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I have never seen any other video explaining the concepts of machine learning so clearly. Keep up the great work..!!

Finally i got perfect trainer for ML, your skills are excellence sir, we are very proud of you sir.

One of the few videos that clearly shows the training data that the model is attempting to fit to. Thank you.

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Best course you can get for learning ML is this only. Explanation is super awesome. Actually most of the books and courses shows you complex looking mathematical equations but this guy made all that easy for us.

Perfectly balanced video. It forces anyone to continue to watch other videos of this series. Very well explained in simple language. 👌

Why didn't you plot the sigmoid curve but only showed the scatter plot?

Perfect explanation with proper examples. Great job.

Sir, I know so surely that I can bank on your data science and python videos when I need to gain an in-depth understanding. Your content gives me the hope and clarity that I needed. God bless you and your undying passion to make such useful content for us. Thank you so much for all your hard-work sir!!! :)

For the first time after so many courses, videos, whitepapers, github, kaggle, exercises, wiki pages I am genuinely enjoying Machine Learning and I am doing all the coding and exercises by myself obviously after learning and understanding it all. Thanks a lot!!!

Actually, I fine tuned my model and was able to achieve an accuracy of 1.0. Thankyou so much sir. This might just be the best channel I have seen.🥳

You make people feel so welcomed to data field with your teaching skills. You are always the best.

i'm Not afraid to learn things with complicated term anymore! this teacher is the best at explanation.

bro you are best .. tried to swirl thru other online videos and then I end up watching your videos and I understand better .

I love your tutorials. They're perfectly paced, with right amount of context and explanation, great examples, and patient but efficient delivery. I hope you continue to produce more. Subscribed here and also Liked all of the videos I've found so far from you. Best.

On first attempt, i considered 'left' as dependent variable and everything else including salary and department as independent variable, got 77% score of accuracy. Thanks for the wonderful video.

78 percent accuracy. I do all your exercises but in this I learned a lot. Thank you sir for such a great series @codebasics

I never seen anyone explaining simple as like this. Others making complicated like maths intuition. Thanks code basics

You are the best teacher! I love the exercises at the end of each topic, which strengthens our understanding of what we learnt!!! Thank you so much! :)

Started learning machine learning on your youtube. Absolute Masterclass , you are my real teacher sir!!!

Thanks a lot for the lucid explanation. In the exercise, I got an accuracy of 77.2% in my model prediction.

Finally got the Python version of Andrew Ag's machine learning course. With a better explanation. thanks.

Dear Sir What a beautiful datasheet you have provided for practice with this video. Spent more than two days to play with it. Playing with the datasheet opened another dimension of the learning curve. Thank you very much for providing relevant exercises like this as a challenge!

I have no other words to say, the comments done by others have already conveyed my message to you!, Lots of love and thank you !

Many thanks this is the first explanation that provides context and examples making its so simple to understand.

Great explanation, I've understood everything, thanks!

I paused the video and commented, it's an excellent series that begins with ML.

Thank you very much for the videos on ML, AI, Python, etc. They help me learn a lot. Your explanations are clear and well understood. Thanks.

Thank You Sir, I have learned a lot from your vids :). I was really perplexed by Logistic Regression and I am glad KZfaq recommended this to me :)

This video is fantastic. I'm teaching myself machine learning and this was one of the most helpful resources I've found online. Excited to watch/work-through the rest of the videos! Thank you so much

amazing. astounding. bewildering. breathtaking. extraordinary. impressive. marvelous. miraculous. even all these adjectives are less to tell the quality of the video. Thanks a million.

Thnaks a lot for theese amazing contents. I have just discovered your videos!

I only could get 77 with logisitic, but then i used lazypredict to find a higher accuracy and then used desicion tree classifier to get a 98, ty!

Awesome as always, thanks for everything! i got a 77% model accuracy based on the satisfaction_level

it is one of the fantastic videos about Logistic Regression .. Many thanks

I love your series of videos as you are concerned with the student's learning! Thanks!

Thank you again! Great explanation! Always great tutorials!

I got 78.833 accuracy value. In this exercise i had known lot of things thank you bro.

Thank you so much, sir. I've got the score in the exercise 0.797. 🙂

If you could build a machine learning model for HR department, then you can remove HR department 😂😂

This video is really really good. Love the way you teach, your pacing and all the things you mentioned are really useful. Thank u and may god bless u!

Dhavel you are excellent in explaining difficult concepts in very simple language!

Thanks a lot for this i was able to implement logistic regression after so many tutorials

Another Great Tutorial, Thank you sir, Waiting for the next tutorial, keep up the good work

Thank you very much ! Your videos are always my best choice to learn ML

Hi, i found your course is truly enhancing the path towards Machine Learning concepts, kindly continue this and sir achieve a complete set of this machine learning course including all the kick start algorithms. Thanks Sparsh

0.84 accuracy score and 0.83 mean cross validation score. Thanks, Your tutorials have been helpful.

Sir, I extremely appreciate your videos and efforts in teaching these things. Very helpful and great explanation!!

God bless you and may He provide angles to solve all your problems. Thank you

one of the best explanation I've ever seen

Your way of teaching is very good. Thanks for the video ❤❤❤

thank you so much ! you have helped alot in learning the algorithms. Saved time with such a quick and easy way of explaining as I didn't have time for my fyp compleion and these videos are saving my time to get an idea of all algorihtms

Thanks a bunch, Subscribed here and also Liked all of the videos I've found so far from you. Best.

This video had good information, it was really helpful. I am still a learner, new to this field. I understand how to write and basics of confusion matrix using binary classification. But some terminologies are confusing. Can you please explain what exactly are base rate, test incidence, conditional incidence, classification incidence? That would be appreciated.

I'm reading books to try to understand some of this, and I'm impressed at the ease of the examples and explanations in your video. O_O gotta watch more

Thanks, sir .. your explanation is really clear and so easy to understand 👍🏼

Mathematics make sense with you now. Finally, I can understand many concepts that I failed to understand them in university. I would just like to point out that many duplicates exist in this data. I would also like to ask why didn't we apply Label encoding for this model?

thankyou for making videos, your content is great

Hello Sir! Great work by you. there is a problem in your code may be due to version of python .... If we use X_train, X_test, y_train, y_test = train_test_split(df[['age']],df[['bought_insurance']],train_size=0.9) then only we get score of 1 otherwise with your code it is 0.66.

Thank you so much for the graphical explanation...the concepts are crystal clear in my mind now.

Sir I tried this method, it is very easy to understand and use.. thank you sir

Thanks one of the best tutorials !

Perfect explanation!

Hey plz tell me what to do when I have multiple Columns like age ,weight , bmi that i need to consider for prediction

Your videos are awesome. I'm learning so much!

I like your tutorials very much, the explanation therein is superb and makes one understand even very hard to grasp concepts.

You are the best teacher

Thanks for a clear explanation. When I wanted to learn Logistics regression I checked a couple of youtube sessions and didn't get the concept well. But When I saw your session I quickly clicked with the confidence that the learning from your channel will be solid as usual. And it was!!! The way you explain together with the visuals are the key to a good understanding

I was doing the exercise and noticed something in the solution you provided. I think you need to drop one of the salary columns to avoid dummy variable trap. This was not done in the solution python notebook provided. Btw, excellent tutorial!!!

Why do we write x and y arguments in split method? Is it because of syntax?

just subscribed, your very good at explaining. thank you!

Very well done and explained even for beginners - thank you so much!

among several videos, this one is the best. appreciated

Exactly what I was looking for, Thank You!

Actually you are the best explainer

very useful video.... you explain everything in a very simple manner. Thank you

thank you sir for your video.

Reasons behind the employee retentions are: 1.Satisfaction Level 2.Average Monthly Hours 3.Promotion Last 5 Years

Very interesting and useful - well presented too

I have an interesting problem for you that I think you'll really enjoy sharing in a video. We would like to know your approach to solving the following problem: We have a soccer (euro football) game where there are penalty picks. We have data on who's the shooter/kicker and who's the goalie, and how many historically have been saved or not saved by the goalie as follows: (Assume the Shooter and Goalie letters are names of players and Saved is 0 (goal not saved, saved) Shooter Goalie Saved A Z 1 B Z 0 A Z 0 B Y 0 A Y 1 .... and so on for various rows Challenge: What we want to know is if shooter B is facing goalie Z, will the goal be saved or not? We want to leverage machine language (not just probability calculation, which can be done manually based on data). How should we solve this? Many thanks in advance! If you make a video on such, I'll even donate :) Promise!

Thanks. It is really informative.

Thank you sir for this amazing explanation of Logistic Regression.

Thankyou very much for all help and support. Can you please make a video on mathematical explanation of sigmoid and logit function also ?

Very well explained, thanks!

Good job. Thanks for teach me. In Portuguese, this content is not yet widely explored. 👍

clearly understandable explanation. Thank you so much.

thank u for the video as i m a teacher it helped me a lot

Sir Kindly confirm whether you already have a video for how to do Exploratory data analysis and feature selection. Thank you.

I have a question, how is the slope value updated for logistic regression when finding the best fit plane or line to seperate the 2 classes? At first I thought gradient descent but that doesn't work because it is used to find the global minima

Awesome explanation. I like this practical math and algorithmic explanation.

at 14:00 , why you used model.score(x_test, y_test), as you have to replace x_test with the prediction from the model to be like this: prediction = model.predict(x_test) model.score(prediction, y_test) then you can compare the prediction with the real value and so you can get the actual score

lol i just love the part on the exercise when he sayed you are a data scientist.

sir please start Deep learning tutorials :)

Nice Sir, try to create SVM or PCA next with some mathematical explanation. thank you

Perfect explanation on logistic regression. Loved it. Thanks a lot.

Very nice and you present easiest way to understand. Thank you

Very Clear Explanation

Sir, why didn't you drop one of the salary column after categorizing it using dummy variable in the solution ?