I'm also running an exclusive webinar on 28th Nov, 2023 for students who are serious about learning data science, consider going over here and reading more about it - topmate.io/ayush_singh13/649984

He is 10 years ahead from his age.

How can a person who is just 15 years old be so productive and intelligent? When I was in 10th grade at that age, I was still learning basic science. In my school, they taught us how to use paint and Microsoft Word in computer class. And I must say, your English is far better than mine.

As an indian you did a great contribution to our society for our youngsters We are proud on you❤️.

Even his personality and articulation are inviting to learn. Wonderful!

This is goldmine. I hope your paid course will be even more simplified version of this: Thank you.

Excellent Course, great job with the graphics and breaking things down! I really liked where you covered Regression, breaking down the Best Fit Line, slope, etc.. Some videos they just rush right through this stuff (assuming everyone already had exposure to this stuff in previous classes).

You are doing an awesome here for everyone Ayush Brother ✨ Thank you so much!

Deepest thanks Mr. Ayush Singh for your excellent training session! Keep up the great work...and more training please. With best regards!

Thanks a lot bro, learning a lot from you and getting motivated from you 😊

I just watched 30min and I can say u worked hard to deliver this content.... You earned yourself a subscriber.... My request is to start a series from scratch to become a Data analyst..... Or something where anyone can start and learn machine learning and AI....

I really applicate the hard work you put into this. Keep doing it. Everyone should take inspiration from you.

Thanks for including the EDA it was so helpful

learning is the conceptula understanding of a certain topic in such a manner that you will be able to explain it to others.

Your course has long hours that's the beauty of the course because it contains very very fundamental things which forms a great and strong base for ML.

Man almost 10 hour i am learning something else right now but will come back to this🔥

Hey Ayush...Thank You for the beautiful lectures on ML. Can you make more videos on projects of ML. So that we can get the practical understanding of ML.

Ayush you done a great work.Hope this will continue.eagerly ,waiting for another video.

Clicked to refresh my recommendations, will watch later. The contents seems fabulous

I would just say fantastic lecture. Hats off to you Ayush, the way you explain the things are awesome.

Thank u so so much for all the knowledge im a novice in data science and ai and llms .... i love to learn and master them hopefully with ur help

Thank you ayush for the detailed explanation

Oh this is the type of teaching I can easily follow. Nice examples

Good job, appreciate the effort to teach these concepts.

Thank you so much for giving quality content.

Oooo my god crazy dude you made such a big course thank you . U are far ahead from your age.

wow thanks man, really APPRECIATE. Was about buying a course from udemy till i saw this and as well detailed thanks.

Subscribed not in AI/ML but thanks for contributing to tech community... I want more 15year old kids of India like you

I was taught Maths and Physics by Indian teachers. There's just something great about how Indian people teach. Some of the nicest and best teachers in the world. Awesome course👍 Subscribed.

Appreciate your hard work for bringing everything for free . Thanks a Lot , It is really helpful for the people like me

you are a life saver!

Amazing work ayush

I can see lots of hard work on this content, well done 👍

You Earned A sub man❤...Really appreciate the content quality just wanted to start ml and not getting any idea thank you very much🙏

Very comprehensive video explanation for Regression on . Hopefully we have more video on such topics in future.

Keep it up young man!

Course is excellent even my brother from 10th std able to understand it and it''l be helpful if you provide notes. We can skim whenever we need to revise like while going to interviews or any exams in college and Hackathon as well. We can complete the learning with the notes.

Oh goodness !!! This course is literally mind blowing. Thanks a lot for this expensive knowledge.

Thanks brother.. Really a great content..

Wonderful Job Sir !!!

Thanks ayush ! This will help a lot ♥️

Really great content and your English is on another level bro

bhai can i just say i love your passion and you are a good human being serving the community

learning means to practice and understand the way something works or carried out

Hey Ayush, I'm a quarter way through your video and I'm amazed by the free value your providing! is there somewhere I can find the sources for your course?

Hey Ayush! Please make a video on how your day looks like?

Awesome ❤ impressive &!example to others

This is what we call real humanity kudos bro 🍻

Hey Ayush thank you so much for sharing such informative knowledge...can you just tell me what are things or concepts should we know prior to start learning machine learning..... especially for the ones who are new to tech background

I wa pleasantly surprised by the fact that some much-needed math were covered in the video!! Unfortunately, much of the hype (from youtube creators) just revolve around importing libraries from python!! In that sense... excellent effort in making the video !! Cheers

I hope this message finds you well. I'm a big fan of your KZfaq channel and have learned a lot from your videos. I was wondering if you would consider creating a video on providing a video channel or playlist that has made videos of math for machine learning from scratch. As someone who is interested in machine learning, I have found that having a strong foundation in math is essential. However, finding good resources that teach math from scratch specifically for machine learning can be challenging. I believe that your expertise and teaching style would make for an excellent resource on this topic. In particular, I would love to see videos that cover the basics of math like 2 + 2 = ? , all the way to more advanced topics such as linear algebra, calculus, and probability. I understand that creating high-quality content takes time and effort, but I truly believe that a video series on this topic would be extremely beneficial to the machine learning community. Thank you for considering my request, and I look forward to watching your future videos.

Great job big man.

🔥🔥🔥 finally wait is over for your teaching material

Great Job Younger Brother Respect from Pakistan

Awesome , thank you Bro for the tutorial ❤

We are proud of the knowledge you are providing to the world for free ❤❤❤❤❤. Lots of love kid

Thank you Ayush! You are doing a great job

god level man ,love you and your work

Wow.. Thank you so much..

your voice ❤❤ bro good vedio keep going 💯

I am starting ... Hope and end i will understand it finally/////

Personally I was looking for a machine learning course for a long time .. and most of them were too short with not such notes .. this course is absolutely amazing .. and providing the material for free is just awesome

superb! proud of you Ayush

I was watching Andrew Ng's videos but he skips a lot of basic stuff and assumes the student knows those simple basic things but I don't lol but this video is so very thorough and easy to understand thanks a ton man you saved me a lot of time and effort

Notes visuals are soo goood. Really appreciated. Thank you so much BRO.This is really great

Thank you😊 plz do cover all the machine learning concepts

Thank you for providing knowledge for free appreciate your hard work.

Inspirational for reason 🙌

Wow bro thank you so much !!!

thank you so much for giving us this course for free and in english bro

Thank you mate ❤. This is amazing 🎉

Thanks a lot ayush

ThankYou

Great work Create a full data science playlist with industry level project Waiting for this playlist

wow love from bangladesh thank you brother

very helpfull video bro thankq so much...

Thanks so much bhaiya

Thanks a lot ❤

Hats off to you

Please kindly make video on data analytics

amazing content and can i know how you got such kinda drawings in your notes?

so excited!!!🤩

amazing bro

Thank you needed that types of video. Keep working on those.

golden content

Really good content

00:00 Get a comprehensive machine learning course with detailed explanation and personalized feedback for free 05:48 Learning mechanisms involve past experiences and memorization approach. 17:09 Generalization helps rats and machines to make decisions based on similar things they have experienced before 22:36 Pigeons build an association between food delivery and certain actions, leading to meaningless learning conclusions. 34:30 Machine learning extracts patterns from data to make intelligent predictions 39:58 Scoping is the first step in building a machine learning system. 50:25 Supervised learning is like studying for an exam with the help of a supervisor 55:50 Supervised learning involves learning from labeled training data. 1:06:17 Machine learning is used to establish relationships between variables 1:11:42 Understanding linear vs. nonlinear data relationships 1:22:12 Best fit line is used to approximate values based on patterns in data. 1:27:21 Finding the best fit line depends on slope and y-intercept 1:37:46 Finding the best fit line for data using optimization algorithm 1:42:51 Numerical representation is necessary for accurate predictions in machine learning 1:53:16 Evaluate model performance by matching predicted values with correct values. 1:58:21 Mean squared error measures the performance of a model. 00:20 Algorithm in machine learning to study and summarize data 2:14:22 Latitude and skin cancer mortality rate have a negative relationship 2:25:15 Minimize J of M and B in hypothesis for cost reduction. 2:30:58 Beta0 represents the price of a house when size is zero. 2:42:04 For every additional hour of study, exam score increases by 5 2:47:49 For every increase in advertising expense, revenue increases by 0.8 dollars 2:58:03 Automatically try different beta values to get low error 3:03:22 Plotting x squared function gives a parabolic graph 3:14:08 Learn about learning rate and update rule for beta values 3:19:01 Learning rate variations and sample data for building function f 3:29:40 Calculate derivative of the given function 3:35:04 Using the update rule, calculate the MSE and updated beta values for beta0 and beta1 in each iteration 3:45:28 Understanding derivatives and gradient descent for parameter optimization 3:50:45 Design Matrix and Prediction in Supervised Learning 4:01:22 Learned about multiple linear regression and vectorized hypothesis function. 4:06:16 Multiple linear regression explained with an example. 4:17:03 Vectorization helps in faster computation of hypothesis and error calculation in multiple linear regression 4:22:20 Gradient descent algorithm for multiple linear regression 4:32:20 Evaluate machine learning model using mean square error and mean absolute error 4:37:50 Mean absolute error and root mean squared error are important in regression analysis. 4:48:02 R squared value of 0.9 indicates 90% of variation in dependent variable can be explained by independent feature X. 4:53:22 Hypothesis testing validates statements based on available data 5:03:39 There is a relationship between input variables and output variable 5:08:46 Understanding hypothesis testing in linear regression 02:28 Hypothesized value is assumed under null hypothesis and disprove it with t statistics 5:24:40 T-test helps to assess the significance of the coefficient relative to the uncertainty of our estimates. 00:15 The P value threshold of 0.05 is used to reject the null hypothesis. 5:40:41 Understanding degrees of freedom in regression 00:19 Visual inspection is a common way to test model assumptions 5:57:02 Linear regression assumptions and remedies 6:07:17 Residuals should not have patterns or trends, violating the independence assumption. 6:12:11 Homo scarcity assumption states that the variance of the residuals in a regression model is constant. 6:22:21 Normality assumption states that residuals should be normally distributed. 6:27:41 Linear regression and normality assumption 6:38:21 Data ingestion and processing before building linear regression model. 6:43:23 Fit method trains the linear regression model using gradient descent algorithm 6:53:26 Understanding Standard Error and Covariance in Regression 6:58:22 The statistical tests performed indicate that the model assumptions are valid. 7:08:45 Build a multivariate regression model to predict death rate for US countries with cancer diseases. 7:14:23 Virtual environment is important for package management and isolation. 00:32 Setting up the project and required libraries 7:30:37 Covered Exploratory Data Analysis (EDA) and pre-processing techniques 7:41:39 Divided data into four equal parts and found quartile numbers. 7:47:09 Identifying and removing outliers using Box Plot 7:57:11 Data exploration reveals missing values and categorical variables. 8:02:15 Techniques for dealing with missing values and analyzing internal variables 8:12:26 Learned about feature engineering and splitting and how to split columns in a data frame 8:17:25 The text explains a process of splitting intervals and converting them into lower and upper bounds to find the median. 8:28:08 One hot encoding is a technique to convert categorical variables into numeric for machine learning. 8:33:12 Prepare and analyze data set using one-hot encoding, drop and fill missing values, and deal with outliers 00:33 Data set is positively skewed with outliers present 8:49:21 Use Z score for outlier detection on normally distributed numerical columns. 8:59:07 Handling outliers in a data set 9:04:30 We use outlier detection techniques on columns based on their type of distribution. 9:14:35 Trimming and capping techniques used to remove outliers from data 9:19:31 Identifying highly correlated features among numeric columns in data using correlation matrix. 9:29:27 Add constant columns instead of removing them

Thank you❤

Amazing

Such a cool video

good stuff mate

Hi ayush , im a mechanical engineer and im getting into machine learning for my research thesis paper . Fromthis perspective , is it of utmost importance to learn all of linear algebra, calculus etc , OR I can just import libraries and continue with it? Do enlighten me as i dont know what is on the other side of this subject. Thanks

Hats off

Plz make a video lectures on machine learning on drug discovery.........

Hey, Whats your vs code theme and font? Great tutorial by the way!

This is amazing Ayush. Loving it

It is a big deal ❤

Thank u so much bro :p❤

This is really great

very visual appealing notes please provide the notes