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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!
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.
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.
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.
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.
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'.
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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.
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.
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
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'.
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 ....
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: ru-vid.com and don't forget to hit the like button as well. Cheers!
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.
I would like to thank the Simplilearn staff, especially Mr. Kennet Rajan for the datasets. Thank you very much and congratulations for the professionalism.
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: ru-vid.com and don't forget to hit the like button as well. Cheers!
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.
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 :)
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 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.
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.
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.
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!
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, 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.
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)
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'."
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.
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!
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.
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.
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 ??
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
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.. 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?
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.
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!
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: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-X3paOmcrTjQ.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 Rzuji, This error occurs when you don't have a valid float data column. Check the following link that will help you solve your query: stackoverflow.com/questions/8420143/valueerror-could-not-convert-string-to-float-idstackoverflow.com/questions/8420143/valueerror-could-not-convert-string-to-float-id"
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: ru-vid.com and don't forget to hit the like button as well. Cheers!
The OneHotEncoder from the video is now deprecated, the functionality is preserved in such code: ``` from sklearn.compose import ColumnTransformer column_trans = ColumnTransformer([('City', OneHotEncoder(), [3])], remainder='passthrough') x = column_trans.fit_transform(x) ```
"Hi Sonam, The error arises if the features are not mapped in the right format. The following link will help you solve this issue: stackoverflow.com/questions/59476165/typeerror-init-got-an-unexpected-keyword-argument-categorical-features"
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()
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: ru-vid.com and don't forget to hit the like button as well. Cheers!
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, 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: ru-vid.com and don't forget to hit the like button as well. Cheers!
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'."
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..
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.
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.
(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'.
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.
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'."
