Machines are getting more intelligent than ever in the modern world. This is mostly brought on by machine learning's rising significance. The process of teaching computers to learn from data and then utilize that information to make judgments or predictions is known as machine learning. Understanding how to judge the performance of these models is essential as more and more sectors start to rely on machine learning. In this blog article, we'll examine the machine learning concepts of loss and accuracy and how they can be used to evaluate model efficacy. What is Loss in Machine Learning? In machine learning, ... Read More

Machine learning has transformed civilization in recent years. It has become one of the industries with the highest demand and will continue to gain popularity. Model creation is one of the core components of machine learning. It involves creating algorithms to analyze data and make predictions based on that data. Even the best algorithms will not work well if the features are not constructed properly. In this blog post, we'll look at the benefits of feature engineering while building models. What is Feature Engineering? Feature engineering is the act of identifying and modifying the most important features from raw data ... Read More

Time series data analysis can be applied to a range of fields, including finance, economics, and marketing. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are extensively used in time series data analysis. A time series correlation between the observations is assessed using PACF plots. Finding the important lag values that enable estimating the series' future values is useful. Even yet, if you are unfamiliar with the PACF graph, it could be challenging to read. In this blog article, we'll help you through each step of comprehending a PACF graph for time series analysis. What is PACF? Partial Autocorrelation ... Read More

Gradient descent is a prominent optimization approach in machine learning for minimizing a model's loss function. In layman's terms, it entails repeatedly changing the model's parameters until the ideal range of values is discovered that minimizes the loss function. The method operates by making tiny steps in the direction of the loss function's negative gradient, or, more specifically, the path of steepest descent. The learning rate, a hyperparameter that regulates the algorithm's trade-off between speed and accuracy, affects the size of the steps. Many machine learning methods, including linear regression, logistic regression, and neural networks, to mention a few, employ ... Read More

Logistic regression is a statistical approach for examining the connection between a dependent variable and one or more independent variables. It is a form of regression analysis frequently used for classification tasks when the dependent variable is binary (i.e., takes only two values). Finding the link between the independent factors and the likelihood that the dependent variable will take on a certain value is the aim of logistic regression. Since it enables us to predict the likelihood of an event occurring based on the values of the independent variables, logistic regression is a crucial tool in data analysis and machine ... Read More

Re-sampling is a statistical technique for gathering more data samples from which inferences about the population or the process by which the initial data were produced can be made. These methods are widely used in data analysis when it is necessary to estimate a population parameter from the given data or when there are few accessible data points. Resampling approaches typically use techniques like bootstrapping, jackknifing, and permutation testing to estimate standard errors, confidence intervals, and p-values. Analyzing and interpreting data is one of a data scientist's most crucial responsibilities. The supplied data, however, isn't always sufficiently representative, which might ... Read More

A tree is a data structure which consists of nodes. The nodes are connected by the edges. The top most node is called as the root and the bottom most nodes are called as the leaves. Leaves are the nodes that do not have any children. Binary Tree A binary tree is a tree in which every node can consist a maximum of 2 children. That means, every node can either have 0 or 1 or 2 children but not more than that. Every node in a binary tree consists of three fields − Data Pointer to the left ... Read More

A linked list is said to have a loop when any node in the linked list is not pointing to NULL. The last node will be pointing to one of the previous nodes in the linked list, thus creating a loop. There will not be an end in a linked list that has a loop. In the below example, the last node (node 5) is not pointing to NULL. Instead, it is pointing to the node 3 and a loop is established. Hence, there is no end to the above linked list. Algorithm Take two pointers fast and slow ... Read More

Converting a list into a string One method for converting a list into a string is to iterate through all the items of a list and concatenate them into an empty string. Example lis=["I", "want", "cheese", "cake"] str="" for i in lis: str=str+i str=str+" " print(str) Output I want cheese cake Using Join Function Join is an in-built function in python which is used for joining the items of an iterable object (ex: list) using a separator which will be specified by the user. We can use the join ... Read More

Linked list is used for storing the data in non-contiguous memory locations. The nodes containing the data items are linked using pointers. Each node consists of two fields. The first field is used for storing the data and the second field contains the link to the next node. Brute Force Technique To find the middle element of a linked list, the brute force technique is to find out the length of the linked list by iterating the entire linked list till NULL is encountered and then the length is divided by 2 to get the index of the middle element. ... Read More