Tag Archives: ai
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 …
Linear Regression Explained with Real Life Example
In this post, the linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post on what is machine learning – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models (mathematical models or equations) for solving supervised learning problems related to predicting continuous numerical value. Supervised learning problems …
AI Product Manager Interview Questions
AI has become such an integral part of our lives that it is important to hire professionals who can help create AI / machine learning products that will be used by many people. These AI product manager interview questions will give you insight into your product manager candidate’s experience, skills, and industry knowledge so that you can get prepared in a better manner before appearing for your next interview as an AI product manager. Check out a detailed interview questions and answers with greater focus on machine learning topics. Before getting into the list of interview questions, lets understand what can be the job description of an AI product manager. …
Building AI-powered Organization & Cultural Traits
Artificial Intelligence (AI) has become an integral part of many organizations’ operations. From customer service to supply chain management, AI is increasingly being used to automate and streamline processes. However, AI can do more than just help you run your business more efficiently; it can also be used to build organizational culture and foster data-driven decision making in general while leveraging analytical tools & techniques. Let’s take a look at how AI-powered organizational and cultural traits can help improve the workplace. The following picture is a summary of cultural traits in AI-driven organization Be Curious The adoption of artificial intelligence (AI) within an organization can enhance curiosity in several ways. …
Challenges for Machine Learning / AI Projects
In this post, you will learn about some of the key challenges in relation to achieving successful AI / machine learning (ML) or Data science projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers, etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business. Whether you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges. Understanding the Business Problem Many times, the nature …
Supply chain management & Machine Learning
As supply chains become more complex, businesses are looking for new ways to optimize and automate their supply chain operations. One area that is seeing a lot of growth is the use of artificial intelligence (AI) and machine learning in supply chain management. There are many different applications for these technologies in supply chain management, from forecasting demand to optimizing inventory levels. In this blog post, we will explore some of the most interesting use cases for AI and machine learning in supply chain management. What is supply chain management and what are its key components? Supply chain management is the process of coordinating and controlling the flow of goods, …
Weak Supervised Learning: Concepts & Examples
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 …
Healthcare Claims Processing AI Use Cases
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 …
ESG & AI / Machine Learning 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 …
Predicting Customer Churn with Machine Learning
Customer churn, also known as customer attrition, is a major problem for businesses that rely on recurring revenue. Customer churn costs businesses billions of dollars every year, and it’s only getting worse as customers become more and more fickle. In fact, it’s been estimated that the average company loses 10-15% of its customers each year. That number may seem small, but it can have a huge impact on a company’s bottom line. Fortunately, there’s a way to combat churn: by using machine learning to predict which customers are likely to churn. In this blog post, we’ll discuss how customer churn prediction works and why it’s so important. We’ll also provide …
Data-centric vs Model-centric AI: Concepts, Examples
There is a lot of discussion around AI and which approach is better: model-centric or data-centric. In this blog post, we will explore both approaches and give examples of each. We will also discuss the benefits and drawbacks of each approach. By the end of this post, you will have a better understanding of both AI approaches and be able to decide which one is right for your business! As product managers and data science architects, you should be knowledgeable about both of these AI approaches so that you can make informed decisions about the products and services you build. Model-centric approach to AI Model-centric approach to AI is about …
Hypothesis Testing Steps & Real Life 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. 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, and, how to formulate them. The knowledge of hypothesis formulation and hypothesis testing holds the key …
Insurance Machine Learning Use Cases
As insurance companies face increasing competition and ever-changing customer demands, they are turning to machine learning for help. Machine learning / AI can be used in a variety of ways to improve insurance operations, from developing new products and services to improving customer experience. It would be helpful for product manager and data science architects to get a good understanding around some of the use cases which can be addressed / automated using machine learning / AI based solutions. In this blog post, we will explore some of the most common insurance machine learning / AI use cases. Stay tuned for future posts that will dive into each of these …
Invoice Processing Machine Learning Use Cases
Invoice processing is a critical part of any business. It’s the process of creating, managing, and paying invoices. Without invoice processing, businesses would have a difficult time keeping track of their finances. There are many different invoice processing use cases. For example, businesses can use invoice processing to keep track of customer payments, manage vendor contracts, and streamline their accounting processes. Invoice processing can also be used to detect fraud and prevent errors. Machine learning / AI can be used to improve invoice processing in a number of ways. As a product manager, it will be helpful to understand these use cases and how machine learning can be used to …
Loan Eligibility Prediction using Machine Learning
It is no secret that the loan industry is a multi-billion dollar industry. Lenders make money by charging interest on loans, and borrowers want to get the best loan terms possible. In order to qualify for a loan, borrowers are typically required to provide information about their income, assets, and credit score. This process can be time consuming and frustrating for both lenders and borrowers. In this blog post, we will discuss how AI / machine learning can be used to predict loan eligibility. As data scientists, it is of great importance to understand some of challenges in relation to loan eligibility and how machine learning models can be built …
Healthcare & Machine Learning Use Cases / Projects
AI & Machine learning is being used more and more in the healthcare industry. This is because it has the potential to improve patient outcomes, make healthcare more cost-effective, and help with other important tasks. In this blog post, we will discuss some of the healthcare & AI / machine learning use cases that are currently being implemented. We will also talk about the benefits of using machine learning in healthcare settings. Stay tuned for an exciting look at the future of healthcare! What are top healthcare challenges & related AI / machine learning use cases? Before getting into understand how machine learning / AI can be of help in …
