Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial | Simplilearn
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This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression, K Means Clustering, Decision Tree, and Support Vector.
Dataset Link - drive.google.com/drive/folder...
Below topics are explained in this Machine Learning course for beginners:
0:00 Table of contents
01:46 Basics of Machine Learning
09:18 Why Machine Learning
13:25 What is Machine Learning
18:32 Types of Machine Learning
18:44 Supervised Learning
21:06 Reinforcement Learning
22:26 Supervised VS Unsupervised
23:38 Linear Regression
25:08 Introduction to Machine Learning
26:40 Application of Linear Regression
27:19 Understanding Linear Regression
28:00 Regression Equation
35:57 Multiple Linear Regression
55:45 Logistic Regression
56:04 What is Logistic Regression
59:35 What is Linear Regression
01:05:28 Comparing Linear & Logistic Regression
01:26:20 What is K-Means Clustering
01:38:00 How does K-Means Clustering work
02:15:15 What is Decision Tree
02:25:15 How does Decision Tree work
02:39:56 Random Forest Tutorial
02:41:52 Why Random Forest
02:43:21 What is Random Forest
02:52:02 How does Decision Tree work-
03:22:02 K-Nearest Neighbors Algorithm Tutorial
03:24:11 Why KNN
03:24:24 What is KNN
03:25:38 How do we choose 'K'
03:27:37 When do we use KNN
03:48:31 Applications of Support Vector Machine
03:48:55 Why Support Vector Machine
03:50:34 What Support Vector Machine
03:54:54 Advantages of Support Vector Machine
04:13:06 What is Naive Bayes
04:17:45 Where is Naive Bayes used
04:54:48 Top 10 Application of Machine Learning
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➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
✅ Key Features
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✅ Skills Covered
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- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
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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!
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 !
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
Machine Learning is the Future and yours can begin today. Comment below with your email to get our latest Machine Learning Career Guide. Let your journey begin.
Do not forget to answer the quiz at 06:50 . Here are the topics covered with the timelines:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
Do check out our Machine Learning Certification Training at www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course . Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!
Can you please share the 1000_Companies csv?
Hello Andre, 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.
I dont have knowledge about python. But i have knowledge in java with ds and ada concept are clear.. Can i start this course or should i start python and jump into this course?... Plzz help me.😳
Hi Bunty, it would be great if you can start learning Python coz it has an edge over Java in a lot of aspects. Java requires you to declare the data types of your variables before using them, while Python does not. Because it is statically typed, it expects its variables to be declared before they can be assigned values. Python is more flexible and can save you time and space when running scripts.
at (4:25:23) when A equals buy... you wrote P(weekday?buy)= = 2/6 it's wrong right ? it should be 9/24
It's really a great.. I can't believe how to make the learning simple... Thank you.. expected more videos
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
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.
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 .
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!
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!
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!
I'm watching machine learning course on youtube is always recommend on my home
That's awesome!
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 ....
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.
Hi, thanks for the tutorial. It is really helpful, I enjoyed watching. Now i would like to try it myself. Please could you send me the datasets used in this course? Thank you.
Hello, 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.
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
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.
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.
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.
1.supervised(Label)
2.unsupervised(Based on past data)
3.unsupervised(Based on past data)
Hi Ravi, Below are the right answers and explanation for the quiz.
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.
@@SimplilearnOfficial oh right
Glad you enjoyed our video!
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.
Just started watching this video as a beginner with a little knowledge on python..but this seems amazing..
Glad it was helpful!
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..
1- supervised
2- Unsupervised
3- supervised...
Pls leme know the answers if m wrong
Thanks this is an amazing video...😊
Hi Atul, Below are the right answers and explanation for the quiz.
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'
@@SimplilearnOfficial Hello first, I would like to thank you for this interesting video as well as for your answers. In fact, I find that your explanation for the second senario is not complete because, to the best of my knowledge, you must specify that it is a recommendation based on the content and not the collaborative recommendation in which we usually use clustering algorithms to group similar people together.
@@lilyabetit6354 Hi Lilya, thanks for appreciating our work. We will definitely share your feedback with our tech team. Thanks.
@@SimplilearnOfficial Hi thank you so much, i am excited and i am waiting the answer of your tech team !
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.
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
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!
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.
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.
Much informative❣️...will recommend your videos to our students also.
Awesome! Thank you!
Quiz#1:
1.Supervised
2.Supervised
3.Unsupervised
Is I am correct?
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'.
@@SimplilearnOfficial Awesome examples!
Thanks for appreciating our work. Cheers!
@@SimplilearnOfficial tnx for the answer
You are welcome!
Quiz answers:
1. FaceBook : Supervised
2. Netflix: Unsupervised
3. Fraud detection: Supervised
Hi Joel, Below are the right answers and explanation for the quiz.
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'
@@SimplilearnOfficial OO waoh Thank you. I really get it now. Question: how does the model get to know which activities are anomalous in scenario 3? Do you maybe simulate case scenarios over time? Else I feel there will be a few successful fraudulent activities before the model gets its bearings
Hello Joel, 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 :)
Thanks a lot for this wonderful tutorial.
Sent!
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.
Superb explanation tqsm sir it's clarity and clear ..... ❤
You're most welcome
very nice explanation.Thank you.
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.
thank you so much.. this is valuable..
Very welcome!
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 ??
What did happen at 3:22:08, Seemed it skipped some at end of Iris Flower Analysis and jumped to KNN.
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 .
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.
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)
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.
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
This video is just awesome!!
Hello thank you for watching our video .We are glad that we could help you in your learning !
categorical_features comes with a Type Error in jupyter notebook. unexpected keyword
solution?
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 😀
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
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!
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.
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!
Its great I think you should publish a book about machine learning
great explanation
Glad it was helpful!
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'."
Brilliant vedio ❤️❤️😍😍🙏🙏🙏🙏
Hello thank you for watching our video .We are glad that we could help you in your learning !
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.
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
At 47:35 I am getting an error called unexpected keyword argument 'categorical_features' why? Any idea?
Amazing lectures💥
Glad you like them!
Thanks for the great vid. could i get the datasets please? thanks
I have a problem: When I run the following line of code
logisticRegr.fit(X_train, y_train)
I get the following error:
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
Wonderful course!
Many thanks!
And may i know what platform you are using for python ?
Thanks
Welcome!
Marvellous tutorial
Thanks a lot!
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!
Will you please make video on python library used in machine learning
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.
Very helpful
Glad it helped!
Powerful Course, very well done, please may i get the data source, and the best and safe way to download Anaconda Jupiter Notebook, very much appreciated
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
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!
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.
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 :)
Hi, Thanks for the video. I will be glad if you kindly send me the data-set.
Hello Dilfaraz, 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.
Great content
Thank you so much!
Through this video course can I apply for the role of data science, the knowledge in this video is enough for an individual to give interview?
You can try but we prefer you take up our course and try for jobs: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
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!
superbbb
Thanks!
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!
(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.
Hello, thanks for this great tutorial. The best think is it is in a single tutorial consisting great content. I need the dataset. please send it on my email.
Hello Supriya, 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.
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
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
Helllo!
Thank you for the turorial. Can you please send me the datasets. Used in this videos.
muzzamil.ahmed0297@ggmail.com
muzzamil.ahmed0297@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!
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.
Hello it would be great opportunity to work on different algorithms on this dataset .Can you pls provide me the dataset
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/"
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.
Error in linear regression It says in onehotencoder categorical_feature is unexpected
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.
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..
Thanks for your great tutorial ! can you send me all data sets which are used in this tutorial ?
Hello Ramashish, 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 rpstat40@gmail.com
cn i get the dataset that is used in descision tree module
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.
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 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.
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.