Importance of Education
Josh, whats your opinion about train,cross validation and test spliting? And does it will be mentioned in future episodes?
You seriously improved your pronnuntiation from "android which is amazing at reading" to "actual human being". I think are the mannerism and the body language, mostly. Greato job, jokes aside, i think it helps to absorb the content.
Please, do more of these videos for advanced level.
hello, this is what i got while running it on my sublime text 3 from sklearn.cross_Validation import train_test_SplitImportError: No module named cross_Validation
you should just do this. google IO is fine. This seems more important
Any one knows where i can get some test datasets in a csv format? learning purposes (im not a machine btw )
Thank you for the great video, but my OCD is going on TILT with that lowercase 'y' variable lol
What this guy is doing with his hands? Swimming? I feel dizzy…
can not understand why are you using Python2.x? why not Phyton3.x?
I build the network for the hardest data set, thanks for the link!
hi, great series!!can you publish the code uses?
I just had 1 question: When we split the data using split command, we basically make the first 75 as training and rest 75 as testing. However, if we don't give training data of the 3rd label, how is it classifying that as well? Or am I interpreting the split function wrongly?Great videos though. As a beginner, these are really helping me.
what if the spliting method takes all data related to one flower (in iris example) and assign it to test data. can we select the order/randomness
sklearn.cross_validation is deprecated. need to change to sklearn.model_selection. Using a IDE is always better than typing code on NotePad
I'm loving this series
I have great trouble keeping up with the syntax, any suggestions ?
sklearn.cross_validation will be obsolete soon.
Something doesn't work for me – some DeprecationWarning appears… What should I do in this case? Here is print screen:
For line number 9 I am getting invalid syntax in Spyder IDE. Please help
Well done, thank you.
I am really moved for this lecture thanks for great videos~
10/10 quality production. Teacher speaks clearly and is easy to understand / enthusiastic. Content is well organized and can be followed each step of the way. I found this series to be the most valuable guide on machine learning on Youtube at the moment. All of the code has worked for me in Python3.4 as well.
Strange smiling after every sentence. Why do big tech company employees seem like cult members?
great explanation … you are a rockstar
could u make a video about Neural Network, its hard to understand this concept
i love this guy
note that cross_validation has changed to model_selection
You are the bomb.
please help me out i am getting this error File "<ipython-input-116-f9c2da5a35bb>", line 6, in <module> x = iris.dataAttributeError: 'function' object has no attribute 'data'
I have created a github repo with all of the code for all of the recipes of this series. I've used Python3 for all recipes. I've also updated all of the libraries and have added some things to the code here and there. Check it out: https://github.com/TheCoinTosser/MachineLearningGoogleSeries
About features in knn:The features are finite.So you can create all combinations of them, and then see if we quit one, the results dont change too much… ok ok it could be a lot of computing power, but is the machine deciding by its own rules, no human interaction.This is just good if some of hour features are good, if all are bad dont solve anything 🙂
Notice here, our accuracy was over 9000
where can I get the source code?
It's a good leason,thank u
My major is Statistics and I want to apply for a PhD position in Statistics. But after seeing this series, I have changed my mind!
Would we select the classifier with the most accuracy after we test? Also, after we test, shouldnt we feed the testing data too, to increase accuracy?
This is Awesome
Why is this a voiceover ( a random chat
Hi All, I created a nicely formatted repository containing the code from this video, but updated to work with new packages.https://github.com/officialgupta/MachineLearningRecipesLike this so people can see it!
This is goood
3 seconds from here 5:16
Awesome series, straight to the point and very clear. Keep going!
+Josh Gordon Hey, I am getting an "value error: too many values to unpack" error on executing.I have tried using model_selection instead of cross_validation, and still same error pops up.Can you help me out?
When you moved that line making red come to the right. I must say it was the magical moment!
Thank Josh…, this is help me to understandmachine learning basically 🙂
this one is good
Doing it on Python 3? Don't want to pause the video and write? Find the code here: https://github.com/akanshajainn/Machine-Learning—Google-Developers
OMG, I finally see a reason for learning math in high school. I'm so happy I took the time to learn about equation of a line and finding slopes. XD
does sklearn automatically classifies what is data and target, if so can we change target
cross_validation will be deprecated soon, we can use model_selection module now.
Why do we use a capital X and lowercase y?
great video. thanks.
If anyone is watching this when cross_validation becomes deprecated, replace cross_validation with model_selection. The classes and functions should work the same, as they are being refactored and moved to this namespace.
Their a lots of different classifier algorithm available . but how once can select suitable algo for classification. What should be the criteria for the selection of classification algorithm
Excellent thanks!! you just opened my mind about machine learning… I was stuck on the concept
Body language tells this guy licks shoes to climb. Disgusting.
Went through it all…Where is the pipeline? 🙂
what if the new dot is neither red nor green , How can the classifier recognize that, and return with value 'false' instead of wrong prediction ? I'm working on face recognition project and I'm using this sklearn library … any ideas how can i recognize the face that it's not in the training data ? thanks
Excellent series, very helpful.
Very well presented!
if you have troubles executing this…1) make sure you have "sklearn.model_selection" instead of "sklearn.cross_validation"2) If your dataset is undefined, check spelling. Uppercase X and lowercase Y used continuously in this example
Great work Gosh, keep it up, a pretty gift to the world from google.
Really a Edu genious can make up something like that. Thanks mate.
Great Videos. Keep it up!
I find myself wondering if he is a real person
What we did in whole year project is in this video ,lol
Can we achieve more accuracy or probably even 100% accuracy by making the classifier more complex or have more parameters. Example– We could classify the dots(more random) better by having a more complex function such as a cubic or a bi-quadratic one right?
amazing video , very well explained . Thank you Sir.
I put this on at night and slept to 12 in the afternoon. I put it back on 3 hours after I woke up and fell asleep again for 2 hours
the code @3:21 doesn't work unless i also include: from sklearn import neighbors
replace sklearn.cross_validation with sklearn.model_selection as cross_validation has been deprecated.
These 7-8 min videos are better then hours of "so called" tutorials. watching in 2018.
from sklearn.cross_validation import train_test_split/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning)
Great. Here is my code for classifying the Iris Flower dataset using the Random Forest Classifier~
from sklearn.datasets import load_irisiris=load_iris()X=iris.dataY=iris.target
from sklearn import ensemblefrom sklearn.cross_validation import train_test_split
clf=ensemble.RandomForestClassifier()clf.fit(X_train,Y_train)predictions=clf.predict(X_test)print("Using Random Forest Classifier, Predictions are:")print(predictions)
from sklearn.metrics import accuracy_score
print("Accuracy Score in percent is:")score=accuracy_score(predictions,Y_test)print(score*100)
If you get the deprecation warning, simply replace:from sklearn.cross_validation import train_test_splitwithfrom sklearn.model_selection import train_test_split
finally i understand machine learning 😍😍😍
Very nice videos I liked all..!!! 🙂 The way you are presenting the example it triggered me to learn Python.. U made it look simple 🙂 I am android developer and have total interest in machine learning…. 🙂 Thanks for the good content… 🙂
Simply Awesome Video. Thanks much Love ML
aaaaaaaaaaaaah! ridiculous pace.hint: watch these videos at 0.5 speed or slower.press pause frequently to digest whats going on.
Thanks a lot, Josh and Google Developers for these awesome episodes. Finally, I've understood ML. You're the best!
The video was, so incomplete
Google's translator don't know what is Scikit in subtitles)
this is very good tutorial, Thanks a lot Josh
Here is a quick summary of the video:
– scikit-learn has a handy function for splitting data sets into a training and a testing set– it's sklearn.model_selection.train_test_split(data_set_features,data_set_labels,test_fraction)– this function will return 1) training_features 2) testing_features 3) training_labels and 4) testing_labels– i.e. it returns a tuple of 4 elements– note, the test_fraction argument specifies the fraction of the data you want to use for testing– so if you put 0.5, it means you want to use half the data for testing (and the other half for training obviously)
– recall that the .predict() method returns a list of predictions for the list of examples you pass it– you can use sklearn.metrics.accuracy_score(test_labels,predicted_labels) to compare two list of labels essentially
– supervised learning is also known as function approximation because ultimately what you are doing is finding a function that matches your training examples well– you start with some general form of the function (e.g. y = mx+b) and then you tune the parameters such that it best describes your training examples (i.e. change m and b until you get a line that best splits your data)
Key thing to take away from the video:Supervised learning is just function approximation. You start with a general function and then tweak the parameters of the function based on your training examples until your function describes the training data well.
Josh, you are not only knowledgeable of all these ML, but also a outstanding instructor. Simplified all these complicated methods. Cant thank you enough.
Why is "X" capital and not "y"?
Are you an ML?
great great videos!!
the content is great but requires an update
from sklearn.model_selection import train_test_split
extremely helpful and simple (y) (y)
Android 5.0 questions is def spam
Thanks excellent series good work 🙂
I am getting this error while doing accuracy check: accuracy_score() missing 1 required positional argument: 'y_pred'
may someone help me to sort out.
i thought this video was about pipeline in sklearn WTF
did I miss something ? Where is the pipeline ?
By the way, cross_validation module has been renamed model_selection. Lesson 0: learn to go read the documentation of modules you use. Stuff changes constantly.
print predictions should be print(predictions)
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