Python Articles - Page 85 of 929

Time complexity is a measure of time, which determines the time required for the algorithm to execute. It also checks how the running time is being increased when the size of the inputs is getting increased. The time complexity is represented with the notation capital O, which sets an upper bound in the worst case performance of an algorithm. Whenever we design an algorithm we should keep in mind not only about the correctness but also about the time complexity and performance characteristics. For example, if an algorithm has time complexity O(n) that would take twice of O(n) to execute ... Read More

The name-tools module is a python library which provides us the tools to work with the names. These are used in many applications likely data cleaning, text processing and Natural Language Processing etc. It has a several functions for comparing and managing the names. Installing name-tools Before working with the name-tools, we need to install it in the python environment. The following is the process for installing the name-tools. pip install name-tools After executing the above code, if you see the text below, then the installation is considered successful. Collecting name-toolsNote: you may need to restart the kernel ... Read More

Python is a powerful language to be used for performing various statistical tests. One such statistical test is the Welch’s t-test. When there are two datasets with equal variances and you have to find out whether their means are the same or not, then using a two sample t-test would be wise enough. However, if the variances of the two datasets are not the same, then Welch’s t-test should be used to compare the means. Syntax stats.ttest_ind(dataset_one, dataset_two, equal_var = False/True) Here, ttest_ind is the function that performs Welch’s t-test. It takes three parameters, The first ... Read More

PyTorch, an open-source machine learning framework, is widely used in academic research and industries. It offers ample tools and libraries to build and train neural networks effectively. Tensors in PyTorch In PyTorch, a tensor is a multidimensional array of values that can be used to represent data for machine learning models. Dimensions may vary as 1D, 2D, 3D, and so on, totally depending on the complexity of the data they represent. Let's take an example, a 1D tensor can be used to represent a sequence of values, such as time-series data, whereas a 2D tensor can be used to represent ... Read More

Machine learning uses the mathematical approach of Singular value decomposition to comprehend huge and complicated data sets. In this mathematical approach, a Unique Valued matrix A is factorized into three matrices via decomposition. In terms of the components of A, the Singular value decomposition of matrix A can be written as A=UDVT. In this case, S denotes A's singular values, whereas U and V stand for A's left and right singular vectors, respectively. Mathematical Algorithm Given Matrix A find the Transpose of matrix A that is (AT). Find A*AT Find the Eigen Vector of A*AT ... Read More

Support Vector Machine (SVM) is a supervised algorithm used widely in handwriting recognition, sentiment analysis and many more. To separate different classes, SVM calculates the optimal hyperplane that more or less accurately creates a margin between the two classes. Here are a few ways to separate hyperplanes in SVM. Data Processing − SVM requires data that is normalised, scaled and centred since they are sensitive to such features. Choose a Kernel − A kernel function is used to transform the input into a higher dimensional space. Some of them include linear, polynomial and radial base functions. Let ... Read More

Pandas is a Python library that is used for data analysis and manipulation. It provides a number of functions for cleaning and formatting data. In this article, we will learn how to clean string data in a given Pandas DataFrame. We will cover the following topics: Removing leading and trailing spaces Replacing special characters Converting to lowercase Removing duplicate values Splitting strings into columns Merging columns Validating data Removing Leading and Trailing Spaces The strip() method can be used to remove leading and trailing spaces from a string. For example, the following code will remove the leading ... Read More

Any dataset used in Machine Learning algorithms may have a number of dimensions. However, not all of them contribute to giving an efficient output and simply cause the ML Model to perform poorly because of the increased size and complexity. Thus, it becomes important to eliminate such features from the dataset. To do this, we use a dimension reduction algorithm called PCA. PCA or Principal Component Analysis helps in removing those dimensions from the dataset that do not help in optimizing the results; thereby creating a smaller and simpler dataset with most of the original and useful information. PCA is ... Read More

Recommendation system is a tool in python that suggests items or content to users based on their preferences and past behaviors. This technology utilizes algorithms to predict users' future preferences, thereby providing them with the most relevant content. The scope of this system is vast, with widespread use in various industries such as e-commerce, streaming services, and social media. Products, movies, music, books, and more can all be recommended through these systems. The provision of personalized recommendations not only helps foster customer engagement and loyalty but can also boost sales. Types of Recommendation Systems Content-based recommendation systems These operate on ... Read More

Machine learning is one of the branches of artificial intelligence that involves developing Statistical models and algorithms that can enable a computer to learn from the input data and make decisions or predictions without being hard programmed. It involves training the ML algorithms with large datasets so that the machine can identify patterns and relationships in the data. What is an Autoencoder? Neural network architectures with autoencoders are used for unsupervised learning tasks. It is made up of a network of encoders and decoders that have been trained to rebuild the input data by compressing it into a lower-dimensional representation ... Read More