Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn

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This Machine Learning Algorithms video will help you learn what is Machine Learning, various Machine Learning problems and algorithms, key Machine Learning algorithms with simple examples, and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest, and KNN algorithm.
Below topics are covered in this Machine Learning Algorithms Tutorial:
00:00 - 03:39 Machine Learning example and real-world applications
03:39 - 04:40 What is Machine Learning?
04:40 - 06:14 Processes involved in Machine Learning
06:14 - 09:40 Type of Machine Learning Algorithms
09:40 - 10:04 Popular Algorithms in Machine Learning
10:04 - 29:10 Linear regression
29:10 - 52:49 Logistic regression
52:49 - 01:04:45 Decision tree and Random forest
01:04:52 - 01:10:28 K nearest neighbor
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#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
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[@umavino]

Thanks for the excellent course. Very easy to understand. Possible to share the python code for me to try out.

[@mailrafat]

this is undoubtedly one of the best, precise and to the point tutorial videos on any learning site! I've tried many videos, but this has by far taught me the most! thanks so much!

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[@rickyleung2864]

Very nice! I'm taking a Machine Learning class on Coursrea and after watching this, I understand more of how to implement the algorithms. The class actually got me confused on how to apply it to daily tasks/projects. Now I have some projects in mind. :D

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[e-mail]

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[@webenggconsultancy7422]

@@SimplilearnOfficial Thank you very much for the video. Excellently presented! Please send me the salary csv file to [e-mail]. Thanks once again.

[@devrajdixit8978]

Best Explanation I have ever seen of this topic..!!!!!Can u plzz send me the dataset andother attachments to my mail id as soon as possible..MAIL ID [e-mail]

[@prasadkurle02]

This is a great tutorial! Could you please send me the study material , datasets and codes used in this tutorial ?? My Email ID : [e-mail]

[@SameerSK52]

As always, great quality lectures by SimpliLearn. Thank you!

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Hello thank you for watching our video .We are glad that we could help you in your learning !

[@nikhilshahare6241]

Thank you sir for providing such a comprehensive knowledge on ML algorithms. I would like to share that the portion covered in this video is what is asked in most of the data analysis ML interviews. this basics are the building blocks to grow upon.

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[@fatemehrahimzadeh5252]

Thank you so much, You mentioned to a very important point for training that I got everything about machine learning. Thanks again!

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[@ikechukwunigelogbuchi4031]

Very easily explained video on machine learning.

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I subscribed your channel for I like your explanation and your response for providing the dataset for each person separately and reading every comment & solving their doubt's.

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[@sudiptadey5343]

Very well explained . Very good video for beginners. I highly recommend . Thank you so much for explaining in detail. The pace is absolutely perfect.

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[@ojoozerubabelogom8498]

This is an excellent summary of ML. It is spot on with well planned walk through applications and process and how python is used

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[@sweetykhut7257]

in linear regression python example when visualising test data you used x_train for predict option . i think you should use x_test . can you explain this please?

[@deepwebtube]

Good explanation ...Thank you! was wondering, why was random state=0 never touched upon?

[@KeyszkielSensei]

this is so good lesson some other only teach the theory no coding but this one show the action of it with plenty of explanation

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Thank you sir.Your team is doing so good for the purpose of helping students.love & respect for simple learning team

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I believe in it one day we have the robots around us,which Can learn and do new opportunities for better solutions for All the part of technological Life.

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Yes, we are moving towards that direction already. So let's just sit back and wait for the future filled with Artificial Intelligence!

[@prashanthshetkar2350]

trust me im leaning ML for almost 6 months and nobody explained as clear as this one. thankzz

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Thank you very much for this informative video ! The content and pace is very nice :)

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I love your sessions. You are one of THE best.

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Thanks so much for explaining everything so clearly!

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Wonderful video. Thank you so much for creating this. I learnt a lot out of this. 🙏

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This is a lifesaver. I have learned so much, and it will help me get a ML job.

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Thank you very much sir for the enormous knowledge provided.

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[@pournimabharatkute5396]

This was a great video with really good explanations. Please share the datasets with me. Thankyou.

[@keerattanwar5892]

Really nice work and helpful! Thank You!

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[@aliburhanguncan6963]

Great explanations, thank you.

[@akhileshchauhan7422]

very clear and full content wideo ,thanks a lot sir

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Hey Akhilesh, thank you for watching our video. We are glad that you liked our video. If you agree that Machine Learning is a good career move, Please refer to this, www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course. Do subscribe and stay connected with us. Cheers :)

[@bushrabashir1669]

Well explained!

[@leadertheboss727]

Very nice... wonderful explanation.. thank you

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[@Drewxy22]

Thank you so much! Great Teaching & Explanation for such a great discipline. Thank you once again!

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[@nagendrabohora9110]

Very clear explanation of the whole topic with good examples...

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[@majuran]

Very good explanation... Thank you so much

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[@rekharaichal7405]

I am beginner , but able to understand your teaching so well Excellent way of teaching ,Thank you very much

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[@jyotikafaldu5495]

Excellent explanation by simplilearn...thank you sir..

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[@andrewtupua4039]

Love learning from you!

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Awesome! Thank you!

[@divyavinod6131]

very good explanation.thank you very much.

[@SumeetChawlaMusic]

Beautifully explained, 1 hr well spent.

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Glad it was helpful! Thanks for watching!

[@DharmendraKumar-sq2tv]

amazing summary video,thank for this type of tutorial.

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[@rovingupta4025]

Very nicely explained Sir! So Thanks. Regards

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[@apekshakapoor197]

Very well explained.

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[@mohankrishnapotnuru]

Thanks a lot for your valuable information

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[@zaheerahamedmulla-synergyl1875]

Very nice explanation of the machine learning. Thank you so much for the tutorial.

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[@dhaneshpt8641]

Very useful.. Thank you very much.

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Thanks for this video. It is such a great help for our study 😁

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[@michaelsujkowski6052]

Great video, Need data sets to practice

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[@deecm22]

This is fantastic!!!!

[@SimplilearnOfficial]

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[@prasanthkumar7328]

I have a small query in a linear regression where we are first plotting the points of the train data and test data using the linear regression object to verify if both we are testing for both why? we can do it for only testing right as we are calculating the error for only test data

[@SimplilearnOfficial]

Hi Prasanth, you need to plot the points first for training data and then after you build the model, you need to verify it with test data points. Hope that helps!

[@vinodkinoni4863]

very much thanks to share a great tutorials on ml ,

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HI Vinod, we appreciate the kind comment! Enjoy!

[@udaykumar-pw8ky]

Nice explanation

[@abhishekbhatt948]

superb.. explanation by mohan sir , Thanks .

[@SimplilearnOfficial]

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[@eto895]

Clear explaination..

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[@imaansarwar2314]

help please in linear regression i have been following same steps and rule but i am getting different values. I am new to it and unable to resolve it

[@jojomanesh]

Many thanks for putting this top-notch video up. If you could, please share the dataset utilized in the tutorial. :)

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

[@harshverm776]

Excellent Work...

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[@ramnarayan684]

Nice explaination ...thnx

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Most welcome

[@friends4323]

Thank you Simplilearn for the video. Please share the data sets used for projects.

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Hello Nik, 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.

[@santoshjoshi9077]

Very well explained, thank you very much. What is the difference between Machine learning regression vs. Statistical linear regression?

[@SimplilearnOfficial]

Hi Santhosh, thanks for watching our video. There is no separate Regression in Machine Learning and Statistics. The idea and the concept are all the same. Regression is actually a statistical concept that is being used in Machine Learning. Hope that helps!

[@qihanhuang3805]

Thank you so much for this wonderful online turtorial, I am a beginner to ML and this tutorial are so helpful. May I ask where can I get the data that has been used in this video for practice? Thank UUUU!

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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.

[@shawnjaswal4293]

Informative video 🤙🤙🤙

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[@ashishvishwakarma2732]

Sir, in logistic regression , when I am using from sklearn.crossvalidation import train_test_split , Then its showing me error that no module named sklearn.cross_validation. I am using python version 3 Please help.

[@SimplilearnOfficial]

Please try using "from sklearn.model_selection import train_test_split". This will work.

[@whytho9350]

Fir linear regression i used abs(error) instead of sqr(error) and worked just fine

[@SimplilearnOfficial]

That's great. There are always multiple ways to get things done.

[@MukeshArethia]

Awesome Mohan!

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Hi Mukesh, we appreciate the kind comment! enjoy!

[@Fdutchman]

TOO GOOD. THANKS

[@SimplilearnOfficial]

Most welcome 😊

[@monalimonalialwadkar135]

Very good.always wanted something like this. Plz post more on ML.

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[@AirMauritius239]

Very interesting & clear explanation - Video. I would be interested to have the data set. Grateful if you could share the dataset for each example explained? Thank you Sir. (Youvraj)

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[@sraghavendrak]

Nice explanation 👌👌

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Hi Raghavendra, we appreciate the kind comment! enjoy!

[@krzysztofkadowski5800]

Clear explanation!

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[@deepikasharma9517]

Really knowledgeable.. all algorithms are explained very well..Could you please provide the dataset and python code for these algorithms

[@SimplilearnOfficial]

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.

[@kamalshah875]

Very good explanation

[@SimplilearnOfficial]

Thanks and welcome

[@vmohakrish]

Hi Team,When I use the below code for generating visualization,it gives me 2 charts one with only 1 as value and other with 0.Please let me know if I am doing anything wrong.I have image of the error attached # Visualizing the test set results from matplotlib.colors import ListedColormap X_set,y_set = X_test,y_test X1, X2 = np.meshgrid(np.arange(start = X_set[:,0].min()-1,stop = X_set[:,0].max()+1,step = 0.01), np.arange(start = X_set[:,1].min()-1,stop = X_set[:,1].max()+1,step = 0.01)) plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha=0.75,cmap=ListedColormap(('red','green'))) plt.xlim(X1.min(),X1.max()) plt.ylim(X2.min(),X2.max()) for i,j in enumerate(np.unique(y_set)): plt.scatter(X_set[y_set == j,0],X_set[y_set==j,1], c=ListedColormap(('red','green'))(i),label=j) plt.title('Logistic regression(test set)') plt.xlabel('Age') plt.ylabel('Estimated Salary') plt.legend() plt.show() drive.google.com/file/d/1SYgj5sOGDjpjoc0OLP5Au6gflJ5ZrgxD/view?usp=sharing

[@Sirvaiya]

Good video. No unnecessary jokes or showoff.

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[@zeyadramadan8759]

Great lecture

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We're glad you think so!

[@tahamansoor599]

perfectly explained

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[@nilavasen8631]

Hello , this is an wonderful video. Can I have the PPT / PDF shown in this course? We can study then offline also. TY

[@enmarkhalis6731]

Thank you , but i did not understand the part during exctracting from 2 of legth 3 it tooke only two attribute but not the gender attribute male or fmale ? you answer is highly appreciated

[@SimplilearnOfficial]

The Gender variable in the dataset is not a significant predictor that can influence the output of the result. Hence, we dropped Gender as an independent variable.

[@amanshaikh7413]

Awesome 👍👍👍

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Thank you! Cheers!

[@shobhanbellamkonda8655]

For me, as a beginner, the tutorial is very helpful. Could you please share the Datasets used for all the algorithms. Thanks a lot in advance.

[@SimplilearnOfficial]

Hi Shobhan, thanks for checking out our tutorial. Please provide your email ID here in the comment section. Our team will send you the dataset as soon as possible.

[@assalaalsbahi3903]

thank you so much, it is very well explained, i would love if you shared with me the dataset

[@SimplilearnOfficial]

Hello Assala, 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.

[@josephndepuh8966]

Thanks so much if there was a way donate I will thanks for putting so much posting great content out there

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Much appreciated! Your thanks is all the donation we need! Thank you for watching!

[@harikrishnamalyala6214]

A small doubt at 15:36, for RMSE, shouldnt that be a square root of mean squared error instead of mean absolute error?

[@SimplilearnOfficial]

We are calculating the root mean squared error over passing mean_absolute_error inside np.sqrt function.

[@naimhkr]

Amazing Content ....

[@SimplilearnOfficial]

Thanks 🙂

[@Julieta.aussie]

excellent presentation

[@SimplilearnOfficial]

Thank you! Cheers!

[@deepthikatturi7987]

Good explanation sir

[@SimplilearnOfficial]

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.

[@rakeshraja1015]

Sir, Your lecture on machine learning is very nice and for depth knowledge on this topics please send the salary data set.

[@SimplilearnOfficial]

Hello Rakesh, 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.

[@vuteem]

I would be very grateful if I would get each data set of every program tested here in these videos. Thank you.

[@SimplilearnOfficial]

Hello Jahid, 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.

[@XainZ14]

Thanks for the informative lecture, I want to follow it using data set, how can I get that.

[@vijaykumar-yz6pg]

good Explanation

[@SimplilearnOfficial]

Thanks and welcome

[@placidekasoka7789]

Hi .do you have the continuation of this topic of ML for unsupervised algorithms ?

[@SimplilearnOfficial]

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!

[@souousable]

Good explanation

[@SimplilearnOfficial]

Thanks and welcome!

[@vmohakrish]

at 25:32 it should have been plt.plot(x_test,lr.predict(x_test),color = 'red') right? But x_train is considered.However chart looks almost same.Dont know how.

[@SimplilearnOfficial]

Hi Mohan!!! You must be aware our Simple Linear Regression model has been trained on the training set data (lr.fit(X_train, y_train)). We obtained a unique equation which is the simple linear equation for those set of training data points. Since our model is trained on the training set, whether we keep train set or test set data while visualizing the test set results, it won't make any difference. We'll obtain the same regression line. To learn more on Linear Regression, refer to the following video: kzbin.info/www/bejne/hIa7laeEZrxkfKc

[@rishabhshah8845]

awesome video 👍👍👍

[@SimplilearnOfficial]

Hey Rishabh, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@ashish18693]

Can you please provide the dataset? or link from where we can download them

[@SimplilearnOfficial]

Hi Ashish, thanks for watching our video. It would be great if you could share your email ID so that we can send the dataset immediately If you want your email ID to be kept hidden, we can do that as well. Thanks.

[@Allinone-ck2lc]

Helpful

[@SimplilearnOfficial]

Glad it helped

[@jamesrobisnon9165]

As far as I know, logistic regression does not need scaling and standardizing the data.

[@SimplilearnOfficial]

The aim of scaling and standardizing your independent variable is to rescale an original variable to have equal range or variance. This can be used for any machine learning algorithm.

[@floraceja1838]

Hello Sir! Thank you for this great tutorial! Can you send me the datasets for more practice please? Thank you in advance!

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

[@KN-tx7sd]

Sir, thank you excellent video to get started. Are the python codes used in this video available to run on Jupyter notebook? Would be of great use for practicing if they are made available.

[@imaansarwar2314]

following

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

[@bighnarajpanigrahi1005]

Very nice explanation..Plz share the dataset

[@SimplilearnOfficial]

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.

[@bethwellmwange3184]

pure greatness. l enjoyed the video. can you please send me the Logistic Regression dataset for practice

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.