Tag Archives: Data Science

Machine Learning Models Evaluation Techniques

AUC-ROC curve

Machine learning is a powerful machine intelligence technique that can be used to develop predictive models for different types of data. It has become the backbone of many intelligent applications and evaluating machine learning model performance at a regular intervals is key to success of such applications. A machine learning model’s performance depends on several factors including the type of algorithm used, how well it was trained and more. In this blog post, we will discuss  essential techniques for evaluating machine-learning model performance in order to provide you with some best practices when working with machine-learning models. The following are different techniques that can be used for evaluating machine learning …

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Data Preprocessing Steps in Machine Learning

data preprocessing in machine learning

Data preprocessing is an essential step in any machine learning project. By cleaning and preparing your data, you can ensure that your machine learning model is as accurate as possible. In this blog post, we’ll cover some of the important and most common data preprocessing steps that every data scientist should know. Replace/remove missing data Before building a machine learning model, it is important to preprocess the data and remove or replace any missing values. Missing data can cause problems with the model, such as biased results or inaccurate predictions. There are a few different ways to handle missing data, but the best approach depends on the situation. In some …

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Bagging vs Boosting Machine Learning Methods

boosting vs bagging differences examples

In machine learning, there are a variety of methods that can be used to improve the performance of your models. Two of the most popular methods are bagging and boosting. In this blog post, we’ll take a look at what these methods are and how they work with the help of examples. What is Bagging? Bagging, short for “bootstrap aggregating”, is a method that can be used to improve the accuracy of your machine learning models. The idea behind bagging is to train multiple models on different subsets of the data and then combine the predictions of those models. The data is split into a number of smaller datasets, or …

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Generative vs Discriminative Models Examples

generative vs discriminative models

f you’re working in the field of machine learning, it’s important to understand the difference between generative and discriminative models. These two types of models are both used in supervised learning, but they approach the problem in different ways. In this blog post, we’ll take a look at what generative and discriminative models are, how they work, and some examples of each. What are Generative Models? Generative models are a type of machine learning algorithm that is used to generate new data samples based on a training set. For example, a generative model could be trained on a dataset of pictures of cats, and then used to generate new cat …

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Weak Supervised Learning: Concepts & Examples

weak supervised learning

Supervised learning is a type of machine learning algorithm that uses a labeled dataset to learn and generalize from. The labels act as supervisors, providing the algorithm with feedback so it can learn to map input data to the correct output labels. In this blog post, we’ll be focusing on weak supervised learning, a subset of supervised learning that uses only partially labeled or unlabeled data. We’ll cover some of the most common weak supervision techniques and provide examples of each. What is Weak Supervised Learning? Weak supervised learning is a type of machine learning where the learner is only given a few labels to work with. Weak supervision is …

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Healthcare Claims Processing AI Use Cases

healthcare claims processing use cases AI and machine learning

In recent years, artificial intelligence (AI) / machine learning (ML) has begun to revolutionize many industries – and healthcare is no exception. Hospitals and insurance companies are now using AI to automate various tasks in the healthcare claims processing workflow. Claims processing is a complex and time-consuming task that often requires manual intervention. By using AI to automate claims processing, healthcare organizations can reduce costs, improve accuracy, and speed up the claims adjudication process. In this blog post, we will explore some of the most common use cases for healthcare claims processing AI / machine learning. Automated Data Entry One of the most time-consuming tasks in the claims process is …

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ESG & AI / Machine Learning Use Cases

ESG AI use cases

Environmental, social, and governance (ESG) factors are a set of standards used to evaluate a company’s performance on issues that have an impact on society and the environment. AI or machine learning can be used to help identify these factors. In this blog post, we will explore some use cases for how AI / machine learning can be used in conjunction with ESG factors. The following is a list of AI use cases related ESG. This list will be updated from time-to-time.  Predict ESG ratings using fundamental dataset: Investors (asset managers and asset owners) started to assess companies based on how they handle sustainability issues. To do this assessment, investors …

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Procurement Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making.  One can get started with procurement analytics with dashboards …

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Backpropagation Algorithm in Neural Network: Examples

In this post, you will learn about the concepts of neural network backpropagation algorithm along with Python examples. As a data scientist, it is very important to learn the concepts of backpropagation algorithm if you want to get good at deep learning models. This is because back propagation algorithm is key to learning weights at different layers in the deep neural network. What’s Backpropagation Algorithm? The backpropagation algorithm is a well-known procedure for training neural networks. In general, backpropagation works by propagating error signals backwards through the network, from the output layer back to the input layer. This process adjusts the weights of the connections between neurons in order to …

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Differences: Decision Tree & Random Forest

sample random forest

In machine learning, there are a few different algorithms that can be used for regression and classification tasks. Two of the most popular are decision trees and random forest. Both of these algorithms have their similarities and differences, and in this blog post, we’ll take a look at the key differences between them. What is decision tree algorithm? A decision tree is a machine learning algorithm that can be used for both classification and regression tasks. The algorithm works by splitting the data into smaller subsets, and then using these subsets to make predictions. Each split is based on a decision criterion, such as the purity of the data or …

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List of Machine Learning Topics for Learning

List of machine learning topics for learning

Are you looking for a list of machine learning topics to learn more about? If so, you’ve come to the right place. In this post, we will share a variety of machine learning topics that you can explore to boost your knowledge and skills. So, whether you’re a data scientist or machine learning engineer, there’s something here for everyone. The following represents a list of topics which can be taken up for learning and mastering artificial intelligence / machine learning: Introduction to data science Introduction to machine learning Check out this detailed post on machine learning concepts & examples. Introduction to deep learning Introduction to reinforcement learning Introduction to linear …

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What are Features in Machine Learning?

Features - Key to Machine Learning

Machine learning is a field of machine intelligence concerned with the design and development of algorithms and models that allow computers to learn without being explicitly programmed. Machine learning has many applications including those related to regression, classification, clustering, natural language processing, audio and video related, computer vision, etc. Machine learning requires training one or more models using different algorithms. Check out this detailed post in relation to learning machine learning concepts – What is Machine Learning? Concepts & Examples. One of the most important aspects of the machine learning model is identifying the features which will help create a great model, the model that performs well on unseen data. …

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Feature Scaling in Machine Learning: Python Examples

In this post you will learn about a simple technique namely feature scaling with Python code examples using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization or standardization. Feature scaling is generally performed during the data pre-processing stage, before training models using machine learning algorithms.  The goal is to …

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K-Nearest Neighbors (KNN) Python Examples

If you’re working with data analytics projects including building machine learning (ML) models, you’ve probably heard of the K-nearest neighbors (KNN) algorithm. But what is it, exactly? And more importantly, how can you use it in your own AI / ML projects? In this post, we’ll take a closer look at the KNN algorithm and walk through a simple Python example. 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. Stay tuned!  Introduction to K-Nearest Neighbors (K-NN) Algorithm K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists …

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What is Machine Learning? Concepts & Examples

what is machine learning

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 …

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Wilcoxon Rank Sum Test: Concepts, Examples

wilcoxon rank sum hypothesis explanation

The Wilcoxon rank sum test is a statistical test used to compare two sets of data. This test is also known as the Mann-Whitney U test. It is a non-parametric statistical hypothesis test used to compare two samples. It is similar to the Student’s t-test, but does not require the assumption of normality. The test is appropriate for use with small sample sizes.  What is Wilcoxon Rank Sum Test? The Wilcoxon rank sum test is a statistical test used to compare two independent samples. The test is used to compare the medians (location of medians) in the two samples. The null hypothesis is that the location of medians in two …

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