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…
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 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…
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 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…
Data Resampling is a technique used in Data Science to adjust the size or distribution of a dataset. It involves modifying the existing dataset by either increasing or decreasing the number of data points. If you want to learn how…
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…
“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…
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 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 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 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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
In Machine Learning, we use data and algorithms to build intelligent systems. In the next ten years, you will explore many new high-paying jobs that require you to know about Machine Learning. So the time you will spend today 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…
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…
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…
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,…
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…
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…
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 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…
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…
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 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…
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…
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…
FLAML is an automatic machine learning library created by Microsoft for fast and lightweight automatic machine learning. It stands for Fast and Lightweight AutoML. If you have never used any AutoML library in Python for machine learning, then this article…
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…
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…
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…
In machine learning, Part of Speech Tagging or POS Tagging is a concept of natural language processing where we assign a tag to each word in a text, based on the context of the text. It helps in understanding the…
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…
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…
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 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 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 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…
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…
The R2 score is one of the performance evaluation measures for regression-based machine learning models. It is also known as the coefficient of determination. If you want to learn how to evaluate the performance of a machine learning model using…
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…
When we want to train a machine learning model, it is always recommended to divide the dataset into training and test sets if your dataset is large. If you are interested in learning how to split a dataset into training…
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 (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…
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…
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…
Perceptron is one of the simplest architecture of Artificial Neural Networks in Machine Learning. It was invented by Frank Rosenblatt in 1957. In this article, I will take you through an introduction to Perceptron in Machine Learning and its implementation…
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 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…
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…
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…
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,…
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 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…
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…
When using a machine learning algorithm, it is very important to train the model on a dataset with almost the same number of samples. This is known as a balanced class. We need to have balanced classes to train a…
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…
In Machine Learning, the AUC and ROC curve is used to measure the performance of a classification model by plotting the rate of true positives and the rate of false positives. In this article, I will walk you through a…
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 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…
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 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 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…
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…
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 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…
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 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 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 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…
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…
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…
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.…
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…
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…
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…
In Machine Learning, StandardScaler is used to resize the distribution of values ​​so that the mean of the observed values ​​is 0 and the standard deviation is 1. In this article, I will walk you through how to use StandardScaler…
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…
In Machine Learning, a seasonal autoregressive integrated moving average (SARIMA) model is a different step from an ARIMA model based on the concept of seasonal trends. In this article, I will introduce you to the SARIMA model in machine learning.…
XGBoost or Gradient Boosting is a machine learning algorithm that goes through cycles to iteratively add models to a set. In this article, I will take you through the XGBoost algorithm in Machine Learning. The cycle of the XGBoost algorithm…
In machine learning, one hot encoding is a method of quantifying categorical data. Briefly, this method produces a vector of length equal to the number of categories in the dataset. In this article, I will introduce you to the One…
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…
In this article, I’m going to take you through an in-depth review of BERT in Machine Learning for word embeddings produced by Google for Machine Learning. Here I’ll show you how to get started with BERT in Machine Learning by…
In Machine Learning, a decision tree is a decision support tool that uses a graphical or tree model of decisions and their possible consequences, including the results of random events, resource costs, and utility. This is a way of displaying…
No discussion of machine learning would be complete without a section devoted to Machine Learning Algorithms. Algorithms are a set of instructions for a computer on how to interact with, manipulate and transform data. What Are Machine Learning Algorithms? An…
The LSTM Network model stands for Long Short Term Memory networks. These are a special kind of Neural Networks which are generally capable of understanding long term dependencies. LSTM model was generally designed to prevent the problems of long term…
AdaBoost Algorithm is a boosting method that works by combining weak learners into strong learners. A good way for a prediction model to correct its predecessor is to give more attention to the training samples where the predecessor did not…
An Algorithm is a series of steps that helps in solving specific problems. Machine Learning Algorithms helps us in providing mathematical equations which help in predicting the future by using the past behaviour of the data. There is no algorithm…
ARIMA model means Autoregressive Integrated Moving Average. This model provides a family of functions which are a very powerful and flexible to perform any task related to Time Series Forecasting. In Machine Learning ARIMA model is generally a class of…
The Random Forest algorithm is an ensemble of the Decision Trees algorithm. A Decision Tree model is generally trained using the Bagging Classifier. If you don’t want to use a bagging classifier algorithm to pass it through the Decision Tree…
Have you ever got confused when you try to choose the best algorithm for a machine learning task? Suppose you are planning to buy a new car. Your planning is never going to be as short as moving to the…
Suppose you have trained a lot of classification models, and your each model is achieving the accuracy of 85 percent. A very simple way to create an even better classifier is to aggregate the predictions of each classifier and predict…
The general goal of a classification model is to find a decision boundary. The purpose of the decision boundaries is to identify those regions of the input class space that corresponds to each class. In this article, I will take…
The Ridge Regression is a regularized version of a Linear Regression. The Ridge Regression enables the machine learning algorithms to not only fit the data but also to keep weights of the model as small as possible. It is quite…
What will you do if your data is a bit more complicated than a straight line? A good alternative for you is that you can use a linear model to fit in a nonlinear data. You can use the add…
Gradient Descent is an optimisation algorithm which is capable of providing optimal performance to a wide range of tasks in Machine Learning. The idea behind a Gradient Descent algorithm is to tweak the parameters using loops to minimize the cost…