# Category Archives: Data Science

## Linear vs Non-linear Data: How to Know

In this post, you will learn the techniques in relation to knowing whether the given data set is linear or non-linear. Based on the type of machine learning problems (such as classification or regression) you are trying to solve, you could apply different techniques to determine whether the given data set is linear or non-linear. For a data scientist, it is very important to know whether the data is linear or not as it helps to choose appropriate algorithms to train a high-performance model. You will learn techniques such as the following for determining whether the data is linear or non-linear: Use scatter plot when dealing with classification problems Use …

## How to deal with Class Imbalance in Python

In this post, you will learn about how to deal with class imbalance by adjusting class weight while solving a machine learning classification problem. This will be illustrated using Sklearn Python code example. What is Class Imbalance? Class imbalance refers to a problem in machine learning where the classes in the data are not equally represented. For example, if there are 100 data points and 90 of them belong to Class A and 10 belong to Class B, then the classes are imbalanced. Class imbalance can lead to problems with training machine learning models because the models may be biased towards the more common class. If there are more examples …

## Linear regression hypothesis testing: Concepts, Examples

In relation to machine learning, linear regression is defined as a predictive modeling technique that allows us to build a model which can help predict continuous response variables as a function of a linear combination of explanatory or predictor variables. While training linear regression models, we need to rely on hypothesis testing in relation to determining the relationship between the response and predictor variables. In the case of the linear regression model, two types of hypothesis testing are done. They are T-tests and F-tests. In other words, there are two types of statistics that are used to assess whether linear regression models exist representing response and predictor variables. They are …

## Differences between Random Forest vs AdaBoost

In this post, you will learn about the key differences between the AdaBoost classifier and the Random Forest algorithm. As data scientists, you must get a good understanding of the differences between Random Forest and AdaBoost machine learning algorithms. Both algorithms can be used for both regression and classification problems. Random forest and Adaboost are two popular machine learning algorithms. Both algorithms can be used for classification and regression tasks. Both Random Forest and AdaBoost algorithm is based on the creation of a Forest of trees. Random Forest is an ensemble learning algorithm that is created using a bunch of decision trees that make use of different variables or features …

## K-Nearest Neighbors Explained with Python Examples

In this post, you will learn about the K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. Introduction to K-Nearest Neighbors (K-NN) K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-nearest neighbors are used for classification or regression. The main idea behind K-NN is to find the K nearest data points, or neighbors, to a given data point and then predict the label or value of the given data point based on the labels or values …

## Correlation Concepts, Matrix & Heatmap using Seaborn

In this blog post, we’ll be discussing correlation concepts, matrix & heatmap using Seaborn. For those of you who aren’t familiar with Seaborn, it’s a library for data visualization in Python. So if you’re looking to up your data visualization game, stay tuned! We’ll start with the basics of correlation and move on to discuss how to create matrices and heatmaps with Seaborn. Let’s get started! Introduction to Correlation Correlation is a statistical measure that expresses the strength of the relationship between two variables. The two main types of correlation are positive and negative. Positive correlation occurs when two variables move in the same direction; as one increases, so do …

## Hidden Markov Models Explained with Examples

Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are named after Andrey Markov, who first developed them in the early 1900s. Markov models are a type of probabilistic …

## Gaussian Mixture Models: What are they & when to use?

Gaussian mixture models (GMMs) are a type of machine learning algorithm. They are used to classify data into different categories based on the probability distribution. Gaussian mixture models can be used in many different areas, including finance, marketing and so much more! In this blog, an introduction to gaussian mixture models is provided along with real-world examples, what they do and when GMMs should be used. What are Gaussian mixture models (GMM)? Gaussian mixture models (GMM) are a probabilistic concept used to model real-world data sets. GMMs are a generalization of Gaussian distributions and can be used to represent any data set that can be clustered into multiple Gaussian distributions. …

## Different types of Probability Distributions: Examples

In this post, you will learn the definition of 25 different types of probability distributions. Before we get into understanding different types of probability distributions, let’s understand some fundamentals. If you are a data scientist, you would like to go through these distributions. This page could also be seen as a cheatsheet for probability distributions. What are Probability Distributions? Probability distributions are a way of describing how likely it is for a random variable to take on a given value. In other words, they provide a way of quantifying the chances of something happening. Probability distributions are often graphed as histograms, with the possibilities on the x-axis and the probabilities …

## Probability: Basic concepts, formulas, and examples

Probability is a branch of mathematics that deals with the likelihood of an event occurring. It is important to understand probability concepts if you want to get good at data science and machine learning. In this blog post, we will discuss the basic concepts of probability and provide examples to help you understand it better. We will also introduce some common formulas associated with probability. So, let’s get started! What is probability and what are the different types? Probability is a concept in mathematics that measures the likelihood of an event occurring. It is typically expressed as a number between 0 and 1, with 0 indicating that an event is …

## When to Use Which Clustering Algorithms?

There are many clustering machine learning algorithms to choose from when you want to cluster data. But which one should you use in a particular situation? In this blog post, we will explore the different clustering algorithms and explain when each one is most appropriate. We will also provide examples so that you can see how these algorithms work in practice. What clustering is and why it’s useful Simply speaking, clustering is a technique used in machine learning to group data points together. The goal of clustering is to find natural groups, or clusters, in the data. Clustering algorithms are used to automatically find these groups. Clustering is useful because …

## Accuracy, Precision, Recall & F1-Score – Python Examples

Classification models are used in classification problems to predict the target class of the data sample. The classification model predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. Performance measures in machine learning classification models are used to assess how well machine learning classification models perform in a given context. These performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine …

## AI / Data Science Operating Model: Teams, Processes

Realizing value from AI/data science or machine learning projects requires the coordination of many different teams based on an appropriate operating model. If you want to build an effective AI/data science operation, you need to create a data science operating model that outlines the steps involved in how teams are structured, how data science projects are implemented, how the maturity of data science practice is evaluated and an overall governance model which is used to keep a track of data science initiatives. In this blog post, we will discuss the key components of a data science operating model and provide examples of how to optimize your data science process. AI …

## Difference between Online & Batch Learning

In this post, you will learn about the concepts and differences between online and batch or offline learning in relation to how machine learning models in production learn incrementally from the stream of incoming data or otherwise. It is one of the most important aspects of designing machine learning systems. Data science architects would require to get a good understanding of when to go for online learning and when to go for batch or offline learning. Why online learning vs batch or offline learning? Before we get into learning the concepts of batch and on-line or online learning, let’s understand why we need different types of models training or learning …

## Deductive & Inductive Reasoning: Examples, Differences

When it comes to data science, there are two main types of reasoning that you need to be familiar with: deductive and inductive. Both of these techniques are important in order to make sound decisions based on the data that you’re working with. In this blog post, we’ll take a closer look at what deductive and inductive reasoning are, what are their differences, and how they’re related to each other. What is deductive reasoning? Deductive reasoning is an important tool in data science. Deductive reasoning is the process of deriving a conclusion based on premises that are known or assumed to be true. In other words, deductive reasoning allows you …

## Steps for Evaluating & Validating Time-Series Models

Time-series machine learning models are becoming increasingly popular due to the large volume of data that is now available. These models can be used to make predictions about future events, and they are often more accurate than traditional methods. However, it is important to properly evaluate (check accuracy by performing error analysis) and validate these models before you put them into production. In this blog post, we will discuss the different ways that you can evaluate and validate time series machine learning models. We will also provide some tips on how to improve your results. As data scientists, it is important to learn the techniques related to evaluating time-series models. …