Summary:- 1. Understand the math behind algorithms 2. How these algorithms behave w.r.t. numerical and categorical variables ? a. Decision Tree uses different ways to split numerical and categorical predictors/variables. 3. How these algorithms works with Text Data ? a. Stemming and Lemmatization b. Bag of words c. TF-DIF d. Word2Vec 4. For which scenario these algorithms are used? a. Regression - Linear regression b. Classification - Logistic regression , Naive Bayes Classifier c. Both - Decision Tree 5. Over-fitting and Under-fitting Conditions :- a. Hyper parameter tuning b. Decision Tree Pruning 6. Impact of Algorithm w.r.t Imbalanced Datasets and how do you fix that? a. Binary Classification Problem b. Feature Scaling # Up-sampling # Down-sampling 7. Impact of Outliers, How to treat them? 8. For which Algorithm, feature Scaling/Normalization is required w.r.t Datasets:- a. DT, Random Forest, XGBoost, Gradient Boosting, ADABoost - Not required b. Linear Regression, Logistic Regression - Required.
Bhaijaan .... Aap aur siraj sir bhaut motivate karte ho ..... I am doing this cuz of you ... PLEASE keep making content like this ... And thanks a lot ......don't have words to express .... .
This is y I follow you. You have videos giving insights into such minute details when it comes to learning methodology. Its not only lessons or implementation . You talk other important stuff too.
6 months before I was totally new to machine learning background, but when I started seeing ur videos. I found myself under the shelter of one of the best ML tutor.... print(f"Thanks a lot sir ji for being my Tutor {accuracy}").....
Sir! First of all thank you so much for posting such contents. It's rare.. And second sir, I would like to request you to make a video on data pre-processing techniques.. It would be very helpful
thanks🙏 a lot sir, It is help me to solve the ML projects. By this videos every one get clarity about ML projects. I hope you and your family safe and doing well
Hi. your videos are a boon to the data science community. Please keep uploading more ! It helps us beginners to get good knowledge. I have a question, I wanted to learn maths behind machine learning algos, where can I find or get good in that math ?
There are good explanations by Krish on his channel... You can try those... Or you can also visit 3blue1brown and for stats you might want to visit StatQuest... I hope this helps...
Yes ...the practical experience part is unique on this channel .... We can get ml algo knowledge from anywhere on open source ...but what is actually happening in industry .. that experience is invaluable and definitely unique
In Summary, 1. Unerstand the math behind the ML algorithm. 2. How each ML technique work with Numerical, categorical and text data? 3. In Which Scenario does ML technique work either Classification or Regression ( & Multi-Class). 4. Overfitting & Underfitting conditions and hyperparameter tuning parameters for that particular ML algorithm. 5. How ML algorithm works with imbalanced Dataset. 6. Impact of Outliers on a ML algorithm. 7. Feature Scaling is required or not for that particular Dataset.Each and every does not required Feature Scaling. 8. Time complexity For each ML technique.
I have subscribed to ur channel. Ur contents are good no doubt about it.. but u being a content producer.. there are many topics videos .I suggest that u make ur videos in a indexed order so that the step by step ur saying about can be followed.
thanks Krish.I know all the algorithms but cant connect the dots.Lots of practice is needed.How is geeks for geeks website suitable for practice. Pls suggest.Thanks
Krish - Will you suggest to make a note while learning through online channels (like my favorite one is yours). If yes, can you please share yours way and some examples to show the most efficient way. Will wait for a video on it. Thanks as always for all your videos... keep helping us. :)
Hey bro... You can check out my notes... It might not be the best notes... But I tried to make it understandable... And if you end up liking the notes... You can make your own notes like that or you can simply use mine... You can find my notes on my channel #programmingcradle Link will be in the description of all the videos... I hope this helps... 😁
Hi Krish, I am from RPA background which in itself is a good career path but I want a transition toward Data Science field. I am little confused about it being right move or not. Can you please guide if its good choice to make or I stick to RPA platform?
You are telling about your experience it's good but for beginners it is difficult to understand, I have just started machine learning but most of the things bounced over my head what you are telling. Please make videos who don't know about programming and learning python so that he can understand.
Hi sir I want to learn python that's maps for MACHINE LEARNING please give me an advice, I know there are many web sites that provides knowledge on py but I want an expert advice to make the thing flexible for me. And I have some basic knowledge on py programming. Thank u in advance. Looking forward for your reply.
Sir, please do the regression model for Bigmart Sales data from 'Analytics Vidhya'. Because, I was getting negative predicted values for sales. btw great video
Sir, I graduated in Electronics and communication engineering in 2015, and since then working in a BPO.., and now I desperately want to switch and make a better career. Is data science a possible field for me? I have been reading and researching about this course and I am not sure ,I would be able to cope with syllabus.. Is it a suitable course? Please Guide...
Hello sir! In this video you mentioned that there are some algorithms which have inbuilt parameters to handle imbalanced data set. Can you please refer about those algorithms. I know only oversampling,undersampling ones. Thank you-
heyy please tell me how to install cufflinks in anaconda jyupter notebooks. I installed it but as i import it into my notebook it shows that no module named 'cufflinks' please help me. I really trying to solve this problem
Sir, I use to find difficulties in choosing the algorithm. I can't interpret which model should I use for a particular problem. Can you suggest a way of doing this?
When you first learn data science, you're not very good at telling which algorithms to apply so it's a good idea to try a variety of them and see which work better. With time, you'll get enough experience to tell which algorithms work better for specific cases. Even when you have plenty of experience, it's still a good idea to try several algorithms (start with simple ones for baseline) and choose the ones that perform best.
Data science covers such a large number of skills, I don't think there's one single book that covers the fundamentals. You'll have to try a combination of books when you start learning it. Just like, when you go to college for a certain degree, you don't take one class but a combination of classes. 😊 I can tell you from personal experience that data science is not easy to learn. If you look up a video about the general principles of data science, they feel intuitive enough to make data science seem easy. Once you start learning data science though, you'll realize how much work and time it takes to learn everything you need to know if you truly want to understand what you're doing and be very good at it. If you are the kind of person who learns better in a team, look into online bootcamps or online programs. Many of them seem to be reasonably priced. If you want to learn on your own, here are some suggestions for books to start with: 1. The Hundred-Page Machine Learning Book, by Andriy Burkov 2. Deep Learning by an Goodfellow, Yoshua Bengio and Aaron Courville 3. Introduction to Statistics by David Lane 4. An Introduction to Statistical Learning by Gareth JamesDaniela WittenTrevor HastieRobert Tibshirani (I struggle with this book because it is more advanced than I had expected and because the examples are in R not Python, but it's an excellent book so I definitely recommend it) You'll also need to learn Python. Try CZcams tutorials or online books like "Automate the boring stuff with Python." Most importantly, choose a project to work on. Even if you have no idea what you're doing, choose a project from a site like Kaggle and google every step of it. It will help you understand the theory you're learning, it will show you your weaknesses (since you'll get stuck a lot 😁), and it will give you the experience you need to understand how to solve types of problems (which algorithms, pipelines, and so on work better for specific types of problems.) Lastly, don't get discouraged. It takes A LOT of time to learn data science. Like I said, it seems easy when you first learn about it, but it's not. Don't give up. If you like solving problems, data science is like a super power that will help you become a great problem solver. Best wishes to you. 😊
Hello sir i am a computer science graduate and I'd like to go with machine learning And i would like to know how much maths is required to became a ML Data scientists
Hello Sir, I just wanted to know when will you be starting a new batch for ML, and please it should be scheduled in Saturday and Sunday. Because I am working in night shift in US Eastern Time zone. @krish Naik Sir
Hello Bagavathy... You can follow Krishs' channel he is having some awesome content... Also you can try dataquest you will learn a lot from their free content... And if you like to pay you can see that too... I personally joined Applied AI Course and so far I am loving it... And with that being said... you can use my notes that I have prepared... I won't say my notes are the best notes for data science 😂 but i guess they are understandable... You can find my notes on my channel #programmingcradle Link will be in the description of all the videos... I hope this helps...
Go for INeuron course if you want to land up working in Data science field. Udemy courses are good but they won't be able to give you practical real world feeling. Stop wasting time behind searching any courses. However if you can spend 25k you can also go for AppliedAI course there also you will get incredible theory explanation of machine learning techniques by Srikanth sir.
Summary:-
1. Understand the math behind algorithms
2. How these algorithms behave w.r.t. numerical and categorical variables ?
a. Decision Tree uses different ways to split numerical and categorical predictors/variables.
3. How these algorithms works with Text Data ?
a. Stemming and Lemmatization
b. Bag of words
c. TF-DIF
d. Word2Vec
4. For which scenario these algorithms are used?
a. Regression - Linear regression
b. Classification - Logistic regression , Naive Bayes Classifier
c. Both - Decision Tree
5. Over-fitting and Under-fitting Conditions :-
a. Hyper parameter tuning
b. Decision Tree Pruning
6. Impact of Algorithm w.r.t Imbalanced Datasets and how do you fix that?
a. Binary Classification Problem
b. Feature Scaling
# Up-sampling
# Down-sampling
7. Impact of Outliers, How to treat them?
8. For which Algorithm, feature Scaling/Normalization is required w.r.t Datasets:-
a. DT, Random Forest, XGBoost, Gradient Boosting, ADABoost - Not required
b. Linear Regression, Logistic Regression - Required.
Thanks
Wow
Thank you so much
Thanks man
Thanks man
You are truly changing the lives of 1000s of DS aspirants. Salute sir.
Now this is what you call a proper guidance..
Bhaijaan ....
Aap aur siraj sir bhaut motivate karte ho .....
I am doing this cuz of you ...
PLEASE keep making content like this ...
And thanks a lot ......don't have words to express .... .
This is y I follow you. You have videos giving insights into such minute details when it comes to learning methodology. Its not only lessons or implementation . You talk other important stuff too.
This is what we call as ‘Teaching by experience’ and Krish Sir you have proved it again..
One of the most efficient way to explain..
A big fan!
🙏🏻🙏🏻
6 months before I was totally new to machine learning background, but when I started seeing ur videos. I found myself under the shelter of one of the best ML tutor....
print(f"Thanks a lot sir ji for being my Tutor {accuracy}").....
How your process is going on?
Hello there krish I'm From Pakistan and I really like and impressed your way of teaching DS and ML.
Thanks.
As always wonderful video Krish... Loved it and it was really helpful... ☺️
sir good series of videos, keep up the good work, I am proud to call you as a mentor :)
Very Well Explained . Have seen so many video's with respect to ML but no1 has ever broken it down so easily. Cheers to Krish
Sir! First of all thank you so much for posting such contents. It's rare..
And second sir, I would like to request you to make a video on data pre-processing techniques.. It would be very helpful
Ah...big thanks.... I feel so so grateful that I came across your channel.
Big thanks for making our journey easier... 😊🙏🌄🌱
thanks🙏 a lot sir, It is help me to solve the ML projects.
By this videos every one get clarity about ML projects.
I hope you and your family safe and doing well
Hi. your videos are a boon to the data science community. Please keep uploading more ! It helps us beginners to get good knowledge.
I have a question, I wanted to learn maths behind machine learning algos, where can I find or get good in that math ?
There are good explanations by Krish on his channel... You can try those...
Or you can also visit 3blue1brown and for stats you might want to visit StatQuest...
I hope this helps...
One day you'll have millions of subscribers✌️
This is exactly what makes kris stand out from other tech youtuber. kudos to you my friend.
Yes ...the practical experience part is unique on this channel ....
We can get ml algo knowledge from anywhere on open source ...but what is actually happening in industry .. that experience is invaluable and definitely unique
What are your favorite sources to learn ML math? Would you recommend getting into the paper and then go-to medium blogs?
Thanks! Great video!
Yeah! I wanna know that too.
Great Sir👍
In Summary,
1. Unerstand the math behind the ML algorithm.
2. How each ML technique work with Numerical, categorical and text data?
3. In Which Scenario does ML technique work either Classification or Regression ( & Multi-Class).
4. Overfitting & Underfitting conditions and hyperparameter tuning parameters for that particular ML algorithm.
5. How ML algorithm works with imbalanced Dataset.
6. Impact of Outliers on a ML algorithm.
7. Feature Scaling is required or not for that particular Dataset.Each and every does not required Feature Scaling.
8. Time complexity For each ML technique.
Thanks a lot sir ji
I have subscribed to ur channel. Ur contents are good no doubt about it.. but u being a content producer.. there are many topics videos .I suggest that u make ur videos in a indexed order so that the step by step ur saying about can be followed.
Very usefull video tqs sir
thanks Krish.I know all the algorithms but cant connect the dots.Lots of practice is needed.How is geeks for geeks website suitable for practice. Pls suggest.Thanks
This video help mi lot thanks sir, Your a Good teacher
Logical, simple, and brilliant. Thank you, Krish!
Thank you, sir, for sharing, it's very helpful
Krish - Will you suggest to make a note while learning through online channels (like my favorite one is yours). If yes, can you please share yours way and some examples to show the most efficient way. Will wait for a video on it. Thanks as always for all your videos... keep helping us. :)
Hey bro... You can check out my notes... It might not be the best notes... But I tried to make it understandable...
And if you end up liking the notes... You can make your own notes like that or you can simply use mine...
You can find my notes on my channel #programmingcradle Link will be in the description of all the videos...
I hope this helps... 😁
Great, krish sir.
This was quite informative Krish!! Thank you.
Does your machine learning playlist cover everything needed in ML?
Great work btw 🔥
Thanks for the video
Hi Krish, I am from RPA background which in itself is a good career path but I want a transition toward Data Science field. I am little confused about it being right move or not. Can you please guide if its good choice to make or I stick to RPA platform?
Thank you krish for introducing this pattern.
I will try this pattern.
Thanks a million
Would you make a video on how you come up with this pattern ....how much time/experience it require for you to come up with this
Great
You are telling about your experience it's good but for beginners it is difficult to understand, I have just started machine learning but most of the things bounced over my head what you are telling. Please make videos who don't know about programming and learning python so that he can understand.
Thank you Krish Sir
Thanks a lot
Thankyou so much......100th comment!!
Hi Would you be able to conduct any live session taking any one algorithm as example.
thanks a lot for guidance ,sir..
love the content! new subscriber :-) stay safe!
Is this video for who learnt Machine Learning or for beginners? If for beginners then how they will know about the technical term you are using?
Krish please post time series problem.....no one is teaching good in regarding time series problem...
Hi krish.I am a beginner. So what is the best way to learn machine learning basics. Any recommendations for books and courses. TIA
Hi sir I want to learn python that's maps for MACHINE LEARNING please give me an advice, I know there are many web sites that provides knowledge on py but I want an expert advice to make the thing flexible for me. And I have some basic knowledge on py programming. Thank u in advance. Looking forward for your reply.
Your information is very helpful but it would be easier to understand if you made this video with visual data than just words.
sir can you train people for machine learning because there is no proper content for learning it
from online
How to connect with us .. Personally. And discuss with you in DS And ML.
Super sir
thanks so much for the video!! all the best
And will you please make a video on outlier detection and its treatment in a used case.
Thank you..
Sir, please do the regression model for Bigmart Sales data from 'Analytics Vidhya'. Because, I was getting negative predicted values for sales.
btw great video
Sir, I graduated in Electronics and communication engineering in 2015, and since then working in a BPO.., and now I desperately want to switch and make a better career. Is data science a possible field for me? I have been reading and researching about this course and I am not sure ,I would be able to cope with syllabus.. Is it a suitable course?
Please Guide...
Nice house mate
Sir can you make video on complete NLP
Sir, do project that cover all concept of feature engineering and feature scaling step by step
Hello sir!
In this video you mentioned that there are some algorithms which have inbuilt parameters to handle imbalanced data set.
Can you please refer about those algorithms.
I know only oversampling,undersampling ones.
Thank you-
SIR PLZZ CAN YOU MAKE ONE SHORT VIDEO WITH EXPLAINING ONE EXAMPLE
Hello sir it was nice lecture.. I have put few personal queries through LinkedIn message.. can you go through them and address those issues.. thanks..
Thank you sir
Hi Krish..... Can you please post a video for handling imbalanced dataset for multi class classification
heyy please tell me how to install cufflinks in anaconda jyupter notebooks.
I installed it but as i import it into my notebook it shows that no module named 'cufflinks'
please help me. I really trying to solve this problem
Will I be able to learn maths behind the algorithms and the parameters are used for regularization from O'Reilly's ML book??
Sir can you recommend any good book for machine learning with python?
Sir, I use to find difficulties in choosing the algorithm. I can't interpret which model should I use for a particular problem. Can you suggest a way of doing this?
I guess by experience we learn it.. initially trial and error
you look cute today mate! best from Amsterdam cozy west side
@krish how about understanding math behind algorithms on Coursera from Andrew Ng. Is it helpful.
As you explained how to learn ml so according to that how to learn by this technique through your ml playlist
Hii Krish...How to conclude which algorithm need to apply for a problem statement?
When you first learn data science, you're not very good at telling which algorithms to apply so it's a good idea to try a variety of them and see which work better. With time, you'll get enough experience to tell which algorithms work better for specific cases. Even when you have plenty of experience, it's still a good idea to try several algorithms (start with simple ones for baseline) and choose the ones that perform best.
@@BiancaAguglia that sounds great.
For a beginner can you suggest a book to get started learning in data science for a non cse student
Data science covers such a large number of skills, I don't think there's one single book that covers the fundamentals. You'll have to try a combination of books when you start learning it. Just like, when you go to college for a certain degree, you don't take one class but a combination of classes. 😊
I can tell you from personal experience that data science is not easy to learn. If you look up a video about the general principles of data science, they feel intuitive enough to make data science seem easy. Once you start learning data science though, you'll realize how much work and time it takes to learn everything you need to know if you truly want to understand what you're doing and be very good at it.
If you are the kind of person who learns better in a team, look into online bootcamps or online programs. Many of them seem to be reasonably priced.
If you want to learn on your own, here are some suggestions for books to start with:
1. The Hundred-Page Machine Learning Book, by Andriy Burkov
2. Deep Learning by an Goodfellow, Yoshua Bengio and Aaron Courville
3. Introduction to Statistics by David Lane
4. An Introduction to Statistical Learning by Gareth JamesDaniela WittenTrevor HastieRobert Tibshirani (I struggle with this book because it is more advanced than I had expected and because the examples are in R not Python, but it's an excellent book so I definitely recommend it)
You'll also need to learn Python. Try CZcams tutorials or online books like "Automate the boring stuff with Python."
Most importantly, choose a project to work on. Even if you have no idea what you're doing, choose a project from a site like Kaggle and google every step of it. It will help you understand the theory you're learning, it will show you your weaknesses (since you'll get stuck a lot 😁), and it will give you the experience you need to understand how to solve types of problems (which algorithms, pipelines, and so on work better for specific types of problems.)
Lastly, don't get discouraged. It takes A LOT of time to learn data science. Like I said, it seems easy when you first learn about it, but it's not. Don't give up. If you like solving problems, data science is like a super power that will help you become a great problem solver. Best wishes to you. 😊
@@BiancaAguglia thanks a lot your reply was really helpfull. @Bianca
Are you currently working in any corporate sir?
Hello sir i am a computer science graduate and I'd like to go with machine learning
And i would like to know how much maths is required to became a ML Data scientists
Thanks you sir 🙏🙏
thanks
Hello Sir, I just wanted to know when will you be starting a new batch for ML, and please it should be scheduled in Saturday and Sunday. Because I am working in night shift in US Eastern Time zone. @krish Naik Sir
Sure
@@krishnaik06 Thank you so much Sir
Which book or which course would suggest for learning?
Sir please do a video about how to refer and code from a IEEE papers.
Which algorithms are sufficient to learn for a fresher while applying for the machine learning engineer job?
Kindly connect our support team in the Skype, and clarify all your queries between 12pm to 12am, Monday to Friday, ID: support@ineuron.ai
Is math of machine learning covers in full stack data science program of ineuron?
Sir Why Decision tree, XGBoost, AdaBoost not required feature scaling and Normalization?
if you have time please explin it.
Kindly connect our support team in the Skype, and clarify all your queries between 12pm to 12am, Monday to Friday, ID: support@ineuron.ai
Sir, pls try to make shorter videos.
who won todays challenge in chatbot
Can 70% 30% be consider as imbalance dataset??
Yes it should be consider as an imbalance dataset
i haven't got my id and password for data science course. please look into this matter
Sir I don't know where I have to start learning the ML algorithms .
Will anyone suggest me any platform???
Hello Bagavathy...
You can follow Krishs' channel he is having some awesome content...
Also you can try dataquest you will learn a lot from their free content...
And if you like to pay you can see that too... I personally joined Applied AI Course and so far I am loving it...
And with that being said... you can use my notes that I have prepared... I won't say my notes are the best notes for data science 😂 but i guess they are understandable...
You can find my notes on my channel #programmingcradle
Link will be in the description of all the videos...
I hope this helps...
Needs materials
How to make data set in matlab?
Is google allowed in interview..!!😅
Yes , I have gone through some interviews where use of internet is totally allowed .
@@Bunny-zv4io wow...that's nice ..I never knew it's allowed..!!
1st view ♥️♥️
Anyone please tell me that is Udemy online course for data science is good or not.
Go for INeuron course if you want to land up working in Data science field. Udemy courses are good but they won't be able to give you practical real world feeling. Stop wasting time behind searching any courses. However if you can spend 25k you can also go for AppliedAI course there also you will get incredible theory explanation of machine learning techniques by Srikanth sir.
@@HarshKumar-zc4ox ok
@@divyanshumishra6739 Applied ai contents are just more than enough. However, if time permits one can go for ml a to z.