Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

[@KeithGalli]

Video outline! 0:20 - What we will be doing! 3:40 - Sci-Kit Learn Overview 6:38 - How do we find training data? 9:33 - Download data 11:45 - Load our data into Jupyter Notebook 16:38 - Cleaning our code a bit (building data class) 20:13 - Using Enums 22:50 - Converting text to numerical vectors, bag of words (BOW) explanation 25:45 - Training/Test Split (make sure to "pip install sklearn" !) 33:45 - Bag of words in sklearn (CountVectorizer) 40:05 - fit_transform, fit, transform methods 42:05 - Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification 47:50 - predict method 53:35 - Analysis & Evaluation (using clf.score() method) 56:58 - F1 score 1:01:01 - Improving our model (evenly distributing positive & negative examples and loading in more data) 1:20:36 - Let's see our model in action! (qualitative testing) 1:22:24 - Tfidf Vectorizer 1:25:40 - GridSearchCv to automatically find the best parameters 1:31:30 - Further NLP improvement opportunities 1:32:50 - Saving our model (Pickle) and reloading it later 1:36:37 - Category Classifier 1:39:14 - Confusion Matrix Thank you for watching! Make sure to like & subscribe if you enjoyed :)

[@shagufta3247]

thanks so much please make videos on Django python full tutorial using visual studio

[@girishvenkatachalam8793]

Thanks man

[@mimididi8689]

Is there anyway I could import another random dataset into my trained model and see if he can predict me the category from the other database (the one I used to trained my model)

[@joneandrewharris8225]

can you help out with my error in the comments

[@alexvidu4517]

This is glorious, been searching for "learn tennis betting game" for a while now, and I think this has helped. Ever heard of - Aiyenjamin Prefatory Approach - (should be on google have a look ) ? It is a good one of a kind guide for discovering how to get a unique tennis betting formula minus the hard work. Ive heard some super things about it and my buddy got amazing results with it.

[@ManishSharma-xq9be]

He not only teaches the good stuff but also teach how to google things and get the job done. Keep going brother!. You are Awesome.

[@KeithGalli]

My goal is for you guys to be able to do this type of stuff on your own! Thanks for the support man, I appreciate it :)

[@shuhratjonzikiryaev9685]

Yes, I agree with you 100%. He is the only person I know on youtube that actually teaches the material so well! I hope to see this channel grow to millions of subscribers.

[@niteshprajapat7918]

yess exactly.... I was confused how to use stackoverflow...but after watching his real world problem tutorial.. I learnt this skill too

[@BennyHarassi]

Please keep uploading you're one of the best tutorial channels.

[@KeithGalli]

Thank you!! Will do my best

[@mucahitugurlu7324]

you're the reason that I've got an internship in a great company :) well.. I'm broke now :D but when I earn tons of money( I hope we all do :D ) I'll donate you Keith !

[@xyphoes345]

How are you doing now, man? Any updates?

[@joelrichmond6256]

Hey man how you doing now

[@KeithGalli]

Doing well now finally! :). Will be back on youtube very soon

[@uchechukwumazi6512]

A quick one for those into machine learning. On a scale of 1-10 how sufficiently enough does this tutorial cover machine learning. I am developing certain skills in data analytics and wanted to add Machine learning into the mix but don’t want to start diving too much into it. Just the necessary I will be need for a day in day out machine learning job requirements

[@uchechukwumazi6512]

A quick one for those into machine learning. On a scale of 1-10 how sufficiently enough does this tutorial cover machine learning. I am developing certain skills in data analytics and wanted to add Machine learning into the mix but don’t want to start diving too much into it. Just the necessary I will be need for a day in day out machine learning job requirements

[@jenn6997]

I like it when you showed us how you would use online resources, all the Googling and documentation stuff, so that we are not afraid to actually go online ourselves and explore more new functions :) Thanks Keith!! Stay healthy! :)

[@daniromsey187]

I think this is the best one I've seen, got me started and also you showed how a real project might look, googling things and reviewing errors, thank you!

[@deufrai1]

50 y.o. software developer here. this is the first hands on video I watch on the subject of ML. As a first step into the subject, I'm very sarisfied with the time I spent with you. You covered the basics, from data prep to model save and load. Surely a good starting point for further personal explorations. Also enjoying your Pandas related content Keep up the good work, and maybe use Jupyter's tab-completion, sometimes ;)

[@sunritjana4573]

I have been doing a lot of courses for ML in scikit, I found this last week, and learnt it. And to be honest, I mastered things, which they couldn't cover in the so-called "mega" courses. You're awesome and also really helpful!

[@rawmetal3052]

This guy is like the human version of W3school, his content is simple, succinct and well thought out

[@saptarshisanyal4869]

This one is just one heck of tutorial. Thanks a ton Keith. I am a Java Architect with 17 years of extensive experience, looking to shift to ML/Data Science. It took me 3 hours to cover this video. I must say first one hour was realy easy to follow but probably you covered a lot of things in the last 40 minutes.

[@FraserMyersMusic]

I was waiting for this! You sir, are a legend

[@stickmanjournal]

@wise guy I think discrete math would help you grasp this

[@Max-my6rk]

i always am being directed back and stay at Keith's video... just awesome...

[@lokeshnagarajan7495]

Amazing video. One won't find such tutorial on Python and Machine learning modules. It's the very video helped to complete my project.

[@KeithGalli]

Glad you liked it!

[@hollmanbaez1423]

You are so good, explaining the hardest things in common language and makes it easy to understand to even my grandma.... Thanks so much for making this simple!

[@dikshyantthapa3367]

You kept appearing on my thumbnail.. I didn't care at first.. Later for once i opened the data science video.. Man.. It was so useful. The application videos of machine learning, data science were awesome. Thanks Keith ❤️.

[@KeithGalli]

Well I'm happy that you ended up clicking on a video :). Also glad that you have found the videos useful. I appreciate the support!

[@kushsheth4801]

that moment of joy when i saw my model work! its like magic too good

[@mohitkishore8494]

This is by far the most useful tutorial that I have ever seen. You are an amazing teacher.

[@quickpresent8987]

I only have a basic knowledge about python and c# language, thanks for teach me a machine learning method !!! . Continue upload these kind of video pls , you are the best teaching channel

[@MrTaken-tl4bw]

In the first exercise if any of you feels like laughing a bit do this: if float(review['overall']) < 2: print(review['reviewText']+ ' ') Also, great video! Didn't know I could enjoy Data Science as much as I am.

[@haraldlons]

Just watched the video in one sitting. It was great! I learned so much, and I loved you showed the entire process from data to evaluation of model. Keep up the good work :)

[@KeithGalli]

Thank you! Glad it was helpful :)

[@kryskoss8410]

I learned more in these 2 hours than my professor taught in 2 weeks. Many thanks!

[@sarowarshouvo]

Hey keith,through this video just completed the first machine learning project.Thanks:).

[@KeithGalli]

Nice work! Your first of many to come 🤠

[@RobinHagg]

Yes. Been starting out with scikit and all videos are just so so. But your videos are always great

[@jenn6997]

Phew, finally finished watching this one:) A lot to take in, but super helpful and interesting! Thanks, Keith! :) Gonna start your real-world task with Pandas tomorrow!

[@AmandeepSingh-cv5qz]

keith ,you are like an elder brother teaching us how to do sums.thanksssssssssssssssssssssssssss a lottttttttttttttttttttttttttt bruhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh

[@alexq5516]

This video is super helpful! I have struggled in making my model using sklearning for several days and you just make my day! Thanks!

[@imadudin1522]

Big thank for you, Keith...!!! May be just requesting for pytorch tutorial.

[@aligh18]

Wow Keith, you're an absolute legend! I can't wait to get through your other videos and see your future work :D

[@Kris-to7vh]

Best channel ever to learn any Python library! 1:05 i wonder what the outcome will be for sarcasm, something like: 'beautiful restaurant that made me puke, raccomand'

[@asafrozali1353]

thanks man , i'm watching your whole data science video series and you are awesome!

[@gannoncondon1864]

Another great video. Really appreciate minimal slides paired with the 'live' coding feel.

[@somshridhar]

Really appreciate your efforts. I did not understand from my class teacher anything. Keith taught it very nicely. Thanks a lot

[@Locke19901]

Keith, this is incredibly helpful. Your teaching style is to be commended. I look forward to more like this for ML.

[@saikumargatla4706]

Your videos. Are changing my life

[@merkol8]

I have implemented my first ml model with the help of you please upload more content you are amazing well done !

[@sudhakar6933]

finally, found another one who's lessons are understandable

[@vijaykumar-od7kx]

Excellent tutorial to learn the fundamentals of SCI-Kit

[@gisleberge4363]

Real helpful, made me realise New possibilities on how to go agout text data - thanks 🙂

[@msc-clk]

I am like machines. I am always learning... Not watched but I believe you made your best.

[@msc-clk]

Edit: I just finished this tutorial and I still support my first comment. NOICE. You are real deal!

[@hsrayyar]

You look so young, but your ability is so good.Thanks for your explanation

[@BarryOGorman]

Great intro - and commitment to good programming

[@prashlovessamosa]

Your channel is heaven to me.

[@bboyhusky]

Great tutorials ! Sentiment.POSITIVE Jupyter-Tip: press esc + numbers 1,2,3 or 4 to create markdown header cells

[@azrulfyz1162]

Wow, that is one comprehensive tutorial. Thanks for the time and effort.

[@DataversePH]

This video is so underrated. Should have atleast 500K views.

[@yongsuhuang6195]

Almost exact 2 years later, a crappy student is watching ur video at 3am, trying to finish his assignment

[@safizaidi2787]

Keith man. This is an awsome video. Please make some more videos just like you did "Solving real world data science task" video.

[@liam4154]

Great video man! some of the best quality educating on youtube!

[@dhruvmk3055]

Great video, but I tested a few other algorithms on the data-set and they seemed to work even better on the data. The algorithms were: Nearest Centroid Classifier and Stochastic Gradient Descent. Thanks for the video though, really helped me.

[@johnhutchinson9445]

Thank you for this video! This saved me so much time digging through documentation to try to understand how to implement these libraries!

[@tedgq]

Letss goo! Didn't know I wanted this video until it was here

[@KeithGalli]

Love to hear it!!

[@drglover31]

KG Intelligence I appreciate your detailed videos on this platform

[@briannnnnnnnnn1037]

This is great! Looking forward to more ML content like regression, decision trees, SVM.

[@ninjaduck3534]

Dude you are an excellent educator, thank you so much for this well structured, well explained video!!

[@lisaw3829]

Watching the tutorial is kind of enjoyment! Have subscribed and waiting for more videos.

[@KeithGalli]

Glad to hear it! Thanks for the sub! :)

[@vilasjagtap6165]

Great stuff Keith. Really good. Keep doing your bit for all of us. Thanks a lot.

[@jamesriri1810]

@Keith Galli this is really dope. Totally love how how you teach the tutorial. Amazing stuff here.

[@DiegoSilva-dv9uf]

Valeu!

[@BM-vz2nb]

Very good and cool Tutorial Keith! Thanks a Ton! Loved it!

[@hectordavila6249]

Thank yoy man, you are awesome, I really appreciate your videos and how you go trough all the process step by step. Please keep uploading.

[@keihinata3740]

Thanks, Keith! I really like how you teach these stuff. Easy to understand and covers all necessary topics. Excellent tutorial. This comment might be considered a 'POSITIVE' sentiment in the model. 😆

[@ssharkwsk9439]

Great videos and series Keith, Kudos to you. Keep it going....

[@amankumarsingh6242]

Your videos are superb. I can see your videos and just get started applying it to my project. Thank you👍.

[@KeithGalli]

That's awesome! Glad you have enjoyed :)

[@haigangzhang8039]

Thanks a lot for the great video, I spent a few days to follow through, and learn a lot!

[@barbaracosta4183]

Thanks for the cool tutorial! Just a quick correction: when you're classifying using Naive Bayes, you used the Decision Tree Classifier, copied from the previous case. It's not super critical, but when I tried to use what I believed to be the corrected version, I found an unexpected error, and to use a working Naive Bayes, had to convert the train vector to a dense matrix using ".todense()". I'm not sure if this is correct though, if you have any input on this, it would be greatly appreciated! Thanks again :) How I tried to do it: clf_gnb = GaussianNB() clf_gnb.fit(train_x_vectors.todense(), train_y) clf_gnb.predict(test_x_vectors[0].todense())

[@WhiteError37]

thanks dude

[@choutos404]

Not working for me, I used .toarray() instead.

[@extremelyfunnyvideos9052]

I also had a problem with this. Where should we insert the fix? this : clf_gnb.predict(test_x_vectors[0].todense()) .. seems to late Anyone manage to get it working? Thanks

[@SasanMaleki]

Thank you!

[@timmdunker8507]

Thanks! :)

[@sandeepsharma-ph8id]

Awesome learning material. Thanks for making it.

[@carlosjacobfield-sierra3759]

This video was great man, keep it up your going places.

[@overgeared]

practical and nicely done. thanks! please do more videos on sklearn, maybe regression & clustering...

[@azrmuradl6420]

amazing! expecting more projects like this

[@nabilelbilali9569]

I think this way it's much clear in the Prep Data x=[t.text for t in reviews] y=[z.sentiment for z in reviews] x_train,y_train,x_teat,y_test=train_test_split(x,y,test_size=0.3)

[@tak68tak]

Sooo POSITIVE. You really saved me. Thanks a lot!

[@utkarshkapil]

Relevant and super helpful in 2024 too !

[@lfmtube]

Very good video! New subscriber and added to my “ Perfect videos” list. Thanks for sharing your knowledge.

[@kihunkim7498]

i miss your tutorial . good job !!!

[@KeithGalli]

Thank you!!

[@mahmoudaldeeb452]

keep going man ,you are the best

[@alexanderscott2456]

43:00 He's referring to Patrick Winston. By sheer chance I was watching one of his lectures on YT early this morning.

[@tobiasksr23]

Awesome. Are you planning making more of this Machine Learning Videos? It would be great if you could include more about the preprocessing part, maybe trying to get data from a source where it is not ordered and with lot of outliers.

[@maksimparnyakov6802]

Thank you very much for proper explanation. It’s got clear after your video

[@DJSEWWES]

big fan of what you are doing keep it up (y)

[@atharvapatil7549]

This was really helpful, I have been watching your videos since last few days. They are really aweseome. Subscribed. Can you please make a video for Face recognition using CNN or suggest me a link to watch.

[@bhoomimukadam8221]

@keithgalli even I want to learn face recognition using cnn plz make a video for that

[@vedantvraj]

Yes Please make a video on Face Recognition.

[@maherelouahabi6440]

Could you make a short video explaining what are the differences between deep learning, machine learning and AI from your point of view. Thank you and good luck

[@andyn6053]

learn to google man

[@richarddoggies3038]

It's a positive comment on your video about how it's cool. Thank you

[@constantineveres3731]

Hey Keith. Looks like the issue with relatively low score (~80%) is caused by imperfection of training data. I'm talking about conversion of Star rate to one of three classifiers: NEGATIVE, POSITIVE, NEUTRAL. The problem is that Stars are assigned by Customers but not Amazon AI Engine. People are treating say 3 star rate in very different way. Even if Customer is not really happy with product and giving fairly negative feedback, he/she still can provide 3 stars rate. So, while 5-4 stars rate is working well for POSITIVE as well as 2-1 stars - for NEGATIVE, there is a little bit uncertainty with 3 Stars rate. I think (5-4-3 stars for POSITIVE and 2-1 stars for NEGATIVE) or (5 stars for PERFECT, 4-3 for POSITIVE and 2-1 NEGATIVE) logic should give us 90-95% score. Thoughts?

[@KeithGalli]

Yeah you're very right with your thoughts. The meaning of the 3-star classification is pretty ambiguous and we can't reliably count on the data rated this way. Ultimately though the models that were being scored with ~80% were only classifying between NEGATIVE (1-2 star) & POSITIVE (4-5 star) so our model had more issues than just how we categorized the data. If we want to get that score up higher we will want to apply some additional processing to our text. Some ideas would be removing stop words (words like "the", "this", "that", etc), lemmatizing/stemming (converting words to a base form), and utilizing bigrams (pairs of words instead of single words). Another reason for a relatively low score is that our data is not perfect. Even some of the 5 star reviews probably have no meaningful information that conveys positive sentiment in their review text. Same goes for 1 star reviews. Potentially doing some manual review of our training data would be another way to improve the score. Hope this information is helpful! BTW, I'm a huge hockey fan and after noticing the hat in your profile picture I have to quickly say.... Go Bruins!!! ;)

[@dibyaranjansahu9971]

Great tutorial,loved it

[@leasrhythm473]

very nice video, so well explained for beginners! Thank you so much!

[@celina6204]

Loved this video! I followed along writing my own code and it helped me put what I've learned into practice. Thank you so much for the practical advice, I can't wait to start on my own projects. Liked and subscribed! :) p.s. Did anyone have issues getting the output from the GridSearch portion? That was the only part that messed up for me. My output: GridSearchCV(cv=5, estimator=SVC(), param_grid={'C': (1, 4, 8, 16, 32), 'kernel': ('linear', 'rbf')})

[@adarshparihar3300]

I was also having this problem, there was no result from GridSearchCV, hope you have got the solution for it.

[@uchechukwumazi6512]

Hello. Good day. A quick one for those into machine learning. On a scale of 1-10 how sufficiently enough does this tutorial cover machine learning. I am developing certain skills in data analytics and wanted to add Machine learning into the mix but don’t want to start diving too much into it. Just the necessary I will be need for a day in day out machine learning job requirements

[@bosorensen]

Wonderfully done! Thank you!

[@robingeorge7217]

Please upload a real world predictive model project

[@ishanpatel3086]

Perfectly done!!💯✨

[@KeithGalli]

Glad you thought so :)

[@Max-my6rk]

Keith Galli: "I'm going insane!" ahahah

[@adventurerwannabe]

that made me chuckle too lol

[@piotrb5161]

It's a good and hard work for... for us! Thank you Keith!

[@KeithGalli]

You're very welcome! Challenging yourself is the best way to learn :)

[@gregmaland5318]

Thank you! This was extremely helpful. (POSITIVE)

[@gianniprocida3332]

Thanks for the excellent tutorial!!

[@girishvenkatachalam8793]

Nice...shall watch full video now

[@robiparvez]

awesome stuff, bro subscribed 🙂

[@hemanthshankar4520]

i really like the way u explain

[@datastako156]

Great tutorials! a learned alot from you more powers!

[@michaelpritchard2350]

Keith has got it DOWN! Very instructive, thank you.

[@bryantjohnston8663]

Well done. Thanks for this!

[@resh8198]

Hello if your Gaussian naive Bayes keeps coming up with an error, try this: from sklearn.naive_bayes import GaussianNB clf_gnb = GaussianNB() clf_gnb.fit(train_x_vectors.todense(), train_y) clf_gnb.predict(test_x_vectors[0].todense())

[@rishabhsharma2190]

Resh Thank you for the solution

[@sreevlogger3847]

Really nice and easy to understand