Build your first machine learning model in Python

In this video, you will learn how to build your first machine learning model in Python using the scikit-learn library.
🔗 Colab colab.research.google.com/
🔗 Code github.com/dataprofessor/firs...
🔗 GitHub github.com/dataprofessor
🔗 Blog towardsdatascience.com/how-to...
📖 DiscoverDataScience www.discoverdatascience.org
📖 www.discoverdatascience.org/a...
Time stamp
0:00 Introduction
0:15 Getting started with Google Colab
1:30 Load dataset
6:52 Split to X and y
8:30 Split data to train/test set
11:45 About DiscoverDataScience
13:00 Model building with Linear regression
21:55 Model building with Random forest
26:00 Model comparison
27:55 Data visualization
30:32 Conclusion
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#datascience #machinelearning #dataprofessor

[@Ts18762]

Amazing- I really liked how casually you would think to add colors, trend line and straight away go and add a few lines to reflect in output. Shows how comfortable you’re- for a beginner like me- that was so instructive when you speak out your thoughts on the go.

[@brittanybutterworth7994]

🐍My heart is pounding so hard! I can't believe it was that 'simple' to make a model!! It gives me so much confidence to start learning more and add more models to the code I've made

[@Samuel-ik5wp]

Stop SIMPING dude.

[@jaredheeralal2095]

​@@Samuel-ik5wp Stop hating

[@binarysaiyan9389]

Try making this model without scikit learn. Then you'll get the real taste of Machine learning

[@martgjepali5266]

@@binarysaiyan9389what’s the best practice with or without

[@nikhilrajput2652]

You literally give the brief on machine learning in a very simple and easy way

[@animelover5093]

I didn't have any prior knowledge of Data Science or Machine Learning, but as a visual learner, I finally understand the purpose of the mathematical equation y = f(X). Initially, after watching a couple of videos and starting from a math tutorial, I was confused about the relevance of math in this field. But now I see its importance, and I am grateful for this new understanding. Thanks @DataProfessor

[@shakirabdo638]

This is the clearest ML Tutorial I’ve ever watched ❤❤❤

[@ololololololoj5634]

This video is amazing! You explain things so clearly, and the quality is excellent.

[@ranahuzaifa147]

This was the easiest video on the ML model. Thanks Prof.

[@dsnewton7089]

After this tutorial, I can now start my ML Journey confidently. May God bless you Data Professor to continue doing this good work. Cheers

[@DataProfessor]

Thanks for the kind words :)

[@prashanthibandari8518]

Excellent video. You explained everything very simple. Which i learnt 1 month in classroom you just explained in few minutes. It is quick recap for me. Thank you..

[@mishanshah6827]

🐍This was my first video on ML and you made it really easy to follow through. I did this because I wanted to feel how it is like to do this , and I like it. I will definitely follow this through and study the actual maths behind it. Thank you for keeping me motivated.

[@ragus1416]

You made the process very simple for us! Great Video! Keep up the good Work!

[@adhithas571]

One of the best beginner videos available on YT😍

[@mhapich]

🐍 This was SO helpful. I feel like I had just been going through the motions in that process before, but you explained WHY each step is done. And I will definitely be using headings from now on to create that table of contents. Thanks, Data Professor, for another great video!

[@DataProfessor]

Glad it was helpful! Yes, using headings and sub-headings helps my future self to skim through the code fairly quickly :)

[@andrewdorsey7411]

🐍 Thanks for this. I appreciate you not just going straight into the core material, but talking about the organization with the headers.

[@bassmonkey70]

🐍Amazing depth covered in a short time-frame! Thank-you for compiling this introductory ML tutorial!

[@nironelokumannage1880]

This is the best machine learning tutorial that i've ever come across !❤ Simple, Clean and Informative ! Thank you sir ! 🙏

[@DataProfessor]

Thanks so much for the kind words :)

[@ranjandeep]

This is amazing Data Professor. Hope to see more of this stuff.

[@sumit5210]

The only video that I understood about ML. Thank you!

[@HerozTech]

this is literally the first machine learning that i have built looking to take it further Thanks for the tutorial 🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍

[@teresanicholls8531]

🐍 Your explanation was clear, concise, and appreciated! You are an amazing teacher who hits all the important points without adding any confusion. Thank you for your time in doing this!

[@DataProfessor]

Wow, thank you for the kind words! Glad to hear that the video is helpful!

[@MrIbadanBoy]

Awesome as always.. I'm applying this to my QSAR dataset in a bit.. Thanks for churning out great n useful content as always.. You d bestest Data Professor.

[@DataProfessor]

Glad it was helpful! And thanks for the kind words :)

[@joelr9924]

Thank you so much for this tutorial. Great to follow and to get an idea of how to actually build a model

[@yao_barna]

Hi! Great video. A quick question... when do we use linear regression as in statistics and when do we use it as a ML algorithm? Both try to find coefficients, both try to minimize the error (however it is defined) but I am still confused on what is the difference between the classical linear regression I learnt in basic algebra course and the ML procedure. Thanks!

[@Skywalker954]

Thanks a lot! Great tutorial, I love how you explain every step of it so it is easy to understand what you are doing.

[@trendsandmore911]

I usually don't spend time commenting on youtube, but dude this is a great video! Easy to follow alone, and very helpful. Thank you

[@DataProfessor]

Glad to hear, I appreciate that!

[@zegiyt4069]

TY you saved me for training our model for my Final Year Project

[@the-basit-ali-com]

I have been spending the past 4 months trying to actually do something tangible but haven't found anything practical like this one Just took an hour and half I belive and I am done with my first mach ine learning modle This has enhanced my confience and now I am going to build more Thankf Professor

[@ChanceMinus]

Brilliant! Extremely helpful... Thank you!

[@sharonrosekhokhololo5304]

The explanations are so easy to understand.

[@aashiqumr]

The best ML tutorial i just watched. Hat's off for your work sir. I really enjoyed and explanations were really great. Thanks again. You earned a subscriber here. keep up the great work sir.

[@DataProfessor]

You're very welcome!

[@chinonsomartins7308]

I really love this video very explicit and well thought !!❣❣

[@forestsunrise26]

Thank you so so much for this tutorial, your videos deserve much more views! Could you please also do a video explaining how to do k-cross validation as well as the methods to measure and compare the accuracy of ML models (confusion matrix, F1 score ect.)? Thank you so much for your videos I learn much more from you than my other profs at the university!

[@DataProfessor]

Sure thing! Thanks for the suggestion. Also glad the videos are helpful :)

[@schimidt99]

Amazing tutorial! Thank you very much!!!

[@_Wonder__]

This was awesome, thanks to your knowledge I feel I can have fun with experimenting with different algorithms and its parameters.🐍

[@tikkki25]

You're a very good teacher. Taught a complex topic so simply

[@DataProfessor]

Thanks for the kind words 😊

[@thanirmalai]

🐍Amazing intro to ml. It really felt easy to build my first ml model

[@AdamC3434]

🐍 this video got me through a few projects. Thank you!

[@mattlitchfield3776]

🐍 awesome video. This one was so much easier to follow along compared to other tutorials I have watch.

[@DataProfessor]

Glad it was helpful!

[@matijaxzivkovic8843]

Great video! I enjoyed a lot and learned a lot. Many thanks for that.

[@negusuworku1871]

This is super. Thank you so much. Keep it up the good work...

[@chaz8572]

Python really is a whole different beast.. Just to accomplish the task we used numpy, pandas, sklearn.model_selection, sklearn.linear_model, sklearn.metrics, sklearn.ensemble, and matplotlib. That's 7 libraries.. So overwhelming... Thanks for the guide. I did it locally within VSCode, so had to keep tweaking and installing libraries along the way, but it worked out well. The ML journey begins. Thanks for the training wheels!

[@naboulsikhalid7763]

before this video, Machine learning was sorcery and scary. Not anymore. Thank you for your neat and casual explanations that make me love the science and the math behind those functions. I am really looking forward to giving it a shot and learning more. thanks a lot. like and sub-earned

[@DataProfessor]

Glad to hear that this video was helpful!

[@AzureCz]

🐍 great video, simple and direct approach, simple models, simple problem. It'd be nice to have more of those simple and direct videos with simple classification and clustering problems, just to get a grasp on them :D

[@DataProfessor]

Great suggestion! More in the pipeline

[@AzureCz]

@@DataProfessor The "Titles" organization inside the notebook really helped to make it more fluid!

[@negusuworku2375]

WOW....very interesting and very helpful....Thank you

[@vichhaiyserey]

This is very informative, thanks!

[@Idontknowcode512]

Best video I have find on youtube. Have you created playlists related to the AI, ML, NN 🤔

[@pencillab6476]

Very well explained. Now i know where to start and what to do....

[@vinnythapz1860]

🐍wow!!thanks...sticking around you definitely will get me throught my internship with flying colours!

[@altangerelbayarsaikhan6386]

🐍 thank you for the great tutorial of the building machine learning model.

[@JonOxtoby]

🐍THanks for such an excellent introduction to running ML models in python - so clearly explained and super helpful tips on working within notebooks!

[@christopherramirez1843]

Excellent video to help understand the basic behind training new models. Just one suggestion, I missed a little more explanation on how the different parameters of the training algorithm would affect the end result.

[@utkarshghildyal6876]

🐍 Great way of explaining machine learning! Made it really easy to understand!

[@ikechukwuewuzie3836]

Great Tutoring from the Data Professor.

[@DataProfessor]

Glad you think so!

[@FrankieLaws]

Thank you, I love this video. I am new to ML. Very thorough in your thoughts and explanation. Question: What do you do with the prediction afterwards?

[@marinapaul__44]

Amazing explanation ,Thank you:)

[@zuodei4408]

🐍Thanks for this video. It will help me move forward in my final project in the university. Keep up the Good work. God bless you🙏.

[@johnotolorin6775]

good work; the presentation indeed helped

[@MichaelKigo]

amazing u have really helped me out

[@FRAMEDSKATEKREW69]

🐍 Finally a simple video, although I had a book for all this I think the pacing was too slow to start me off. Now I am more interested in know why and how some of this code work and for what reason. This is my process on being able to really understand and process information for coding lol Thanks for the video!

[@ajayf]

🐍appreciate the video, very clear and informative

[@janrehman9536]

🐍 Great content, please continue with your amazing effort. I have learnt so much from this video! Can you make more machine learning projects, I found this video super helpful!

[@gregz3040]

Very helpful, thank you!!!

[@RiyazBashaRK]

Hi ! Can you please mention in one line what exactly we found at the end I see it shows linear regression but what exactly does it depicts?

[@horanj.1022]

🐍 Great video Data Professor! I really liked this tutorial because you didn't miss explaining terms you used without making any assumptions

[@DataProfessor]

Thanks! 😃

[@negusuworku1871]

Thank you Prof. Very helpful

[@sphithak1221]

Thanks. Super helpful!

[@ShiftKoncepts]

already so much better than datascience 365 tutorials!

[@stackflow4007]

Great video ! After making the model can we deploy this to AWS? or do we need to reprogram in AWS again?

[@FiftyFiveCreams]

Great video man!

[@jeffreyboadu4452]

🐍Wow. This is my first time witnessing how models can be created. Great video as well.

[@LalBharat]

Thank you so much. this is really helpful

[@karthikponna2487]

it was great learning from you thank you

[@kasiviswanathanmadurai]

Really thanks a lot. Its very much useful

[@user-vl4nl5nm3z]

🐍 I'm doing a classification model for my thesis and your video has been really useful, thank you so much for the content. Could you make a video explaining the feature extraction? it'd be really helpful. Thank you again I love your videos.

[@fnulnu5645]

Simply awesome.

[@SuperPrasathkumar]

Great start👌

[@rahulthewild]

🐍 Love the video. Thank you so much! Wondering what should be my next step after this video?

[@natedaub]

🐍 I loved this. thanks so much.

[@matt-young]

🐍thank you! Really enjoyed the detailed steps and explanations.

[@LordAngryGopnik]

Amazing tutorial!!!!

[@nithyathimmaraju]

I think you should also define why random forest is used, what is the difference between linear regression and random forest. Why max_deoth is set to 2, and all the other details because these elements are primarily important for anyone to understand what is actually being done.

[@12MissFairy24]

you are amazing! thank you so much!

[@sonamsodani3272]

🐍thank you for posting this tutorial. It is helpful and easy to follow. 👍

[@ShrikrishnagopalSharma]

This was really nice content i have been struggling from where to start for my project and this video has just given me the way thanks @Data professor😇😇😇

[@DataProfessor]

Glad it was helpful!

[@jernejkrumpestar5765]

Great tutorial :D

[@AyunZoom]

Thank you, very helpful

[@MohamedHamdy-pk3nm]

Great job Thanks 🙏

[@hamadkhanofficial2298]

Great ❤video for me really helpful ❤

[@ddjazz]

Great video , I thought the comparison at the end , would show the actual values from the testset vs the predicted values for the Y , could you explain the difference?

[@blessingajayi9457]

Awesome 👏 thank you

[@hassanrashed6329]

Great tutorial

[@vincent8018]

Good job. Thank you for putting this online 🐍

[@bablo4867]

Hey! Great video!! I just have a problem : when I execute the Evaluate Model Performance part of the last model, it says that the name “y_rf_test_pred” isn’t defined, even though I did it the same as you. What should I do ??

[@jamontfleming9194]

🐍Very helpful video as I am starting my journey into training AI. Your instructions were very clear and easy to follow. Thanks for sharing!

[@pratikkhadka9151]

🐍Video was really helpful !

[@formosaboba]

🐍Really nice tutorial thank you!

[@benaiahenebeli9531]

I just had to subscribe to your channel. 👍

[@user-hu2dc1tt6b]

thank you very much ,Now I gain some confidence 🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍

[@thandothemisunderstood]

So, if we have experimental data already trained and tested, does this mean we can predict the solubility of the new molecule yet to be synthesized? Thanks for simplifying it.

[@metanulski]

The part I did not unterstand was the evaluation. For example. In the end we got a Training MSE fo Liniar regression of 1.007. Is the good or bad? Is the 1.028 form Random forest a better or worst result.