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It's a very great tutorial ever found on youtube, Thanks a lot for sharing your valuable time and knowledge. It would be great if would have put all the practice datasets in the description.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Refer Naive Bayes Method. Time Stamp 4:22:24: The probability of a Purchase on a weekday P(B) = P(Weekday) has been given as 11/30. Weekday stats show: Probability of Buy as 9/24. Please explain how to arrive at 11/30 for probability of buy.
I m 31 now, I m a complete fresher in machine learning and in python, I was working as a supermarket billing guy for the past 8 years. Can I have a future in big companies if I study this??
100% Yes for sure.. But not easy.. Once u learn these technologies, U will understand what u need to learn more.. to get a job It will take atleast 1.5 years for your success.. I would suggest once u complete the basics, select a role that u want to achieve > search for jobs on Naukri with role like "data engineer" or "data scientist" etc> write down companies requirement > then start learning most frequent requirements So u will be confident for applying such jobs next time. All the best..
This is a great tutorial! Very easy to follow for beginners. Thank you for this! Could you please tell me how I can find the coefficient for the variable “State” in total? As now the variable has split into two and each of those has a separate coefficient.
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hi Sir, In Linear Regression at 54:00, we have 4 input label column but we are getting (large no. of regression coefficients) that is slope values. Why ? We should get only 4 slope coefficient value.
Sorry, you didn't get everything correct. You can check out the correct answers below: Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
This was very helpful. Well explained in detail and thanks for sharing the timelines as well. COuld you please provide me with the data set used in the tutorial.
Hello Ajith, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Hi Fazal, we are glad you love our videos. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@krishna2803 Hello Krishna, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hi Makinde, we are glad you found our video helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@RahulSharma-wz6yv Hi Rahul, thanks for watching our video. We have sent the requested dataset to your mail ID. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
Hi Bruce, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
I would like to thank the Simplilearn staff, especially Mr. Kennet Rajan for the datasets. Thank you very much and congratulations for the professionalism.
You almost got the answers correct. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Dear Sir, For KNN diabetes test below codes I modify and working. for column in range(5): #means = np.mean(dataset.iloc[:, column +1]) mean = int(dataset.iloc[:,column+1].mean(skipna=True)) #dataset.iloc[:, column+1].replace(0, np.NaN, inplace =True) dataset.iloc[:, column+1].replace(np.NaN, mean, inplace =True)
We are delighted to have been a part of your learning journey! If you want to continue honing your skills and keeping up-to-date with industry trends, check out our course offerings in the description box.
Hi, I hope everyone is safe and sound. I am new to machine learning. I have got some questions about Multicollinearity (Testing VIF Score). 1. When building a multiple linear regression model, should we check for multicollinearity? 2. What models do require to check for multicollinearity issue? 3. If there is multicollinearity issue, how can we eliminate it? 4. Is testing a VIF score for each feature a viable option to eliminate multicollinearity? 5. I have not watched the full video but will you teach multicollinearity handling? Thanks!
Nice video, tried to use it to explain ML to kids; however incorrect description of reinforcement learning. What you explained in the reinforcement learning part was supervised learning - when you have correct answers. In reinforcement learning, you don't have correct answers.
Make for us a video on how to make an API or an application using python and Sckit Learn library, Because we will not just be doing it in Jupiter notebook, Kindly make that video I will really appreciate
Hello Vaibhav, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Richard was great.. The way he taught Linear regression was superb even a person who doesn't have any knowledge of python can understand it. But Mohan is not a proper teacher. Infact first he should go with logistic regression and Sensitivity specificity accuracy threshold value but he doesn't covered that.. This session is only good becoz of Richard. Mohan you took a wide example for logistic first atleast clear us with binary logistic.. Sorry but not happy with Mohan's lecture.. And all the best Richard you are a gem. Now switching to some other machine learning course..
Thank you for your review. We are sorry to hear you had such a frustrating experience, but we really appreciate you bringing this issue to our attention
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
The explanation was good. Thanks a lot for sharing your valuable time and knowledge. It would be great if would have put all the practice datasets in the description.
Scenario 1: friends photo is the feature and it has the label that he is my friend so scenario 1 is supervised learning. Scenario 2: it has only feature with my past movie taste and does not has the label so it should be unsupervised learning. Scenario 3: analysed fraud transactions is the feature and flagging the the transactions is the label so it is supervised learning. Hope I am right please let me know if not, Thank you and your course is so far so good.
Hi, you almost got the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Can please also ask , for the k means example , you load the CHINA & FLOWER image , where are you actually taking does images from , m a bit confused because i wanted to compress my own image
Hello Harish, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello Sir, Thanks for giving such wonderful lectures on this topic! I have a doubt on one hot encoding in linear regression.....categorical_features = [3] is not working ...showing an error.....how can i rectify this?????????i tried with column transformer instead but output changed to different values ....
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Im an instrumentation engineer but im attracted towards Machine learning is it possible for the persons like me to become a Data scientist or Machine Learning engineer by hard work and gaining knowledge through work without a degree in computer science? Please reply.
"Yes, It is completely possible for a any engineers to become a Data Scientist. Data Science is a great field for Math and Stat enthusiasts. However, it would be a little hard during initial days due to lack of programming knowledge. The only thing would be to have a right approach, motivation and ready to learn whatever is required to become a data scientist To kickstart, you can check out the Data Science playlist for learning the basics: th-cam.com/video/X3paOmcrTjQ/w-d-xo.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training."
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hello Ganapathi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
The entire video is really great. But I had a doubt in interview questions section at time stamp 5:16:00 it's been said that when model gets higher accuracy in train data and less on test data that's over fitting which I think is not correct as per my understanding it should be the case with under fitting. And when model tries to judge each point correctly that is having high validation accuracy and less training accuracy that's the over fitting case.
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hello Shubham, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi Martin, thank you for watching our video and for the honest feedback. We will definitely look into this. Do subscribe, like and share to stay connected with us. Cheers :)
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
Very Nice video. Thanks, sharing this! Could you please put a link for the datsets used in the video? Would like to download them to practice and code along. (Y)
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hello Vinay, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
This is an awesome course. How can we get the data for the examples. It will be very useful if you share this data with me for learning the code better.
Hello Ganapathi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
hi.. i dont know if I'm doing the right thing. At 47:23, I got an error in this form: TypeError: __init__() got an unexpected keyword argument 'categorical_features'. which way around it?
hi , for the code where you predicting digits(logistic regression) , after importing the libraries you load the digits (1:14:33), i just wanted to understand something , where do you load these digits from coz you just type 'load_digits' but you do not put any directory where you taking these digits from ??
I am getting an error for below code please help me from sklearn.preprocessing import LabelEncoder,OneHotEncoder labelencoder=LabelEncoder() X[:, 3]= labelencoder.fit_transform(X[:, 3]) onehotencoder=OneHotEncoder(categorical_features=[3]) X=onehotencoder.fit_transform(X).toarray()
(47:22) from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder = LabelEncoder() X[:, 3] = labelencoder.fit_transform(X[:, 3]) onehotencoder = OneHotEncoder(categorical_features=[3]) X = onehotencoder.fit_transform(X).toarray() print(X) whenever I try to run this code, it shows a type error as show below: Type error: __init__() got an unexpected keyword argument 'categorical_features'.
How do I get jupyter to give me all the parameters for the RandomForestClassifier fit (i.e., all the input and default parameters). When I run clf.fit(train[features], y), I do not get the verbose output you get.
"Yes, It is completely possible for a Agricultural Engineer to become a Data Scientist. Data Science is a great field for Math and Stat enthusiasts. However, it would be a little hard during initial days due to lack of programming knowledge. The only thing would be to have a right approach, motivation and ready to learn whatever is required to become a data scientist To kickstart, you can check out the Data Science playlist for learning the basics: th-cam.com/video/X3paOmcrTjQ/w-d-xo.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training."
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🔥Purdue - Post Graduate Program in AI and Machine Learning - www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?GarcDY58&Comments&TH-cam
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - www.simplilearn.com/iitg-generative-ai-machine-learning-program?GarcDY58&Comments&TH-cam
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Dene waala jab bhi deta deta chhapar phad ke thankyou for such amazing course huge respect ✊🙏🏻🙏🏻🙏🏻
Simplilearn always provided us the best tutorials, great job, really love it.
Glad you like them!
It's a very great tutorial ever found on youtube, Thanks a lot for sharing your valuable time and knowledge. It would be great if would have put all the practice datasets in the description.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
@@SimplilearnOfficial hi team could you please send me the datasets used in this video as well? My email id is mou229@Gmail.com
@@SimplilearnOfficial l 🙏 LP see
@@SimplilearnOfficial mo
Refer Naive Bayes Method. Time Stamp 4:22:24: The probability of a Purchase on a weekday P(B) = P(Weekday) has been given as 11/30. Weekday stats show: Probability of Buy as 9/24. Please explain how to arrive at 11/30 for probability of buy.
I'm watching machine learning course on youtube is always recommend on my home
That's awesome!
I m 31 now, I m a complete fresher in machine learning and in python, I was working as a supermarket billing guy for the past 8 years. Can I have a future in big companies if I study this??
Oh Yes
You can
I started IT when I was 34
Now I am 39.
Please go ahead and learn all the way, the future is bright for you
Ya best of luck
Thanks for sharing your valuable experience.
Yeah.. Best of luck
100% Yes for sure..
But not easy..
Once u learn these technologies, U will understand what u need to learn more.. to get a job
It will take atleast 1.5 years for your success..
I would suggest once u complete the basics, select a role that u want to achieve > search for jobs on Naukri with role like "data engineer" or "data scientist" etc> write down companies requirement > then start learning most frequent requirements
So u will be confident for applying such jobs next time.
All the best..
This is a great tutorial! Very easy to follow for beginners. Thank you for this!
Could you please tell me how I can find the coefficient for the variable “State” in total? As now the variable has split into two and each of those has a separate coefficient.
does it mean that, knowing everything in this course, qualifies me as a machine learning expert? asking for a friend please .
I love this channel than edureka because of animated explaination
Hats off to your working
❤️❤️
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
Is it possible to get the dataset? I want to implement the codes by myself. Thank you in advance.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hi Sir,
In Linear Regression at 54:00, we have 4 input label column but we are getting (large no. of regression coefficients) that is slope values. Why ? We should get only 4 slope coefficient value.
Facebook - Supervised Learning
Netflix - Unsupervised Learning
Fraud detection - Supervised Learning
Sorry, you didn't get everything correct. You can check out the correct answers below:
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
right
Thanks for watching our video @Amjad
@@SimplilearnOfficial How her all answers are correct? Her last 2 answers are wrong based on your explanation.
Sorry! its our mistake.
Just started watching this video as a beginner with a little knowledge on python..but this seems amazing..
Glad it was helpful!
This was very helpful. Well explained in detail and thanks for sharing the timelines as well. COuld you please provide me with the data set used in the tutorial.
Hello Ajith, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial ajith172@homail.com
@@ajiththalachil THanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel and stay updated.
@@SimplilearnOfficial sir,can you please send the datasets for me too..here is my email id.
thamisbhasith8@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
TYSM for uploading this , Efforts appreciated , it was great learning the whole course :) .
Can you guys please send me .csv file of data sets ?
Hi Fazal, we are glad you love our videos. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
did they sent .csv?
@@nithishreddy2572, I also asked them many times, but I didn't receive any files. :'-(
@@krishna2803 Hello Krishna, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial please provide CSV files and required file need to learn ml
pandeyvivek203@gmail.com
Ppt was easy and impressive, and the course contents started from scratch and explained with sufficient examples thank you simplilearn
Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!
Hi, thanks for the tutorial, It is really helpful. Please could you send me the datasets used in this course. Thank you.
Hi Makinde, we are glad you found our video helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial hello sir, please send me also the dataset, my email is rahul.rameshwar.sharma@gmail.com
@@RahulSharma-wz6yv Hi Rahul, thanks for watching our video. We have sent the requested dataset to your mail ID. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
Hi please send the datasets to me too! bruce@cebilingual.com
Hi Bruce, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
7:18 Scenario 1 & 2: unsupervise, Scenario 3: supervised
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
I would like to thank the Simplilearn staff, especially Mr. Kennet Rajan for the datasets. Thank you very much and congratulations for the professionalism.
Many many thanks! Do subscribe to our channel and stay tuned for more.
@@SimplilearnOfficial tamalmajumder687@gmail.com , pls mail the csv
#Scenario 1 answer supervised learning, Scenario 2 unsupervised , Scenario 3 supervised learning questions at 7:00
You almost got the answers correct. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
At sometimes I didn't understand what they are telling .. Please add a captions to this tutorial . It is very much helpfull . Please consider this .
Dear Sir,
For KNN diabetes test below codes I modify and working.
for column in range(5):
#means = np.mean(dataset.iloc[:, column +1])
mean = int(dataset.iloc[:,column+1].mean(skipna=True))
#dataset.iloc[:, column+1].replace(0, np.NaN, inplace =True)
dataset.iloc[:, column+1].replace(np.NaN, mean, inplace =True)
Excellent tutorial and the best i have seen so far on internet. Thanks.😀😃
We are delighted to have been a part of your learning journey! If you want to continue honing your skills and keeping up-to-date with industry trends, check out our course offerings in the description box.
It's really a great.. I can't believe how to make the learning simple... Thank you.. expected more videos
Hi, I hope everyone is safe and sound. I am new to machine learning. I have got some questions about Multicollinearity (Testing VIF Score).
1. When building a multiple linear regression model, should we check for multicollinearity?
2. What models do require to check for multicollinearity issue?
3. If there is multicollinearity issue, how can we eliminate it?
4. Is testing a VIF score for each feature a viable option to eliminate multicollinearity?
5. I have not watched the full video but will you teach multicollinearity handling?
Thanks!
Nice video, tried to use it to explain ML to kids; however incorrect description of reinforcement learning. What you explained in the reinforcement learning part was supervised learning - when you have correct answers. In reinforcement learning, you don't have correct answers.
Hi Varvara, Thanks for the feedback. We shall share your concerns with the concerned department.
Make for us a video on how to make an API or an application using python and Sckit Learn library,
Because we will not just be doing it in Jupiter notebook,
Kindly make that video I will really appreciate
Will watch this soon.
Very grateful to Simplilearn. Thank you so much for sharing your knowledge with us.🙏
Hello thank you for watching our video .We are glad that we could help you in your learning !
Tutorial are amazing for a begginer.I request for dataset.
Hello Vaibhav, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Very Good Explanation. Need Dataset. It will be helpful
Richard was great.. The way he taught Linear regression was superb even a person who doesn't have any knowledge of python can understand it. But Mohan is not a proper teacher. Infact first he should go with logistic regression and Sensitivity specificity accuracy threshold value but he doesn't covered that.. This session is only good becoz of Richard. Mohan you took a wide example for logistic first atleast clear us with binary logistic.. Sorry but not happy with Mohan's lecture.. And all the best Richard you are a gem. Now switching to some other machine learning course..
Hi Mayank, Thanks for the feedback. We shall share your concerns with the concerned department.
@@SimplilearnOfficial Thanks.. All the best..
I wish the video should include subtitle because some intructors’ voices are hard to listen to
Thank you for your review. We are sorry to hear you had such a frustrating experience, but we really appreciate you bringing this issue to our attention
Superb explanation tqsm sir it's clarity and clear ..... ❤
You're most welcome
Its great I think you should publish a book about machine learning
Will you please again explain that how to find best fit line in linear regression ?
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
Will you please make video on python library used in machine learning
Can you please give notes for this video as well. It will be very helpful
The explanation was good. Thanks a lot for sharing your valuable time and knowledge. It would be great if would have put all the practice datasets in the description.
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Scenario 1: friends photo is the feature and it has the label that he is my friend so scenario 1 is supervised learning.
Scenario 2: it has only feature with my past movie taste and does not has the label so it should be unsupervised learning.
Scenario 3: analysed fraud transactions is the feature and flagging the the transactions is the label so it is supervised learning.
Hope I am right please let me know if not, Thank you and your course is so far so good.
Hi, you almost got the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Thanks a lot for this wonderful tutorial.
Sent!
This video is just awesome!!
Hello thank you for watching our video .We are glad that we could help you in your learning !
Can please also ask , for the k means example , you load the CHINA & FLOWER image , where are you actually taking does images from , m a bit confused because i wanted to compress my own image
U kept ur words.u made us understand simple.thank u.can I get datasets.
Hello Harish, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial gopal3jyoti@gmail.com
For linear regression
I didn't got l,can u send me again
@@SimplilearnOfficial yeah I got it, thank u
Thanks for an intuitive video, really enjoyed it. It would be great if you can send me the datasets that have been used in this course.
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Hello Sir,
Thanks for giving such wonderful lectures on this topic!
I have a doubt on one hot encoding in linear regression.....categorical_features = [3] is not working ...showing an error.....how can i rectify this?????????i tried with column transformer instead but output changed to different values ....
prereg's for this video: Intermediate python programmer, understand array's, bayesian probabilty and confusion matrix
Hi Sasha, this video can be viewed by beginners too. We have covered all the concepts from basics. Thanks.
Great Extplaination can i get your dataset to learn better?
Hi, thanks for watching our video. Please share your mail ID to receive the dataset. Thanks.
thank you so much.. this is valuable..
Very welcome!
Great tutorials ..! Loved it ... Please provide CSV datasets to get some hands-on experience ...
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
great explanation
Glad it was helpful!
very nice explanation.Thank you.
Hello #Simplilearn Will machine learning and Artificial Intelligence will lead to shortage of computer science engineer jobs in coming years?
Hi, there will be reduction in redundant jobs and there will be more options for new jobs which includes ML and AI tech. Thanks.
Thanks for amazing tutorial, I'm looking for. Really good explanation and concept. it will be good if you will send practice dataset
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Brilliant vedio ❤️❤️😍😍🙏🙏🙏🙏
Hello thank you for watching our video .We are glad that we could help you in your learning !
Hey, can you guys please provide with subtitles for people with hearing issues. It is way difficult for me.
Hey ,we'll go into these implementations in detail in another video. Stay tuned!
Marvellous tutorial
Thanks a lot!
Im an instrumentation engineer but im attracted towards Machine learning is it possible for the persons like me to become a Data scientist or Machine Learning engineer by hard work and gaining knowledge through work without a degree in computer science? Please reply.
"Yes, It is completely possible for a any engineers to become a Data Scientist.
Data Science is a great field for Math and Stat enthusiasts.
However, it would be a little hard during initial days due to lack of programming knowledge. The only thing would be to have a right approach, motivation and ready to learn whatever is required to become a data scientist
To kickstart, you can check out the Data Science playlist for learning the basics: th-cam.com/video/X3paOmcrTjQ/w-d-xo.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training."
Great tutorial! Very easy to follow. I learned a lot. Thanks a lot!!
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
1 Supervised
2 supervised
3 unsupervised
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hello it would be great opportunity to work on different algorithms on this dataset .Can you pls provide me the dataset
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Request you to please share a copy of the data sets for all the examples in this video.
Hello Ganapathi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
can you please provide the dataset for DecisionTree.Thank you
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Thank you for the great tutorial. would you please send me the dataset?
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial I have not received anything yet. my email is cemhfathi@gmail.com
Very good tutorial for beginners . I m impressed but plzz simplilearn let me know how I can have the same dataset that u have???
My mail id is priyanshig9170@gmail.com
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
The entire video is really great. But I had a doubt in interview questions section at time stamp 5:16:00 it's been said that when model gets higher accuracy in train data and less on test data that's over fitting which I think is not correct as per my understanding it should be the case with under fitting. And when model tries to judge each point correctly that is having high validation accuracy and less training accuracy that's the over fitting case.
No dear. Tutor is right.
I suggest you to explain code line what it does it just copy paste maybe this is some how confusion... I hope you understand
Thanks for the feedback!
1-supervised,2-unsupervised,3-unsupervised
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Amazing video. I need the dataset to practice. can someone help me out?
Hello Shubham, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
@@SimplilearnOfficial shubham.fulzele10@gmail.com
hey i have been waiting for it. can you send please ?
Nicely explained.. Possible to get the dataset and csv files ?
Hi Richa, the data set is available in the video description. Cheers! =]
hi, on the logistics regression tutorial, where are you getting the images dataset from? Thank you
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Very little explanations of some important code blocks especially for graphics in logistic regression and k-means clustering
Hi Martin, thank you for watching our video and for the honest feedback. We will definitely look into this. Do subscribe, like and share to stay connected with us. Cheers :)
Much informative❣️...will recommend your videos to our students also.
Awesome! Thank you!
Hi, good tutorial. Started learning linear regression. Can you please share the data set used in linear regression (companies dataset). Thanks
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Amazing lectures💥
Glad you like them!
Hello! thanks, but just please give us the subtitle.
thanks for this great tutorial.
Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial Done
Thanks for the great vid. could i get the datasets please? thanks
the legend says Simplilearn still replies to every comment posted on any of their videos
Hi, we always try to keep the engagement live with our viewers always. Thanks.
Hi . Is it possible to send the dataset . Want to give it a try by myself
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Error in linear regression It says in onehotencoder categorical_feature is unexpected
Very Nice video. Thanks, sharing this! Could you please put a link for the datsets used in the video? Would like to download them to practice and code along. (Y)
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
@@SimplilearnOfficial thanks, I would like to prefer to send it private or if I could inbox you. Thanks once again 😀
quiz =
supervised
supervised
unsupervised
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
Ur videos are clear but the audio is not good please maintain good audio
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
Thanks
Welcome!
Would have been great if algo were explained in sequence, I mean first all supervised then unsupervised and also some in class quiz
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Wonderful course!
Many thanks!
Hi, the video looks engaging, I need the dataset to continue Could you provide.
Hello Vinay, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Have you explained the topics by implementing them on data?
Yes!
This is an awesome course. How can we get the data for the examples. It will be very useful if you share this data with me for learning the code better.
Hello Ganapathi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
hi.. i dont know if I'm doing the right thing. At 47:23, I got an error in this form: TypeError: __init__() got an unexpected keyword argument 'categorical_features'. which way around it?
"Hi,
Please check this link to solve your query www.programmersought.com/article/14565313808/"
Great explanation
Please did you explain a book 📖
So, we can take that book as references too
Thanks for appreciating our work. But, we didn't explain a book!
hi , for the code where you predicting digits(logistic regression) , after importing the libraries you load the digits (1:14:33), i just wanted to understand something , where do you load these digits from coz you just type 'load_digits' but you do not put any directory where you taking these digits from ??
categorical_features comes with a Type Error in jupyter notebook. unexpected keyword
solution?
And may i know what platform you are using for python ?
I am getting an error for below code
please help me
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder=LabelEncoder()
X[:, 3]= labelencoder.fit_transform(X[:, 3])
onehotencoder=OneHotEncoder(categorical_features=[3])
X=onehotencoder.fit_transform(X).toarray()
This really great tutorial.
Glad you think so!
(47:22)
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:, 3] = labelencoder.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features=[3])
X = onehotencoder.fit_transform(X).toarray()
print(X)
whenever I try to run this code, it shows a type error as show below:
Type error: __init__() got an unexpected keyword argument 'categorical_features'.
If anyone has any idea how to correct this, please reply.
Python code you are showing is not clear. Make it a little more zoom
Thanks for the feedback!
How do I get jupyter to give me all the parameters for the RandomForestClassifier fit (i.e., all the input and default parameters). When I run clf.fit(train[features], y), I do not get the verbose output you get.
At 47:35 I am getting an error called unexpected keyword argument 'categorical_features' why? Any idea?
agriculture engineer can become a datascientist and what we are needed about informatique science
"Yes, It is completely possible for a Agricultural Engineer to become a Data Scientist.
Data Science is a great field for Math and Stat enthusiasts.
However, it would be a little hard during initial days due to lack of programming knowledge. The only thing would be to have a right approach, motivation and ready to learn whatever is required to become a data scientist
To kickstart, you can check out the Data Science playlist for learning the basics: th-cam.com/video/X3paOmcrTjQ/w-d-xo.html. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training."