Logistic regression is a type of classification algorithm used in machine learning very frequently. It is one of the easiest and most efficient classification algorithms, which is tried out on almost every model built for classification problems. However, we can also use logistic regression for solving regression problems, although in ... Read More

Introduction In Python, Lists and dictionaries are one of the most used methods for data collection and processing. There are so many operations related to lists and dictionaries which are commonly used to get the data in the desired form. Sometimes we may also need to zip the two different ... Read More

Introduction In Python, tuples are one of the widely used methods to store and process data according to the requirements. There are so many operations involved in a tuple where the data is preprocessed and transformed according to the requirements of the problem statement. Zipping operations are one of the ... Read More

Introduction In Python, lists are one of the widely used methods to store the numerical or string values in the same. They are mutable and are defined by using the square brackets []. Such types of lists can contain different elements in them, which can be of different data types. ... Read More

Introduction In Python, the “yield” is a type of keyword that is used, when working with the generators. Unlike the normal or the classic functions in Python, where the output or the value is returned using the keyword “return”, the generators use the “yield” keywords for the same. In ... Read More

Introduction In machine learning, linear regression is one of the best algorithms used for linear types of data and it returns very accurate predictions the same. Although after training a model with any algorithm it is necessary to check the performance of the algorithm to get an idea about how ... Read More

Introduction Anomalies are values or data observations that are very different from the other observations in the existing datasets., Detecting and processing the anomalies become essential while building a machine learning model, as the quality of the data that is to be passed to the model should be fair enough ... Read More

Introduction Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the ... Read More

Introduction Maximum likelihood is an approach commonly used for such density estimation problems, in which a likelihood function is defined to get the probabilities of the distributed data. It is imperative to study and understand the concept of maximum likelihood as it is one of the primary and core concepts ... Read More

Introduction Hyperparameter tuning in machine learning is a technique where we tune or change the default parameters of the existing model or algorithm to achieve higher accuracies and better performance. Sometimes when we use the default parameters of the algorithms, it does not suit the existing data as the data ... Read More