Category Archives: Machine Learning

Car Insurance & Machine Learning Use Cases

car insurance machine learning use cases

The car insurance industry is one of the many sectors that have been disrupted by the advent of machine learning. In the past, car insurance companies have relied on historical data to set premiums. However, machine learning / AI has enabled insurers to better predict risk and price insurance policies more accurately. As a result, AI / machine learning is transforming the car insurance industry by making it more efficient and customer-centric. In this blog, you will learn about some key car insurance use cases which can be dealt using machine learning. Detecting fraudulent car insurance claims Fraudulent car insurance claims are a problem for both insurers and policyholders. They …

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Posted in Insurance, Machine Learning. Tagged with , .

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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Diabetes Detection & Machine Learning / AI

diabetes diagnosis using machine learning

Diabetes is a chronic disease that affects millions of people worldwide. The early detection of diabetes is crucial to preventing the development of serious complications. However, traditional methods of diabetes detection are often inaccurate and invasive. Machine learning / AI offers a promising solution for the early detection of diabetes. Machine learning algorithms can automatically detect patterns in data and use those patterns to make predictions. Machine learning is well suited for the detection of diabetes because it can handle the large amount of data required for accurate predictions. In addition, machine learning algorithms can automatically identify patterns that are too subtle for humans to discern.  Quick Overview on Machine …

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Posted in Healthcare, Machine Learning. Tagged with , .

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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Posted in AI, ESG, Machine Learning. Tagged with , , , .

Neural Network Types & Real-life Examples

deep neural network examples from real-life

Neural networks are a powerful tool for data scientists, machine learning engineers, and statisticians. But what exactly are they? In this blog post, we’ll explore the concept of different types of neural networks, provide real-life examples of how they’re used. By the end of this post, you’ll have a better understanding of how neural networks work and how they can be used to solve complex problems.  Before jumping into examples, you may want to check out some of my following posts on neural networks: Deep Learning Explained Simply in Layman Terms Neural network explained with perceptron example Perceptron explained with Python example Also, lets understand some terminologies which will later …

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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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Checklist for Training Deep Learning Models

training deep learning models checklist

Deep learning is a powerful tool for solving complex problems, but it can be difficult to get started. In this blog post, we’ll provide a checklist of things to keep in mind when training and evaluating the deep learning models and deciding whether they are suitable to deploy in production. By following this checklist, you can ensure that your models are well-trained and ready to tackle real-world tasks. Validation of data distribution The distribution of data can have a significant impact on the performance of deep learning models. When training a model, it is important to ensure that the training data is representative of the distribution of the data that …

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Posted in Data Science, Deep Learning, Machine Learning.

Machine Learning with Limited Labeled Data

machine learning with limited labeled data

One of the biggest challenges in machine learning is having enough labeled data to train a model. This is especially true for supervised learning tasks such as image classification, where a large dataset is often required. However, what do you do when you only have limited labeled data? In this blog post, we will discuss some of the following techniques that can be used to train machine learning models when you only have limited labeled data. Self-supervised learning Semi-supervised learning Weakly-supervised learning Active learning Few/zero-shot learning Transfer learning Challenges with Machine Learning models trained with limited labeled data When a machine learning model is trained with limited labeled data, it …

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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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Model Compression Techniques – Machine Learning

model compression technique for machine learning

In recent years, there has been an explosion of interest in machine learning (ML). This is due in large part to the availability of powerful and affordable hardware, as well as the development of new ML algorithms that are able to achieve state-of-the-art results on a variety of tasks. However, one of the challenges of using ML is that many algorithms require a large amount of data and computational resources in order to train a model that generalizes well to new data. To address this challenge, a number of model compression techniques have been developed that allow for the training of smaller, more efficient models that still achieve good performance …

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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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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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How to Identify Use Cases for AI / Machine Learning

identify ai and machine learning use cases

As artificial intelligence (AI ) and machine learning (ML) solutions and technologies continue to evolve, more and more businesses are looking for ways to incorporate them into their operations to realize a greater business impact. But with so many potential applications, it can be difficult to know where to start. In this blog post, we’ll outline some tips for identifying AI / ML use cases. We’ll also provide a few examples of how AI & machine learning can be used in business settings. So if you’re thinking about adding AI or machine learning to your toolkit, read on! This blog post will be appropriate for product managers, business analysts, data science …

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