 2.0}
Decision_Tree 0.805263 {'criterion': 'entropy', 'splitter': 'best'}
Good job subhajit on exercise 👏👍
but if i want to make predictions using this svm model how we can do that??
Thanks for the video. This was so much easier than the training provided on my MSc
This was an awesome video, please make more videos like this
Glad it was helpful!
Dhaval Patel @ codebasics. I am done with your tutorials till now. It's really great. Your videos shows your passion. Heartful thanks to you for providing an Initial step into ML.
If you can upload more real life project then that would be awesome.
I would like to request one thing, Please start creating videos for OpenCV. If people are learning from your videos then surely it will be worthy.
Thanks again !!!
I am working on more projects. I have one project series coming up pretty soon. Stay tuned
@@codebasics Bro really excited... Counting the days 😄😄
Amazing explanation ! Thanks !
Thanks! So nicely explained 👍👍
Glad it was helpful!
Appreciate your efforts sir, nicely explained
I am glad you liked it
Great explanation
Thank you sir! My result for the exercise is svm 0.968842 {'C': 1, 'kernel': 'poly'}, with best_score 96.88%.
you are the best teacher !!!
Thanks Samad for your kind words of appreciation
Excellent! Thanks for this tutorial
Glad it was helpful!
Thank you sir...You are a good man.
Great video, thanks
Thank you a lot. Excellent video. Congratulations.
Wow, this video was incredibly informative! It not only answered all the questions I had, but also covered additional topics that I hadn’t even considered. The explanations are very easy to understand. Thank you for the great content and for going the extra mile to ensure thorough understanding. Looking forward to more videos like this! Just a quick question I see that when fitting the models which include the GridSearchCV object and later the RandomizedSearchCV object with the training data you used iris.data and iris.target, instead of X_train and y_train. Should it have been X_train and y_train?
Excellent explanation. Thank you very much!
Very good explanation!
I am happy this was helpful to you.
very useful demonstration man! thank you!
Glad it was helpful!
Thank you so much for making things clear
Man, you just did it so well, thank you very much.
Glad you liked it!
Thank you. You solved my problem
Best 4 min from last.
Contains very useful information, thanks.
great work. thank you.
In exercise my answer coming is best model : SVM , best_params = { kernel=rbf,C=5 } with best_score of 0.97
BTW Happy Teachers Day sir 😊😊
You are a great guy
This video was so good and the fact that you gave the code. I subscribed :D
I am happy this was helpful to you.
Thank U sir to add Arabic translation, I appreciate that 😞🙏🏻
for iris dataset svm and logistic_regression results are same for paramters :
svm 0.980000 {'C': 1, 'kernel': 'rbf'} and
logistic_regression 0.980000 {'C': 100} ,
for given exercise for digits dataset the best model is
svm score and paramters are : 0.947697 {'C': 1, 'kernel': 'linear'} ,
second best model is : random_forest 0.941586 {'n_estimators': 192}
thank you sir for such a nice explanation
this was so helpful, thank you!
Awesome video, well explained!!
🙏😊👍
Using RandomizedCV:-
LogisticRegression : Best Score =91% , C=1
RandomForestClassifier: Best Score =94% , n_estimators=100
DecisionTreeClassifier : Best Score =81% , criterion = entropy
SVC : Best Score =97%, C=50, gamma =scale, kernel= rbf
GaussianNB : Best Score =72%, priors=None, var_smoothing=1e-15
MultinomialNB :Best Score =87%, alpha =0
Best Classifier =SVM
but if i want to make predictions using this svm model how we can do that??
Do you have your code posted somewhere? Would love to see it.
Amazing 🤩❤️
Glad it was helpful!
I have visited lots of channels and have bought 2-3 courses, but only this channel could make these concepts clear to me. Thankful to you! @codebasics🏆
Really good videos
Your videos are really quite informative and the exercises are helpful. Thanks !!
Glad you like them!
you are the best
you are soo good!!
Thank you!! It was really helpful. You just explain everything in the right way..😊
Glad it was helpful!
great job
Sir your teaching helps me a lot. Please make a video on pipelines.