Having problems with the code? I just finished updating the notebooks to use *scikit-learn 0.23* and *Python 3.9* 🎉! You can download the updated notebooks here: github.com/justmarkham/scikit-learn-videos
Sorry to be off topic but does anyone know of a method to get back into an Instagram account..? I was stupid forgot the password. I would love any tricks you can give me!
@Lyle Elias Thanks for your reply. I got to the site thru google and im trying it out atm. Seems to take quite some time so I will get back to you later with my results.
This is by far the best Sci-kit Learn tutorial on UA-cam. I can say this because I have seen almost every tutorial and this covers everything starting from scratch.I knew how all the algorithms work but what I needed was how do I implement those algorithms from loading the data set to all terminologies to checking the accuracy and what not and this series has everything I was looking for ,thank you so much for this.Really appreciate it.
The last two videos are the best ones I’ve seen someone explain scikit learn’s predictions. Every other video jumps straight to the full analysis but in reality, you can predict in as little as 4 lines of code. Great job!
*Note:* This video was recorded using Python 2.7 and scikit-learn 0.16. Recently, I updated the code to use Python 3.6 and scikit-learn 0.19.1. You can download the updated code here: github.com/justmarkham/scikit-learn-videos
This video series sets such a high standards for Content, Context and Delivery of Machine Learning training ! Its a winner for all those who are starting to learn Machine Learning !! Thank you so much for your efforts Kevin !!!
Uno de los mejores manuales sobre "Machine learning" que he visto. Gracias por ofrecernos la oportunidad de aprender. Además, tu pronunciación es perfecta para hispanohablantes
I couldn't agree more with berry jordaan. The way you deliver the content of a quite complex topic naturally guides me to want to learn more about machine learning. Thank you very much
Excellent teaching !!! I am required to set up competency around advance analytic involving ML/DS (since I am coming from DWH and BI practice) in my organization, so I wanted to learn and practice. Now , I feel like taking this as a full time profession and become Data Scientist. It's so much fun and exciting work, such video has made it lot easier. Thank you !!!
Wow I must say your teaching style is amazing. Very organized, thorough and easy to follow. Thanks for your time, and keep making great videos! I wish more professors were like you at my school.
Thank you Kevin for sharing well organized, normal speed video lectures on scikit learn. These videos are very helpful to teach ML in python to graduate students. The links in the resources are also very valuable. You deserve appreciations. I would suggest to upload lectures ML with R.
You're very welcome! I'm glad to hear the videos have been helpful to you! I'm focused on Python these days, so I don't anticipate making any videos on R - sorry!
I have been reading from a lot of source but till date this series is the best! I wish there much more videos and reference which will take us to the advanced level!
Best video series I've come across on sklearn! I tried a few other channels before this and was left feeling like I still had no idea what was going on, but after only 5 of your videos I already feel way more confident that I can actually get into it, cheers!
Thank you so much for putting up this series. I was looking for something basic yet comprehensive and something easy to follow. This is being very helpful to me . Thanks.
Thanks so much for all these videos! Im doing an internship at a really nice group but they're letting me figure out most of the stuff by myself so this is super useful!
Great series, honestly it's the most easily understandable lecture about one of the complicated topic in computer science. Love the flow of the video, the tempo of the complexity, really easy to follow. I have several comments to improve in my opinion: 1. When you point out on specific parts of the screen, it would be great to not just use the cursor but also a more visually impactful feedback (there are tools for this) 2. Would love to get a repeated definition of the specific terms (such as model complexity, what does that mean? The higher the value of n_neighbors the more complex it is? what does it mean to be complex?) 3. I understand that this is an introduction class, but it would be really helpful to show the industry's best practices (advanced series?) Great work, I subscribed, and liking all of your videos.
+SomeIndoGuy Thanks for your very kind comments, as well as your feedback! Regarding model complexity, this is an excellent essay on the bias-variance tradeoff (a critical machine learning topic) that touches on model complexity: scott.fortmann-roe.com/docs/BiasVariance.html
Awesome.....Highly effective communication......So for the best of Machine Learning videos......very grateful to the author. The flow and methodology makes Machine Learning look so simple which in fact is quite complex for beginners like me.
Thanks so much for your kind comment! I'm glad to hear the machine learning videos have been helpful to you. I know it's complex but you will get it eventually... good luck with your education!
I started after you put a video on how to make submission on kaggle on my request,I did well in last contest and finished 144 in leader board :) All credit goes to you
Amazing!!! That's great to hear! :) For others who might be interested, this is my video about creating Kaggle submissions: ua-cam.com/video/ylRlGCtAtiE/v-deo.html
Most notable take aways from the video: - "Plotting testing accuracy vs model complexity is a very useful way to tune any parameters that relate to model complexity." - "Once you have chosen a model and it's optimal parameters and are ready to make predictions on out of sample data, it's important to re train your model on all of the available training data." - Repeating the train/test split process multiple times in a systematic way using k fold cross_validation
Wow. This video in particular is one of the most useful videos that I have found in the entire UA-cam. Thanks you very much, your a great person and a great teacher!
Thank you very much again! I look forward to learning mode on the various libraries and models for machine -learning, with great examples as usual. Greetings!
You're very welcome! I have a video series on pandas (for data exploration, cleaning, etc) that might interest you: ua-cam.com/play/PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y.html
A great series for sklearn beginners, and your rate of speech really taken care of people like me, for a moment there I thougth I had a significant progress in hearing, HAHA. Thank you very much from China.
awesome video. i would also love to see a video regarding SVM kernels, major differences among them, when to choose them, and how the different parameters may affect the classification and the metrics.
Really your lectures are AWESOME :-)...........................The way you are explaining is really SUPER ( at every point you are giving reason why you are doing this things makes your lectures UNIQUE from all others resources). One more problem which currently i am facing is related to problem solving related to machine learning, so please is it possible to make 4-5 videos in which you explain five different types of problems with five different fields ( like business ,medical,education,banking etc).
When it comes to using the model for future predictions on real-life data, you can directly use the trained model without retraining it with the whole training data, including the test data. The idea is that the model has learned patterns and relationships from the training data that generalize well to unseen data, including real-life data. Retraining the model with the entire dataset, including the test data, is generally not recommended as it may lead to overfitting. Overfitting occurs when the model becomes too specific to the training data, capturing noise and irrelevant patterns, which can reduce its performance on new data.
Man, your videos are great! :) I'm taking the Machine Learning Nanodegree by Udacity and your videos are an awesome supportive material. Thanks for sharing with us!
you're doing a great job, I would just emphasize on giving more examples that are relatable and speaking like you're talking to another person in the room. I only give feedbacks because thats what I would've wanted from people tuning in.
Great videos kevin. I like your deliberately slow style. It is hard to improve, but if I may suggest something. As your videos are long, it would be useful if you have an index in the description with links to the times of the subtopics. That would help a lot on review and certainly would increase the number of re-visits.
Thanks for the suggestion! I know the videos are super long, but ever since making this series, I have tried to make shorter videos. And, thanks for the time-coding suggestion! I'll consider it.
Thank you so much for educating us......The resources are really helpful....Structured lectures are interesting....Please continue videos with SciPy NumPy SciKit
Wow Videos...Great for reference. Really appreciate your efforts in making such videos which proves very beneficial to beginners like me. Please keep sharing your knowlegde to us through such excellent videos.
Great series of courses on Pandas and Scikit Learn! I’ve been enjoying every video I watched on this channel. Thanks so much! On machine learning using Scikit Learn, I’m wondering if you could share a lesson on Random Forest and related concepts. Thanks again. Terry
Thanks for your kind words! Thanks for your lesson suggestion. I don't have a video about that topic, but check out this page and search for "random forests": www.dataschool.io/start/
I like the videos. Great work! Hope to see more. But I do worry a bit that you say you will still be using a lot of train/test split in the future. The problems that this method introduces are well established in the literature, and, given the ease of implementing either a cross-validation or bootstrap in Python/Scikit, it is a good habit for students/beginners to get into. Looking at this from the other side -- working with students who have picked up the train/test split habit from prior classes and online learning -- it is usually very hard to get them to use more valid procedures in their work. And except for exotic circumstances, it is usually not possible to justify using train/test for either real-world or more basic research. (See Hastie, Tibshirani, and Friedman's books for justifications.) Still, with that caveat, I do recommend your videos to students! Thank you for your work.
+Matthew Turner I do appreciate the point. However, there are goals for model evaluation beyond just producing the most reliable estimates of out-of-sample error. For example, error diagnosis often requires looking at the confusion matrix, and while train_test_split makes this easy, cross_val_score does not. As well, cross_val_score needs to be used as part of a pipeline if you have any preprocessing, such as feature extraction or feature standardization. Teaching pipeline adds complexity that I find most students struggle with early on. That is another reason I use train_test_split, because you can do proper model evaluation (that includes preprocessing) without pipeline. All in all, it comes down to one's priorities, as well as the Python and scikit-learn fluency of one's students. I do appreciate your perspective, and am aware of the tradeoffs, but I've made a purposeful choice in this area based on my educational priorities and the backgrounds of the students I hope to reach. Thanks for sharing my videos with others! I appreciate it.
Actually, English is a second language for around 50% of my viewers, and many of them have commented that they appreciate that I speak in a clear manner. For native English speakers that find the videos move too slowly, it can be helpful to use the UA-cam controls to speed up the videos.
Great video series... im following your explanation and links and its awesome! Keep up with this series, exploring more and more about sklearn and python for data science!
Hey Kevin, you are creating an excellent resource for those interested in getting started! I have two questions, first I was wondering if you knew of any dataset repositories that we could practice these techniques independently. I like to learn by doing, and having a repository of datasets would be useful. Secondly, do you have a Patreon account or some way I can give some donations to you for these videos? Your work done on these and your other videos is well deserving of at least some sort of contribution to your pocket!
drumsking10 Wow, thanks for the kind comments! 1. The UCI Machine Learning Repository is an excellent collection of datasets. You can filter on task, attribute type, etc., and many of the datasets are well-documented: archive.ics.uci.edu/ml/datasets.html 2. That's very generous of you! It is a lot of work (10 to 20 hours per video for this series), but it has also been a lot of fun! I don't currently use Patreon, though if you visit my main channel page, there is a box on the right side that says "Support this channel": ua-cam.com/users/dataschool
+Tony Nicholas You're welcome!! It was a ton of work to create this series, but it's great to hear that it has been a valuable resource for a lot of people.
Having problems with the code? I just finished updating the notebooks to use *scikit-learn 0.23* and *Python 3.9* 🎉! You can download the updated notebooks here: github.com/justmarkham/scikit-learn-videos
Sorry to be off topic but does anyone know of a method to get back into an Instagram account..?
I was stupid forgot the password. I would love any tricks you can give me!
@Emmitt Kyrie Instablaster :)
@Lyle Elias Thanks for your reply. I got to the site thru google and im trying it out atm.
Seems to take quite some time so I will get back to you later with my results.
That's some killer delivery, you didn't waste a word! Great tutorial!
Thanks so much!
This is by far the best Sci-kit Learn tutorial on UA-cam. I can say this because I have seen almost every tutorial and this covers everything starting from scratch.I knew how all the algorithms work but what I needed was how do I implement those algorithms from loading the data set to all terminologies to checking the accuracy and what not and this series has everything I was looking for ,thank you so much for this.Really appreciate it.
Wow! Thank you so much for your kind words! :)
Really simple to understand. Doesn't make it seem like "its a library thing, library does it for ya". Thank you for doing this
You're very welcome! Thanks for your kind words!
I like the pace of these videos. You speak really slow and clear which helps your viewer to digest the information on the fly. Loving your work!
Thanks for the feedback! I'm really glad to hear that my presentation of the material works well for you. Good luck with your education!
Yeah, the slow pace is generally great, though personally I view these at 1.25 speed. Still clear at that rate too. :)
"models that overfit have learned the noise in the data rather than the signal" - yes, well said!
Glad it was helpful to you!
Your way of delivery is exceptional. I have never seen somebody teaching so well like you. I made me interested in ML Thanks bro...God bless U
Awesome, thank you!
Dear Kevin. To me your videos are a reference, as those of Mr Andrew Ng. Very good job! Thank you very much from Spain :)
You're very welcome!
The last two videos are the best ones I’ve seen someone explain scikit learn’s predictions. Every other video jumps straight to the full analysis but in reality, you can predict in as little as 4 lines of code. Great job!
Thanks! :)
Your teaching style is outstanding. As someone who has used R in the past, I really appreciate the clarity of your explanations and demonstrations.
Thank you, Frank!
*Note:* This video was recorded using Python 2.7 and scikit-learn 0.16. Recently, I updated the code to use Python 3.6 and scikit-learn 0.19.1. You can download the updated code here: github.com/justmarkham/scikit-learn-videos
You're very welcome!
@@dataschool can we have a lecture about Tensorflow?
I thank God I landed on your videos. I see things clearer than ever. You are a gifted tutor. God bless you sir.
Wow, thanks so much for your incredibly kind comments!
loving this series man just started out with ML and DS understanding everything
That's excellent to hear!
This video series sets such a high standards for Content, Context and Delivery of Machine Learning training ! Its a winner for all those who are starting to learn Machine Learning !! Thank you so much for your efforts Kevin !!!
Wow, thank you so much for your very kind comment! I really appreciate your support!
Uno de los mejores manuales sobre "Machine learning" que he visto. Gracias por ofrecernos la oportunidad de aprender. Además, tu pronunciación es perfecta para hispanohablantes
A student from CN jumping across the Great Wall learned this excellent class. Thx.
Awesome! You're very welcome!
I couldn't agree more with berry jordaan.
The way you deliver the content of a quite complex topic naturally guides me to want to learn more about machine learning.
Thank you very much
Thank you so much for your comment - you're very welcome!
This is such a gem for beginners .Thank you very much Kevin
You're very welcome!
Excellent teaching !!! I am required to set up competency around advance analytic involving ML/DS (since I am coming from DWH and BI practice) in my organization, so I wanted to learn and practice. Now , I feel like taking this as a full time profession and become Data Scientist. It's so much fun and exciting work, such video has made it lot easier. Thank you !!!
Awesome! So glad to hear! Thanks for your kind words, and good luck on your educational journey :)
Wow I must say your teaching style is amazing. Very organized, thorough and easy to follow. Thanks for your time, and keep making great videos! I wish more professors were like you at my school.
+Juan P Castillo What a nice comment! Thank you so much for your generous words! I'm glad the series has been helpful to you :)
I was looking for ML tutorials and can say that your videos are simply the best.Thanks a lot
Wow, thank you so much! What a nice comment!
This was one of the best videos on the topic that I've found. Thank you for being so succinct and breaking this down so clearly!
You're so very welcome!
Thank you Kevin for sharing well organized, normal speed video lectures on scikit learn. These videos are very helpful to teach ML in python to graduate students. The links in the resources are also very valuable. You deserve appreciations. I would suggest to upload lectures ML with R.
You're very welcome! I'm glad to hear the videos have been helpful to you! I'm focused on Python these days, so I don't anticipate making any videos on R - sorry!
I have been reading from a lot of source but till date this series is the best! I wish there much more videos and reference which will take us to the advanced level!
Thanks so much for your kind comment!
My confident level is super high to learn Machine Learning after seeing this video. Your every word is very clear and correct. Thank you very much.
That's awesome to hear!
Kevin this series is excellent, you are able to really simplify the topic to make it easy to learn Thanks
Thank you!
Best video series I've come across on sklearn! I tried a few other channels before this and was left feeling like I still had no idea what was going on, but after only 5 of your videos I already feel way more confident that I can actually get into it, cheers!
Awesome! Thanks for your kind comments, and good for you! :)
It used to be hard for me to learn Machine Learning, but now thanks to you it isn't anymore
Thanks so much! That is awesome to hear 😄
OMG finally found a ML tutor who is awesome.... i cant skip any seconds in your videos, even each words are informative
Thanks so much for your kind words! I truly appreciate it!
Man, he just makes it so easy to learn.
Wish we had half as good teachers as him in school.
Thank you so much Gautam!
Thank you for these videos! they are well made and clear. I don't think i understood ML until sitting through your videos.
Thanks for your kind comment! That's so nice to hear.
Thanks Sir , for giving effort to make these videos . Being a beginner I find these resources extremely helpful .
You're welcome!
Thank you so much for putting up this series. I was looking for something basic yet comprehensive and something easy to follow. This is being very helpful to me . Thanks.
That's great to hear! You are very welcome.
I'm watching this tutorial from last few days..Very very precise and accurate content.. it made me to rewind watch many times..! great..!
Awesome! Glad it's helpful to you!
Thanks so much for all these videos! Im doing an internship at a really nice group but they're letting me figure out most of the stuff by myself so this is super useful!
You are so welcome!
This is a brilliant tutorial -- I love everything about it. Thanks.
+Khalil Muhammad Wow, thank you! I really appreciate your kind words!
“Overfitting learns the noise of the data, rather than the signals”
I finally understand what overfitting means.
Awesome! So glad to hear!
Yes , you are a great teacher.
Great series, honestly it's the most easily understandable lecture about one of the complicated topic in computer science.
Love the flow of the video, the tempo of the complexity, really easy to follow. I have several comments to improve in my opinion:
1. When you point out on specific parts of the screen, it would be great to not just use the cursor but also a more visually impactful feedback (there are tools for this)
2. Would love to get a repeated definition of the specific terms (such as model complexity, what does that mean? The higher the value of n_neighbors the more complex it is? what does it mean to be complex?)
3. I understand that this is an introduction class, but it would be really helpful to show the industry's best practices (advanced series?)
Great work, I subscribed, and liking all of your videos.
+SomeIndoGuy Thanks for your very kind comments, as well as your feedback!
Regarding model complexity, this is an excellent essay on the bias-variance tradeoff (a critical machine learning topic) that touches on model complexity: scott.fortmann-roe.com/docs/BiasVariance.html
I have to say this is another great lesson by Kevin. Thank you very much indeed.
Thanks! I'm glad my videos are helpful to you!
Awesome.....Highly effective communication......So for the best of Machine Learning videos......very grateful to the author. The flow and methodology makes Machine Learning look so simple which in fact is quite complex for beginners like me.
Thanks so much for your kind comment! I'm glad to hear the machine learning videos have been helpful to you. I know it's complex but you will get it eventually... good luck with your education!
I believe the K value you have set for teaching is perfect for my learning. thanks
+vishwas s HA! Love a good machine learning joke :)
I started after you put a video on how to make submission on kaggle on my request,I did well in last contest and finished 144 in leader board :) All credit goes to you
Amazing!!! That's great to hear! :)
For others who might be interested, this is my video about creating Kaggle submissions: ua-cam.com/video/ylRlGCtAtiE/v-deo.html
I love the series so far. I have learned so much. Thank you for creating these. They are quite easy to follow.
Awesome, that's great to hear!
Most notable take aways from the video:
- "Plotting testing accuracy vs model complexity is a very useful way to tune any parameters that relate to model complexity."
- "Once you have chosen a model and it's optimal parameters and are ready to make predictions on out of sample data, it's important to re train your model on all of the available training data."
- Repeating the train/test split process multiple times in a systematic way using k fold cross_validation
Great summary! I approve :)
Can't thank you enough for your great tutorials, Kevin. Make everything so clear and understandable. You're awesome man.
Thank you so much!
I love you man, i have watched every single video of yours.
Thank you so much!
Wow. This video in particular is one of the most useful videos that I have found in the entire UA-cam. Thanks you very much, your a great person and a great teacher!
Wow, thank you so much! :)
very good work man. u even reply to all the messages from the path 3 years. its very helpful.
make more of these videos
This lecture is fantastic and extremely helpful to learn machine learning from scratch, very appreciate to share this wonderful vedio
Thanks for your kind comment!
if it means anything to you, i really like the way you put things and simplify them, thanx man
Thanks for your kind comment!
Thank you very much again! I look forward to learning mode on the various libraries and models for machine -learning, with great examples as usual. Greetings!
Thanks for your suggestions! This course might interest you: www.dataschool.io/learn/
great video ! great resources to understand the Bias-Variance trade-off. You are a reference Kevin. Thanks a ton.
You're very welcome!
Your videos are so cool and easy to understand, thanks for uploading,please keep on doing it!
Thanks!
Thanks for the great video series. Please keep uploading .
You're very welcome! I have a video series on pandas (for data exploration, cleaning, etc) that might interest you: ua-cam.com/play/PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y.html
Certainly it would. Thanks allot.
A great series for sklearn beginners, and your rate of speech really taken care of people like me, for a moment there I thougth I had a significant progress in hearing, HAHA. Thank you very much from China.
You are very welcome! It's great to hear that my delivery works for you! Thanks for your kind comment.
I really disliked machine learning after we got taught it at uni. you really have sparked my interest again thank you so much for this series.
That's great to hear! You are very welcome.
Quarantine with Data School is lit!!
Thank you for such clear and well done tutorials!
Thanks for your kind words!
Nicely paced set of tutorials. Thanks
You're welcome!
awesome video. i would also love to see a video regarding SVM kernels, major differences among them, when to choose them, and how the different parameters may affect the classification and the metrics.
Glad you liked it, and thanks for the suggestion!
It is just awesome to understand the concept from you. Thanks a ton!
You're welcome!
Dude, thank you very much for this set of videos. I'm from Brazil and I'm really enjoying youe course.
Great!
Really your lectures are AWESOME :-)...........................The way you are explaining is really SUPER ( at every point you are giving reason why you are doing this things makes your lectures UNIQUE from all others resources).
One more problem which currently i am facing is related to problem solving related to machine learning, so please is it possible to make 4-5 videos in which you explain five different types of problems with five different fields ( like business ,medical,education,banking etc).
Thanks for your suggestion!
my goodness. what intriguing and useful videos. you have a true gift
Thank you!
you're the right model for us , because u trained such a way
Thanks!
When it comes to using the model for future predictions on real-life data, you can directly use the trained model without retraining it with the whole training data, including the test data. The idea is that the model has learned patterns and relationships from the training data that generalize well to unseen data, including real-life data.
Retraining the model with the entire dataset, including the test data, is generally not recommended as it may lead to overfitting. Overfitting occurs when the model becomes too specific to the training data, capturing noise and irrelevant patterns, which can reduce its performance on new data.
Thanks for sharing, but if I'm understanding you correctly, I respectfully disagree.
this is the best machine learning tutorial I have ever gone through.
thank u so much.👍
You're very welcome! :)
Man, your videos are great! :)
I'm taking the Machine Learning Nanodegree by Udacity and your videos are an awesome supportive material. Thanks for sharing with us!
Thanks for your kind words! You are very welcome :)
matee, awesome videos. You saved my ass for an ML deadline. Awesome, really
That's awesome to hear!
you're doing a great job, I would just emphasize on giving more examples that are relatable and speaking like you're talking to another person in the room. I only give feedbacks because thats what I would've wanted from people tuning in.
Thanks for your suggestions!
You brought back my motivation to study. I am so Grateful :) Thanks a lot!!!
You're welcome! :)
Thank you very much for this elaborated explanation. Very helpful
You're welcome!
Best tutorial I have ever seen. period.
Wow! Thank you so much!
You are doing a really good work with concise explantions, thanks a lot !!
+arab ilies You're very welcome!
Great explanation of noise and signal!
Awesome, thank you!
for i in range(1, 10001) :
print(“THANK YOU VERY MUCH")
HA! Love it! You're very welcome :)
Data School Thank you.
Your reply shows your passion for programming.
Keep up the good work of teaching.
Thanks! :)
No ,this is the correct one:
while 1 == 1:
print("THANK YOU VERY MUCH")
while True: print("THANK YOU VERY MUCH")
i love everything about the series. Thank you very much
Thanks!
Great videos kevin. I like your deliberately slow style. It is hard to improve, but if I may suggest something. As your videos are long, it would be useful if you have an index in the description with links to the times of the subtopics. That would help a lot on review and certainly would increase the number of re-visits.
Thanks for the suggestion! I know the videos are super long, but ever since making this series, I have tried to make shorter videos.
And, thanks for the time-coding suggestion! I'll consider it.
Thank you so much for educating us......The resources are really helpful....Structured lectures are interesting....Please continue videos with
SciPy
NumPy
SciKit
Thanks for your suggestions!
Stunningly clear logic and structure. Thank you!
You're very welcome! 🙌
Wow Videos...Great for reference. Really appreciate your efforts in making such videos which proves very beneficial to beginners like me. Please keep sharing your knowlegde to us through such excellent videos.
You are very welcome!
Thanks. Clear and concise, straight to the point
Thanks for your comment!
Man, thanks for what you're doing. It's a really great help for people
You're very welcome! Thanks for your comment.
Your Explaination is simply superb. Love to watch some more videos
Thanks so much for your kind comment! The entire video playlist for this series is here: ua-cam.com/play/PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A.html
Great series of courses on Pandas and Scikit Learn! I’ve been enjoying every video I watched on this channel. Thanks so much!
On machine learning using Scikit Learn, I’m wondering if you could share a lesson on Random Forest and related concepts. Thanks again.
Terry
Thanks for your kind words! Thanks for your lesson suggestion. I don't have a video about that topic, but check out this page and search for "random forests": www.dataschool.io/start/
Thanks for the reply! Much appreciated!
Your videos are really benificial! Please Keep it up!
Charbel Zeaiter Thanks for your kind comment! :)
Thank you for the great class! I learned so much from your video!!!
Great to hear!
Thank you very much for your valuable time explaining machine learning. I will always think how they predict the future value. Now I know.
You're welcome!
I ADORE YOU 💕💕💕 you teach difficult concepts really well. Thank you and i hope you keep posting.
Thank you! :)
I like the videos. Great work! Hope to see more.
But I do worry a bit that you say you will still be using a lot of train/test split in the future. The problems that this method introduces are well established in the literature, and, given the ease of implementing either a cross-validation or bootstrap in Python/Scikit, it is a good habit for students/beginners to get into. Looking at this from the other side -- working with students who have picked up the train/test split habit from prior classes and online learning -- it is usually very hard to get them to use more valid procedures in their work. And except for exotic circumstances, it is usually not possible to justify using train/test for either real-world or more basic research. (See Hastie, Tibshirani, and Friedman's books for justifications.)
Still, with that caveat, I do recommend your videos to students! Thank you for your work.
+Matthew Turner I do appreciate the point. However, there are goals for model evaluation beyond just producing the most reliable estimates of out-of-sample error. For example, error diagnosis often requires looking at the confusion matrix, and while train_test_split makes this easy, cross_val_score does not.
As well, cross_val_score needs to be used as part of a pipeline if you have any preprocessing, such as feature extraction or feature standardization. Teaching pipeline adds complexity that I find most students struggle with early on. That is another reason I use train_test_split, because you can do proper model evaluation (that includes preprocessing) without pipeline.
All in all, it comes down to one's priorities, as well as the Python and scikit-learn fluency of one's students. I do appreciate your perspective, and am aware of the tradeoffs, but I've made a purposeful choice in this area based on my educational priorities and the backgrounds of the students I hope to reach.
Thanks for sharing my videos with others! I appreciate it.
This man speaks so mechanically I feel like I'm in an English as a Second Language class.
Actually, English is a second language for around 50% of my viewers, and many of them have commented that they appreciate that I speak in a clear manner. For native English speakers that find the videos move too slowly, it can be helpful to use the UA-cam controls to speed up the videos.
your kind words sound beautiful!
+yu jerry Ha, thanks! :)
Really great videos. Very informative videos for newbies like me. Thanks!!!!
You're welcome! Thanks for your kind comment!
Bravo! This was clear and easy to follow. Thanks!
You're very welcome! Thanks for the kind comments!
Great video series... im following your explanation and links and its awesome!
Keep up with this series, exploring more and more about sklearn and python for data science!
Rodolfo De Nadai Excellent, great to hear! I've got a new video coming out very soon :)
Cool!! 100% that i'll watch!
I like how you teach! really clear! Thank you very much from China!
Thanks for your kind comment!
Hey Kevin, you are creating an excellent resource for those interested in getting started! I have two questions, first I was wondering if you knew of any dataset repositories that we could practice these techniques independently. I like to learn by doing, and having a repository of datasets would be useful. Secondly, do you have a Patreon account or some way I can give some donations to you for these videos? Your work done on these and your other videos is well deserving of at least some sort of contribution to your pocket!
drumsking10 Wow, thanks for the kind comments!
1. The UCI Machine Learning Repository is an excellent collection of datasets. You can filter on task, attribute type, etc., and many of the datasets are well-documented: archive.ics.uci.edu/ml/datasets.html
2. That's very generous of you! It is a lot of work (10 to 20 hours per video for this series), but it has also been a lot of fun! I don't currently use Patreon, though if you visit my main channel page, there is a box on the right side that says "Support this channel": ua-cam.com/users/dataschool
I finally launched a Patreon campaign, and I'd love to have your support! www.patreon.com/dataschool/overview
Better teacher than profs:)
Thanks so much! :)
Love you man. Cant thank you enough for all your hard work.
+Tony Nicholas You're welcome!! It was a ton of work to create this series, but it's great to hear that it has been a valuable resource for a lot of people.
The links at the bottom of your comments are great.
Thanks!