# Tag Machine Learning Algorithm

## Machine Learning: A Game Changer in Healthcare Hey there! Let’s chat about something that’s really shaking things up in healthcare: machine learning (ML). Think of it as a super-smart computer that’s learning how to help doctors diagnose and treat us better. It’s like having a brainy sidekick…

## Machine Learning Interview Questions on Performance Metrics When preparing for Machine Learning interviews, it’s crucial to have a good understanding of performance evaluation metrics, as they are key to assessing and improving model performance. So, if you are looking for some challenging interview questions related to performance…

## Scikit-learn for Machine Learning Scikit-learn is a powerful library that provides a wide range of tools for data preprocessing and machine learning. It is built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of the…

## ARIMA and SARIMA for Time Series Forecasting Time Series Forecasting is a statistical technique used to make predictions based on historical time-ordered data points. It’s valuable when dealing with data that changes over time, such as stock prices, sales figures, weather data, or economic indicators. ARIMA and…

## Regression Performance Evaluation Metrics Regression metrics are quantitative measures used to evaluate the performance of regression models. They provide information about how well a regression model fits the data and how accurately it predicts the outcome variable. If you want to learn how to…

## Here’s How to Choose Machine Learning Algorithms Machine Learning means using data and algorithms to build intelligent systems. There are various Machine Learning algorithms used for training models that help in solving problems and building intelligent systems. While learning Machine Learning, many people are often confused about…

## Introducing Machine Learning Algorithms: Handbook “Machine Learning Algorithms: Handbook” is a book based on a step by step guide to all Machine Learning algorithms with implementation using Python. In this article, I’ll give you a complete overview of the book so that you can understand…

## Ridge and Lasso Regression In Machine Learning, Ridge and Lasso Regression are regularization techniques used in linear regression to prevent overfitting and improve the model’s generalization to unseen data. They work by adding a penalty term to the linear regression loss function. If you…

## Machine Learning Algorithms Guide Machine Learning means using data and algorithms to build intelligent systems. Machine Learning algorithms are computational procedures that enable machines to learn patterns and relationships from data without being explicitly programmed. These algorithms allow machines to make predictions, decisions, and…

## Hyperparameter Tuning in Machine Learning Hyperparameter tuning is an essential process in Machine Learning that involves optimizing the settings that guide the training of a Machine Learning model. But these settings themselves are not learned from the data. These settings, called hyperparameters, play a vital…

## Data Preprocessing Pipeline using Python Data preprocessing is a critical step in data science tasks, ensuring that raw data is transformed into a clean, organized, and structured format suitable for analysis. A data preprocessing pipeline streamlines this complex process by automating a series of steps, enabling data…

## Scaling and Normalization in Machine Learning In Machine Learning, Scaling and Normalization are techniques used in data preprocessing to transform features or variables in a dataset. These techniques ensure that the data is in an appropriate range and distribution, which facilitates efficient training of Machine Learning…

## Here’s how Polynomial Regression Algorithm Works In Machine Learning, Polynomial Regression is a regression algorithm used to model nonlinear relationships between input features and output labels. So, if you are new to Machine Learning and want to know how the Polynomial Regression algorithm works, this article…

## Here’s How SVM Algorithm Works Support Vector Machine (SVM) is a popular supervised learning algorithm used for classification and regression problems in Machine Learning. The basic idea behind SVM is to find a decision boundary that separates data points of different classes with the maximum…

## Here’s How Random Forest Algorithm Works In Machine Learning, the Random Forest algorithm is an ensemble learning method for classification and regression tasks. It is composed of multiple decision trees, where each tree is built on a random subset of the input features and a random…

## Here’s How Decision Tree Algorithm Works In Machine Learning, Decision Tree is an algorithm that works like a flowchart or tree structure to make decisions based on input data. It starts with a question or condition and then follows different branches based on the answers to…

## Here’s How Naive Bayes Algorithm Works In Machine Learning, Naive Bayes is an algorithm that uses probabilities to make predictions. It is used for classification problems, where the goal is to predict the class an input belongs to. So, if you are new to Machine Learning…

## Here’s How Logistic Regression Algorithm Works In Machine Learning, Logistic Regression is a statistical model used for binary classification problems. It is used to predict the probability of an outcome based on the input features. It uses a sigmoid function to map the input features to…

## Here’s How Linear Regression Algorithm Works In Machine Learning, Linear Regression Algorithm is a statistical technique for calculating the value of a dependent variable based on the value of an independent variable. The goal of linear regression is to find the best-fit line that describes the…

## Decision Tree Algorithm in Machine Learning The decision Tree algorithm is a supervised Machine Learning algorithm used for both classification and regression problems. It is based on a sequential decision process. If you want to know everything about the Decision Tree algorithm, this article is for…

## Roadmap to Learn Machine Learning In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, you…

## Machine Learning Algorithms Every Beginner Should Know You must have heard about some machine learning algorithms before, but most beginners often get confused about how many machine learning algorithms they need to learn when they see professionals using algorithms (specifically in Kaggle) that they haven’t heard about…

## Process of Natural Language Processing Natural Language Processing (NLP) is a subset of machine learning in which we aim to train computers to understand human languages. The chatbot you see in a banking app, Siri on iPhone, or Google translator are examples of natural language…

## How Neural Network Works A neural network is a computational structure that connects an input layer to an output layer. This computational structure is used in training deep learning models that can easily outperform any classical machine learning algorithm. As a data science beginner,…

## Sentence and Word Tokenization using Python Tokenization is the first step you should perform after collecting a textual dataset in every problem based on Natural Language Processing. Sentence and Word tokenization are two different strategies of tokenization that you should know. In this article, I will…

## Visualize a Machine Learning Algorithm using Python A machine learning algorithm is used to find relationships between features and labels. Features are the independent variables that we feed into an algorithm to train a machine learning model, and labels are the dependent variables that we aim to…

## How to Give Inputs to a Machine Learning Model One of the mistakes data science newbies make while working on a machine learning project is that they train a machine learning model, check its accuracy score, and then complete the project. They do not test the performance of the…

## Binary Classification Algorithms in Machine Learning Binary classification is one of the types of classification problems in machine learning where we have to classify between two mutually exclusive classes. For example, classifying messages as spam or not spam, classifying news as Fake or Real. There are…

## Multiclass Classification Algorithms in Machine Learning When there are only two classes in a classification problem, this is the problem of binary classification, just like that, classification with more than two classes is called multiclass classification. If you want to know the best algorithms for multiclass…

## Multilayer Perceptron in Machine Learning A Multilayer Perceptron or MLP is one of the simplest feed-forward neural networks. Multilayer Perceptrons are the types of neural networks which are bidirectional as they foreword propagation of the inputs and backward propagation of the weights. If you want…

## Clustering Algorithms in Machine Learning Clustering is the task of identifying similar instances based on similar features and assigning them to clusters based on similar instances. It sounds like classification where each instance is also assigned to a group, but unlike classification, clustering is based…

## Use Cases of Different Machine Learning Algorithms There are so many algorithms in machine learning that you need to learn. Understanding the use cases of every machine learning algorithm is very difficult for every data science beginner, but it is very important as it helps you to…

## LeNet-5 Architecture using Python The LeNet-5 architecture is the most widely used architecture of convolutional neural networks. It was created by Yann LeCunn in 1998. If you have never used the LeNet-5 architecture of convolutional neural networks, then this article is for you. In…

## Comparison of Classification Algorithms in Machine Learning There are many real-life use cases to create your unique machine learning projects. If you’re still struggling to work on an actual use case, find something practical and unique, like a machine learning project where you’ll show a comparison of…

## Mean Shift Clustering in Machine Learning The mean shift algorithm is a nonparametric clustering algorithm that does not require prior knowledge of the number of clusters. If you’ve never used the Mean Shift algorithm, this article is for you. In this article, I’ll take you through…

## Mini-batch K-means Clustering in Machine Learning The Mini-batch K-means clustering algorithm is a version of the standard K-means algorithm in machine learning. It uses small, random, fixed-size batches of data to store in memory, and then with each iteration, a random sample of the data is…

## Agglomerative Clustering in Machine Learning Agglomerative clustering is based on hierarchical clustering which is used to form a hierarchy of clusters. It is one of the types of clustering algorithms in machine learning. Unlike the K-Means and DBSCAN clustering algorithms, it is not very common…

## Multinomial Naive Bayes in Machine Learning The Multinomial Naive Bayes is one of the variants of the Naive Bayes algorithm in machine learning. It is very useful to use on a dataset that is distributed multinomially. This algorithm is especially preferred in classification tasks based on…

## Bernoulli Naive Bayes in Machine Learning The Bernoulli Naive Bayes is one of the variations of the Naive Bayes algorithm in machine learning and it is very useful to use in a binary distribution where the output label may be present or absent. If you have…

## F-Beta Score in Machine Learning F-beta is the weighted harmonic mean of the precision and recall. It is used as a performance evaluation measure for classification-based machine learning models. If you’ve never used this performance measurement metric before to evaluate your classification models, this article…

## Passive Aggressive Regression in Machine Learning Passive Aggressive Regression belongs to the category of online learning in machine learning. It is not one of the most commonly used machine learning algorithms, but it can nonetheless be used to achieve efficient results that solve regression-based problems. So,…

## Stochastic Gradient Descent in Machine Learning Stochastic gradient descent is a machine learning algorithm that is used to minimize a cost function by iterating a weight update based on the gradients. If you’ve never used the SGD classification algorithm before, this article is for you. In…

## Explained Variance in Machine Learning In machine learning, variance is the difference between the actual samples of the dataset and the predictions made by the model. When working on a regression-based machine learning problem, it is very useful to know how much of the variance…

## t-SNE in Machine Learning t-SNE is a very powerful machine learning algorithm that can be used to visualize a high-dimensional dataset also in two-dimensional figures. It stands for t-Distributed Stochastic Neighbor Embedding. If you want to learn more about t-SNE and how to visualize…

## Independent Component Analysis in Machine Learning Independent Component Analysis (ICA) is one of the alternatives of PCA that is used to find the underlying factors or components from a multivariate statistical dataset. This is different from a standard PCA because it looks for components that are…

## Non-Negative Matrix Factorization in Machine Learning Non-Negative Matrix Factorization (NNMF) is a group of machine learning algorithms used in multivariate analysis and linear algebra. It is used in place of PCA when the dataset is composed of non-negative items. If you don’t know anything about the…

## Fill Missing Values in a Dataset using Python When working on a data science task, sometimes many missing values act as a hindrance while getting the correct information from a dataset. We can easily remove the missing values, but sometimes we need to fill these values depending on…

## Handling Categorical Data in Machine Learning While solving problems based on classification with machine learning, we mostly find datasets made up of categorical labels that cannot be processed by all machine learning algorithms. So, if you want to learn how to handle categorical data in machine…

## Bias and Variance using Python When training a machine learning model, it is very important to understand the bias and variance of predictions of your model. It helps in analyzing prediction errors which help us in training more accurate machine learning models. In this article,…

## Kernel PCA in Machine Learning Kernel PCA is one of the variations of principal component analysis in machine learning that uses kernel methods to perform the initially linear operations of principal component analysis. In this article, I will take you through an introduction to Kernel…

## One-vs-all and One-vs-one in Machine Learning We usually train a machine learning model to produce a single value or a single label as the output. But when the number of output classes is greater than one, this is the problem of multiclass classification. One-vs-all and One-vs-one…

## Assumptions of Machine Learning Algorithms The performance of a machine learning algorithm on a particular dataset often depends on whether the features of the dataset satisfies the assumptions of that machine learning algorithm. Not all machine learning algorithms have assumptions this is why all algorithms…

## Sparse PCA in Machine Learning Principal Component Analysis (PCA) is a dimensionality reduction algorithm used to reduce the dimensionality of a dataset. Sparse PCA is one variation of PCA that can exploit the natural sparsity of data while extracting the principal components. In this article,…

## Unsupervised Machine Learning Algorithms In unsupervised machine learning, the training data is not labelled and here you have to find clusters to detect the similarities between different data points. In this article, I’m going to introduce you to all the unsupervised machine learning algorithms…

## Polynomial Regression in Machine Learning Polynomial regression is a machine learning algorithm that is used to train a linear model on non-linear data. Sometimes your data is much more complex than a straight line, in such cases, it is not a good option to train…

## Best Algorithm for Binary Classification In Machine Learning, binary classification consists of distinguishing two classes. For example, classifying messages as spam or not as spam. We have a bunch of machine learning algorithms for the binary classification task, so to help you choose the best…

## How to Select a Machine Learning Algorithm? There are so many machine learning algorithms for all machine learning problems like classification, regression, and clustering. You may be familiar with more than 3-4 algorithms for each task, but the problem every newbie faces is how to select a…

## Scikit-learn Tutorial for Machine Learning Scikit-learn is one of the most useful Python libraries for machine learning. All the concepts that we study about machine learning theoretically can be implemented by using the Scikit-learn library in Python. In this article, I will take you through…

## The Most Common Machine Learning Algorithms The demand for data scientists and machine learning engineers has led to great competition for your first job in data science. It is believed that someone with a very good knowledge of the fundamentals of data science is more likely…

## BIRCH Clustering in Machine Learning The BIRCH is a Clustering algorithm in machine learning. It stands for Balanced Reducing and Clustering using Hierarchies. In this article, I will take you through the concept of BIRCH Clustering in Machine Learning and its implementation using Python. BIRCH…

## Feature Scaling in Machine Learning Feature Scaling means resizing features so that no feature dominates other features. In machine learning, we use the concept of feature scaling to make sure that all the features we use to train a machine learning model are at a…

## Cosine Similarity in Machine Learning Cosine similarity is a method used in building machine learning applications such as recommender systems. It is a technique to find the similarities between the two documents. In this article, I’ll give you an introduction to Cosine Similarity in Machine…

## Most Popular Clustering Algorithms in Machine Learning In Machine Learning, clustering involves identifying similar instances and then assigning them to similar clusters or groups of instances. It is an unsupervised machine learning technique. In this article, I’ll give you an introduction to the most popular clustering algorithms…

## Principal Component Analysis in Machine Learning The principal component analysis (PCA) is a dimensionality reduction algorithm. This is one of the easiest and most intuitive ways to reduce the dimensions of a dataset. In this article, I will walk you through the Principal Component Analysis in…

## Dimensionality Reduction in Machine Learning Dimensionality reduction is used to reduce the dimensions of a data set to speed up a subsequent machine learning algorithm. It removes noise and redundant features, which improves the performance of the algorithm. In this article, I will introduce you…

## K-Means Clustering in Machine Learning The K-Means Clustering is a clustering algorithm capable of clustering an unlabeled dataset quickly and efficiently in just a very few iterations. In this article, I will take you through the K-Means clustering in machine learning using Python. K-Means Clustering…

## Collaborative Filtering in Machine Learning Collaborative filtering is a recommendation system method that is formed by the collaboration of multiple users. The idea behind it is to recommend products or services to a user that their peers have appreciated. In this article, I will introduce…

## Gradient Descent Algorithm in Machine Learning Gradient Descent is an optimization algorithm used to train a machine learning model differently. It is best suited for problems where there are a large number of features and too many samples to fit in the memory of a machine…

## Passive Aggressive Classifier in Machine Learning Passive Aggressive Classifier belongs to the category of online learning algorithms in machine learning. It works by responding as passive for correct classifications and responding as aggressive for any miscalculation. In this article, I will walk you through what Passive…

## Content-Based Filtering in Machine Learning Most recommendation systems use content-based filtering and collaborative filtering to show recommendations to the user to provide a better user experience. Content-based filtering generates recommendations based on a user’s behaviour. In this article, I will walk you through what content-based…

## Naive Bayes Algorithm in Machine Learning In machine learning, the Naive Bayes algorithm is based on Bayes’ theorem with naïve assumptions. This makes it easier to train a model by assuming that the features are independent of each other. In this article, I will give you…

## CatBoost Algorithm in Machine Learning The CatBoost algorithm is based on Gradient Descent which is a powerful technique for classification and regression problems in Machine Learning. In this article, I will introduce you to the CatBoost algorithm in Machine Learning and its implementation using Python.…

## Classification Algorithms in Machine Learning In classification algorithms, a computer is programmed to specify to which category an entry belongs. Object detection is one of the problems where a classification algorithm can be used. In this article, I will introduce you to all the machine…

## What are Machine Learning Algorithms? To understand everything about machine learning, you need to understand what machine learning algorithm is. In this article, I will explain what machine learning algorithms are. Machine learning algorithms are a type of statistical techniques applied to data using a…

## Support Vector Machine Tutorial using Python The Support Vector Machine is a very powerful and flexible class of supervised machine learning algorithms for classification and regression tasks. In this article, I will introduce you to a machine learning tutorial on Support Vector Machine using Python. Support…

## Choose Algorithm in Machine Learning For any given machine learning problem, many algorithms can be applied and several models can be generated. A spam detection classification problem, for example, can be solved using a variety of models, including naive Bayes, logistic regression, and deep learning…

## Difference Between Algorithm and Model in Machine Learning A very common confusion among those new to machine learning is the difference between a machine learning algorithm and a Model in machine learning. The two terms are often used interchangeably, which makes it even more confusing. So in this…

## Machine Learning tutorial on k Nearest Neighbor with Python

k-Nearest Neighbors with Python

## Artificial Neural Networks with Machine Learning

Classify Clothes using Python and Artificial Neural Networks