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NerdML
Индия
Добавлен 24 апр 2020
Being Machine Learning professional introducing Machine Learning tutorials, going further than just the basics including mathematical derivations of algorithm, inner functionality of algorithm & coding practice on actual data-set. Learn about machine learning, data analysis, data pre-processing and more.
If you cannot find something, just ask!
Happy Leaning & stay safe!!!
If you cannot find something, just ask!
Happy Leaning & stay safe!!!
Roadmap for Data Scientist | How I Would Learn Data Science Smartly(If I Had to Start Over) | NerdML
In this video, I talk about how I would learn data science if I had to start again from scratch. Learning data science and machine learning is a little bit different for everyone, but hopefully this video will help you to build the necessary foundation to tackle these fields.
Complete data science roadmap: Do you want to learn data science step by step starting from very beginning? Are you the person who doesn't have any technical or programming background and want to join every growing data science industry? Are you a mechanical engineer or a bachelor of commerce graduate and have doubts about what the learning path would look like for you? If you are one of these then this video is for ...
Complete data science roadmap: Do you want to learn data science step by step starting from very beginning? Are you the person who doesn't have any technical or programming background and want to join every growing data science industry? Are you a mechanical engineer or a bachelor of commerce graduate and have doubts about what the learning path would look like for you? If you are one of these then this video is for ...
Просмотров: 5 493
Видео
Understanding Intuition of Attention Models in Neural Networks | Attention Is All You Need | NerdML
Просмотров 1 тыс.3 года назад
In this video, we discuss Basic Intuition of Attention Models in neural networks. We go through the architecture with examples. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple networ...
A friendly introduction to Convolutional Neural Networks (CNN) & Image Recognition explained| NerdML
Просмотров 3,8 тыс.3 года назад
In this video, we explain the concept of Convolutional Neural Networks, how they're used, and how they work on a technical level. We also discuss the details behind convolutional layers and filters. A friendly explanation of how computer recognize images, based on Convolutional Neural Networks. All the math required is knowing how to add and subtract 1's. (Bonus if you know calculus, but not ne...
Vanishing/Exploding Gradients - An Old Problem results from backpropagation (Deep Learning) | NerdML
Просмотров 1,7 тыс.3 года назад
In this video we will understand what Vanishing Gradients & Exploding Gradients are & the problems they cause during training. How can we fix the vanishing gradient problem & exploding gradient problem with your network. If deep neural networks are so powerful, why aren’t they used more often? The reason is that they are very difficult to train due to an issue known as the vanishing gradient & ...
What is Forward Propagation & backpropagation calculus really doing in Deep learning? | NerdML
Просмотров 1,8 тыс.3 года назад
In this video we will understand What is Forward Propagation & backpropagation calculus really doing in Deep learning. The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walk through here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backprop...
Activation Functions in a Neural Network | Sigmoid,Tanh,ReLU,Leaky ReLU,Softmax Functions | NerdML
Просмотров 6 тыс.3 года назад
This is the video of "Activation Functions in a Neural Network explained". In this video we will cover the Sigmoid Tanh ReLU Leaky ReLU Softmax Activation Functions. In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be...
Neural Network In 5 Minutes | What is a Neural Network? | How Neural Networks Work | NerdML
Просмотров 8093 года назад
This video on "What is a Neural Network" delivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the different parameters used in a Neural Network such as weights, bias and activation functions. We will also understand how to train a N...
AI vs Machine Learning vs Deep Learning | AI VS ML VS DL | NerdML
Просмотров 3,3 тыс.3 года назад
This AI vs Machine Learning vs Deep Learning video talks about the differences and relationship between AL ML and DL. The tutorial video will also cover what AI VS ML VS DL entail how they work with the help of examples and whether they really are all that different. This AI vs Machine Learning vs Deep Learning video will explain the topics listed below: Start (0:00) 1. What is Artificial Intel...
DBSCAN (Density Based Spatial Clustering of Applications with Noise) Clustering | NerdML
Просмотров 7513 года назад
The DBSCAN (Density-based spatial clustering of applications with noise) algorithm is based on this intuitive notion of “clusters” and “noise”. The key idea is that for each point of a cluster, the neighborhood of a given radius has to contain at least a minimum number of points. Below topics are explained in this video: 1). Basic understanding of DBSCAN (00:08) 2). Epsilon & minimum points exp...
Hierarchical Clustering | NerdML
Просмотров 1 тыс.4 года назад
This video will help you to understand how we can make use of Hierarchical Clustering algorithm for solving unsupervised learning problem. We will mathematically solve the problem. We will understand what is Hierarchical Cluster, what is Agglomerative approach, dendogram and how can we apply all these in Clustering algorithm. Do subscribe to my channel and hit the bell icon to never miss an upd...
K-means clustering algorithm with solve example: how it works | NerdML
Просмотров 27 тыс.4 года назад
This video will help you to understand how we can make use of K-Means Clustering algorithm for solving unsupervised learning problem. We will mathematically solve the problem. We will understand what is K-Means Cluster, what is Euclidean Distance & centroid and how can we apply all these in Clustering algorithm. The K-means algorithm starts by placing K points (centroids) at random locations in...
Random Forests - Building, Using and Evaluating | Fun and Easy Machine Learning | NerdML
Просмотров 8804 года назад
Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don't have the same problems with accuracy. In this video, I walk you through the steps to build, use and evaluate a random forest. Want to learn why Random Forests are one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning? What this video ...
Decision Tree for Regression Part 3 | NerdML
Просмотров 9 тыс.4 года назад
This video will help you to understand how we can make use of standard deviation for building Decision Tree algorithm for Regression problem. We will mathematically solve the regression problem. I have divided Decision Tree tutorial into several parts which will cover basic intuition, Classification problem solving & regression problem solving. Do subscribe to my channel and hit the bell icon t...
Decision Tree Classifier with Decision Tree Analysis & Example: how it works |Part -2| NerdML
Просмотров 4,9 тыс.4 года назад
This "Decision Tree Classifier with Decision Tree Analysis & Example" video will help you to understand how can we make use of Entropy, Information Gain & Gini Impurity for building Decision Tree algorithm. We will mathematically solve the classification problem. I have divided Decision Tree tutorial into several parts which will cover basic intuition, Classification problem solving & regressio...
Decision Tree (Basic Intuition - Entropy, Gini Impurity & Information Gain) | NerdML
Просмотров 50 тыс.4 года назад
Decision Tree (Basic Intuition - Entropy, Gini Impurity & Information Gain) | NerdML
Support Vector Machine Kernel Trick (Part - 4) | NerdML
Просмотров 1,1 тыс.4 года назад
Support Vector Machine Kernel Trick (Part - 4) | NerdML
Support Vector Machine Quadratic Optimizer(Primal & Dual Optimizer) (Part - 3) | NerdML
Просмотров 1,7 тыс.4 года назад
Support Vector Machine Quadratic Optimizer(Primal & Dual Optimizer) (Part - 3) | NerdML
Support Vector Machine Mathematics Intuition - hyperplane, margin (Part - 2) | NerdML
Просмотров 8 тыс.4 года назад
Support Vector Machine Mathematics Intuition - hyperplane, margin (Part - 2) | NerdML
Learning: How Support Vector Machines (SVM) algorithm works - Basic Intuition (Part -1) | NerdML
Просмотров 1,7 тыс.4 года назад
Learning: How Support Vector Machines (SVM) algorithm works - Basic Intuition (Part -1) | NerdML
How K Nearest Neighbors Classifier Algorithm works | KNN Algorithm Using Python | NerdML
Просмотров 9364 года назад
How K Nearest Neighbors Classifier Algorithm works | KNN Algorithm Using Python | NerdML
Naïve Bayes Classifier, Clearly Explained!!! - Fun and Easy Machine Learning | NerdML
Просмотров 9594 года назад
Naïve Bayes Classifier, Clearly Explained!!! - Fun and Easy Machine Learning | NerdML
An Introduction to Logistic Regression - Maths Intuition | NerdML
Просмотров 1,1 тыс.4 года назад
An Introduction to Logistic Regression - Maths Intuition | NerdML
Machine Learning Fundamentals: Bias and Variance | Overfitting and Underfitting Explained | NerdML
Просмотров 9 тыс.4 года назад
Machine Learning Fundamentals: Bias and Variance | Overfitting and Underfitting Explained | NerdML
Multiple Linear Regression & Polynomial Regression Model Example, The Very Basics | NerdML
Просмотров 1,9 тыс.4 года назад
Multiple Linear Regression & Polynomial Regression Model Example, The Very Basics | NerdML
Introduction to Bayesian Linear Regression statistics | NerdML
Просмотров 6 тыс.4 года назад
Introduction to Bayesian Linear Regression statistics | NerdML
Machine Learning - The Mathematics of Machine Learning | NerdML
Просмотров 1,8 тыс.4 года назад
Machine Learning - The Mathematics of Machine Learning | NerdML
An Introduction to Linear Regression Analysis, The Very Basics - Coding Least Square Error | NerdML
Просмотров 1,1 тыс.4 года назад
An Introduction to Linear Regression Analysis, The Very Basics - Coding Least Square Error | NerdML
An Introduction to Simple Linear Regression Analysis - Gradient Descent | NerdML
Просмотров 9214 года назад
An Introduction to Simple Linear Regression Analysis - Gradient Descent | NerdML
Introduction To Machine Learning | Machine Learning Basics 2021 | What Is Machine Learning? | NerdML
Просмотров 1,3 тыс.4 года назад
Introduction To Machine Learning | Machine Learning Basics 2021 | What Is Machine Learning? | NerdML
Simple Linear Regression - Mathematical Explanation | NerdML
Просмотров 1,3 тыс.4 года назад
Simple Linear Regression - Mathematical Explanation | NerdML
Walchand walo tumhare toh lag gye hai bete
Sorry didn’t get yoy
Can you show us, how to derive the formula of entropy?
Will create a separate video on that
@NerdML thank you
great video!
Thanks man!
Hello sir, you are using a labelled dataset for unsupervised (K-means Algorithm). can you explain how the labelled dataset works for K-means algorithm.
God Bless you! Very simple. Thanks
Thanks a lot🙏🏻
how did you solved the equation 11 ? Is there any code available ?
Hello brother , why consider coefficient of variable 10% ?? It is already given or not ?? And btw good explanation ..!!❤
I need the pdf please
thank you sir,explanation for k means clustering is easy.
Glad you liked it😊
nice explanation
Thanks
I don't know even after putting in so much effort why you haven't got the views that you deserve, try to learn about keywords and Hashtags hope that will help!
Oh thanks man for your concern Will surely look for the keywords & stuff Thanks for the suggestion
Sir can u share the coding part for this dataset or where can i get the code from scratch
Sure, will let you know Just drop your email id here
Nobody talks about cnn back propagation.
It’s not about CNN back propagation, as this is very important topic i though to make a separate video on it & add mathematical aspects of back propagation
@@NerdML thank you Sir! Please do it. There is very few tutorials/videos about CNN BP.
Sure, i will create one
if you are explaining a topic using an example or a sum can you at least recheck the solution once before posting the video. That will help a lot. 11th Row is coming in cluster 2 not C1. Waste a lot of time coz of this.
Sure i will keep this in mind but there’s lot of mess while preparing the whole content so hope you can understand these kind of mistakes
Thanks man, I'm also very confused about this.
helo the value for fourth row for cluster one is 7.21 not 6.32
Thanks for highlighting, i will check again
Yes, it will be 7.21
Thanks for the confirmation
@@artijaganguly4811haha Sbai eki video dekhche. Smi Students op😂
😂😂
How you got 9.32 which value you insert at X . I have getting SD value 10.63 How... Please reply
SD will be 9.32 only So according to formula you have to put value of x one by one in the formula & subtract it from mean value then take whole square of that value and divide by n i.e. 14 then take next value of x do the same & add it to the previous value Repeat this till 14th value of x from the table Then find under-root of final sum which is 86.88 (219.04 96.04 38.44 27.04 148.84 282.24 10.24 23.04 3.24 38.44 67.24 148.84 17.64 96.04) You will get 9.32 as standard deviation
@@NerdML Thankyou
You’re welcome Happy Learning
keep up the good work nice explanation
Thanks 🙏🏻
U saved my life!!!! Thx sooooooo much ^ ^
Pleasure is mine🙏🏻
is vector x assumed to be represented as x1, y1 or is it assumed to be represented as x1, x2 ?
You can assume that as x1,x2 as each vector can have its space in three dimensions i.e. x,y,z Hope it has cleared your doubt
@8:00 sir u please check d meaning of instead.
Sorry not getting you
@@NerdMLafter the word instead negative things come like here for instance negative which could have happened in the case of z he was good ubsaid instead of memorizing AND LEARNING THE PROCEDURE CONCEPTS. Learning procedure is what he did to his merit. Instead of using instead u should have said he did.
Thanks for correction👍🏻
thank you
My pleasure
It was great
Thanks🙏🏻
Hlw sir , your content is osm. I much clarify throughout your good teaching way. thanks sir
Thanks to you for liking it Happy Learning!!
Great video, subscribed! Clear, concise, friendly, and easy-going !
Thanks for liking it Happy Learning!!
great work
Thanks dude👍🏻
amazingly explained...
Oh thanks😊
wow broo
Thanks 🙏🏻
Hi, thanks a lot for your video. I have a question. I have studied machine learning and deep learning concepts theoretically and now I would like to get into data science and start using softwares. What is the most powerful alternative to spss? Im not looking for a programming language for now. I am considering jasp, jamovi, gretl, but I'm not sure in which we get neural network analysis. Any suggestion of which one choosing? Thanks a lot in advance.
Hey thanks for liking my content🙏🏻 See jamovi is better out of Jasp, jamovi & gretl If you are highly focused on bayesian models then only jasp is good Rest you can try all three & proceed accordingly as your point of view can be different according to your choice of interest & use case
@@NerdML thank you very much. So is it possible to work on neural networks concepts with jamovi? Feel free to suggest any other powerful software (with no coding required) I may have not considered. Thanks again
Hey I am not much aware about other tools but yeah will do a research on that & get back to you
@@NerdML thank you very much. Yeah I would really appreciate if you can suggest me what in your opinion is the best open source software for machine learning, that doesn’t require coding.
Grt session 👌
Thanks Varun!!
Nice video...👌👌👍
Glad you liked my content🙂
@@NerdML yea...actually it is easiest way to explain this front of interviewer and for our fundamentals as well
Exactly, that’s why moulded it into this fashion so that anyone can remember this without mugging up the concept during exams/interview
Thanks for sparing time to make this video. I learnt a alot with it
Glad you liked it Keep supporting 👍🏻
such a great explanation - thank you
Thanks🙏🏻
Awesome video with detailed explanations. Can you suggest other topics/concepts that need to be learnt for Data science? If they are already included in your channel, that would be great.
Thanks Akshara, glad you liked the content Recently I have uploaded a video on “Complete Roadmap for a learning Data Science from scratch” You can watch that
@@NerdML Thank you very much! Much appreciated!
Pleasure is mine!!
Good one ...👍👍👍
Thanks dear👍🏻
Great content .. keep it up 👍👍👍
Oh thanks man!!
Nice👏👏👏
Thank you!!😀
Thanks for this easiest explanation, absolutely awsome. please keep continue 👍
Sure, thanks Ranjan😊
Nice content 👍
Thanks 🙏🏻
Thank you Rahul
Thanks Priya
Thank you Rahul sir for this video. We waited for so long. Hope there is more tutorials on the way 👏👏👏
Sure Prashant By the way thanks for liking the content
Very helpful roadmap for learning data science strategically. Good work 👏👍
Thanks, tried building a structure
Best content👌🏻
Thanks😊
Thanks for sharing your knowledge and guidance with us. Your content is lit 🔥.
Thanks a lot❤️
I totally agree
Amazing video 👏👏
Thanks mate!! 😀
Content nicely presented, keep sharing the knowledge to world 👍🏻
Glad you liked it👍 Thanks!!
Amazing stuff Rahul... Keep up the good work ❤️❤️
Thanks buddy 👍🏻
Nice explanation ✌️...all the things are nicely presents 👌.
Thanks dude👍🏻 Keep supporting
Thank you bro😘😘😘
Very nicely explained you have good command on subject Sir please keep uploading more videos.
Thanks a lot & i will make sure
how to get that phi function? how 8? come in that phi.