Unique Speed Data Science

                                                                                  LSTM  we will discuss now each and every stage with the help of the above diagram. State1:Memory View The memory view is responsible for remembering and forget the information based on the context of an input. (you didn't get it, wait now you will understand). In the above diagram, the memory view is the top line.   key points( Ct-1, X, +,  and Ct) . The input is an old memory, X is multiplication which forgets the useless information from the old memory, and " +"   addition lets merge all these things. when we multiply the old memory with '0' the old memory will "0" or if we multiply with vector "1" The old memory won't change. ( what ...

                 SUPPORT VECTOR MACHINE:- Support vector machine:-it is a type of supervised learning algorithm it is used to solve both classification and regression problem. Note :- It is mostly used for classification problems. what we are going to learn in SVM: a) Support vectors b) Hyperplane c) Marginal Distance d) Linear Separable e) Non-linear separable f) support kernels NOw we will discuss everything in detail. Hyper plane:- in the above diagram, we have drawn three lines(A, B, C) separating two data points (stars and reds) The lines (A, B, C) are called Hyperplanes. Note:- “Select the hyper-plane which segregates the two classes better” i.e  above there are three hyperplanes how to select the best hyperplane? b)Marginal Distance:- When we draw a hyperplane the plane creates two new(------) dotted lines one line above the hyperplane and one line below the hyperplane line. see the below image you will get an ...

                           KNN interview questions 1) Which of the following distance metric can not be used in k-NN? A) Euclidean Distance B) Manhatten Distance c) Hamming Distance E) Minkowski Distance F) Jaccard Distance G) All the above Answer:- G All of these distance metric can be used as a distance metric for KNN 2)Knn is for regression or classification? Answer:- Knn is used for both classification and regression problems. 3) When we use Manhatten Distance? Answer:-Manhatten distance is used for continuous variables. 4) You have given the following 2 statements, find which of these options is/are true in case of k-NN? In the case of very large value of k , we may include points from other classes into the neighborhood, so it leads to overfitting. In case of too small value of k the algorithm is very sensitive to noise.(it will affect our model performance). Answer:-The above two points are answers. 5...

                               🌲🌳 Decision Tree 🌲🌳 Decision Tree   Algorithm has come under supervised Learning, it is used for both  Regression and Classification. Important Terminology related to Decision tree: Root Node:  It represents the entire population or sample and this further gets divided into two or more homogeneous sets. Splitting:  It is a process of dividing a node into two or more sub-nodes. Decision Node:  When a sub-node splits into further sub-nodes, then it is called the decision node. Leaf / Terminal Node:  Nodes do not split is called Leaf or Terminal node. Pruning:  When we remove sub-nodes of a decision node, this process is called pruning. You can say the opposite process of splitting. Branch / Sub-Tree:  A subsection of the entire tree is called a branch or sub-tree. Parent and Child Node:  A node, which ...

                           K-Nearest Neighbour The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN means in short Similar things near to each other. The KNN algorithm uses ‘ feature similarity ’ to predict the values of any new data points. I am going to explain this knn with a simple example:- In the above table, we have S.No, Height, Weight & Age in our table for S.No.5 the weight is missing, So now we need to predict the weight of the person based on his Height and Age. graph example in the above graph, X_axis represents the age and the y_axis represents the Height of a person. in the above graph, I write 5 numbers, in that  4 values have output and one id not having output now see How KNN help us. the 5th number I want to predict which is circled. hint:-By seeing th...

                                 MULTI LINEAR REGRESSION Before going into MULTI LINEAR REGRESSION first look into Linear Regression. LINEAR REGRESSION:-It is all about getting the best line for the given data that supports linearity. for Linear regression please check my previous post. In Linear regression, we have only one independent variable and one dependent variable. In Multilinear Regression, we have more than one independent variable and one dependent variable. This is the main difference between Multilinear regression and Linear regression. Formulae for Linear regression and Multilinear Regression is listed below: Evaluation metrics for Multi-linear Regression problems are: a)Mean Absolute error b)Mean Squared error c)Root Mean Squared Error d)..... For Evaluation metrics I had posted another post please check it. For the code part please check my Github In ...