Category Archives: Data Science
K-Fold Cross Validation – Python Example
In this post, you will learn about K-fold Cross-Validation concepts with Python code examples. K-fold cross-validation is a data splitting technique that can be implemented with k > 1 folds. K-Fold Cross Validation is also known as k-cross, k-fold cross-validation, k-fold CV, and k-folds. The k-fold cross-validation technique can be implemented easily using Python with scikit learn (Sklearn) package which provides an easy way to calculate k-fold cross-validation models. It is important to learn the concepts of cross-validation concepts in order to perform model tuning with the end goal to choose a model which has a high generalization performance. As a data scientist / machine learning Engineer, you must have a good …
Positively Skewed Probability Distributions: Examples
Probability distributions are an essential concept in statistics and data analysis. They describe the likelihood of different outcomes or events occurring and provide valuable insights into the characteristics of a given data set. Skewness is an important aspect of probability distributions that can have a significant impact on data analysis and decision-making. In this blog, we will focus on positively skewed probability distributions and explore some real-life examples where these distributions occur. We will discuss what a positively skewed distribution is, what are its different types with formula and definitions. By the end of this blog, you will have a better understanding of positively skewed distributions and be able to …
Maximum Likelihood Estimation: Concepts, Examples
As data science continues to grow in importance and relevance, so too does the need for tools and techniques that can help extract insights from large, complex datasets. One such tool that is becoming increasingly popular among data scientists is Maximum Likelihood Estimation (MLE). This is becoming more so important to learn fundamentals of MLE concepts as it is at the core of generative modeling (generative AI). MLE is a statistical method used to estimate the parameters of a probability distribution, based on a set of observed data points. MLE is particularly important for data scientists because it underpins many of the probabilistic machine learning models that are used today. …
Generative vs Discriminative Models: Examples
The field of machine learning is rapidly evolving, and with it, the concepts and techniques that are used to develop models that can learn from data. Among these concepts, generative and discriminative models are two widely used approaches in the field. Generative models learn the joint probability distribution of the input features and output labels, whereas discriminative models learn the conditional probability distribution of the output labels given the input features. While both models have their strengths and weaknesses, understanding the differences between them is crucial to developing effective machine learning systems. Real-world problems such as speech recognition, natural language processing, and computer vision, require complex solutions that are able …
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 learning. …
Sequence to Sequence Models: Types, Examples
Sequence to sequence (Seq2Seq) modeling is a powerful machine learning technique that has revolutionized the way we do natural language processing (NLP). It allows us to process input sequences of varying lengths and produce output sequences of varying lengths, making it particularly useful for tasks such as language translation, speech recognition, and chatbot development. Sequence to sequence modeling also provides a great foundation for creating text summarizers, question answering systems, sentiment analysis systems, and more. With its wide range of applications, learning about sequence to sequence modeling concepts is essential for anyone who wants to work in the field of natural language processing. This blog post will discuss types of …
Natural Language Processing (NLP) Task Examples
Have you ever wondered how your phone’s voice assistant understands your commands and responds appropriately? Or how search engines are able to provide relevant results for your queries? The answer lies in Natural Language Processing (NLP), a subfield of artificial intelligence (AI) that focuses on enabling machines to understand and process human language. NLP is becoming increasingly important in today’s world as more and more businesses are adopting AI-powered solutions to improve customer experiences, automate manual tasks, and gain insights from large volumes of textual data. With recent advancements in AI technology, it is now possible to use pre-trained language models such as ChatGPT to perform various NLP tasks with …
Types of Data Visualization: Examples
In today’s data-driven world, the ability to extract insights from vast amounts of information has become a critical skill for data scientists and analysts. Visualizing data through charts, graphs, and other types of visual representations can help them uncover patterns and relationships that might be difficult to spot otherwise. However, not all visualizations are created equal, and choosing the right type of visualization can make all the difference in communicating insights effectively. That’s why understanding the different types of visualization available is crucial for data visualization experts and data scientists. In this blog, we’ll explore some of the most common types of visualization, including comparison plots, relation plots, composition plots …
Hypothesis Testing Steps & Examples
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events in order to establish new knowledge. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, how to formulate them and how to use them in …
Statistics Terminologies Cheat Sheet & Examples
Have you ever felt overwhelmed by all the statistics terminology out there? From sampling distribution to central limit theorem to null hypothesis to p-values to standard deviation, it can be hard to keep up with all the statistical concepts and how they fit into your research. That’s why we created a Statistics Terminologies Cheat Sheet & Examples – a comprehensive guide to help you better understand the essential terms and their use in data analysis. Our cheat sheet covers topics like descriptive statistics, probability, hypothesis testing, and more. And each definition is accompanied by an example to help illuminate the concept even further. Understanding statistics terminology is critical for data …
Machine Learning Bias Explained with Examples
In the artificial intelligence (AI) / machine learning (ML) powered world where predictive models have started getting used more often in decision-making areas, the primary concerns of policy makers, auditors and end users have been to make sure that these systems using the models are not making biased/unfair decisions based on model predictions (intentional or unintentional discrimination). Imagine industries such as banking, insurance, and employment where models are used as solutions to decision-making problems such as shortlisting candidates for interviews, approving loans/credits, deciding insurance premiums etc. How harmful it could be to the end users as these decisions may impact their livelihood based on biased predictions made by the model, thereby, …
Machine Learning Concepts & Examples
Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover a detailed introduction to what is machine learning (ML) including different definitions. We will also learn about different types of machine learning tasks, algorithms, etc along with real-world examples. What is machine learning & how does it work? Simply speaking, machine learning can be used to model our beliefs about real-world events. For example, let’s say a person came to a doctor with a certain blood report. A doctor based on his belief system …
Geometric Distribution Concepts, Formula, Examples
Geometric Distribution, a widely used concept in probability theory, is used to represent the probability of achieving success or failure in a series of independent trials, where the probability of success remains constant. It is one of the essential tools used in a wide range of fields, including economics, engineering, physics, and statistics. As data scientists / statisticians, it is of utmost important to understand its concepts and applications in a clear manner. In this blog, we will introduce you to the basics of Geometric distribution, starting with its definition and properties. We will also explore the geometric distribution formula and how it is used to calculate the probability of …
Data value chain: Framework, Concepts
As organizations become increasingly data-driven, understanding the value of data is critical for success. The data value chain framework helps to identify and maximize the value of data by breaking it down into its components. In this post, we will explain what a data value chain is, why it’s important, and how to implement it. Data Value Chain Framework: Key Stages The data value chain (DVC) is a business model that helps organizations understand how to create, manage and utilize their data assets in order to realize maximum business value based on using them. It breaks down the various stages of an organization’s entire journey with its data into distinct …
Ensemble Methods in Machine Learning: Examples
Machine learning models are often trained with a variety of different methods in order to create a more accurate prediction. Ensemble methods are one way to do this, and involve combining the predictions of several different models in order to get a more accurate result. When different models make predictions together, it can help create a more accurate result. Data scientists should care about this because it can help them create models that are more accurate. In this article, we will look at some of the common ensemble methods used in machine learning. Data scientists should care about this because it can help them create models that are more accurate. …
Random Forest Classifier Python Example
Random forest classifiers are popular machine learning algorithms that are used for classification. In this post, you will learn about the concepts of random forest classifiers and how to train a Random Forest Classifier using the Python Sklearn library. This code will be helpful if you are a beginner data scientist or just want to quickly get a code sample to get started with training a machine learning model using the Random Forest algorithm. The following topics will be covered: What is a Random Forest Classifier & How do they Work? Random forests are a type of machine learning algorithm that is used for classification and regression tasks. A classifier …