Ravi Kumar Blog Posts

Understanding the below mentioned algorithms is the first step towards leveraging the power of Machine Learning. Each has its own strengths and best-use scenarios. Explore and experiment to find the right fit for your data challenges. P.C: QuantumEdgeX Linear Regression   ->Used for predicting continuous values. ->Models the relationship between dependent and independent variables by fitting a linear equation. Logistic Regression   ->Ideal for binary classification problems. ->Estimates the probability that an instance belongs to a particular class. Decision Trees   ->Splits data into subsets based on the value of input features. ->Easy to visualize and interpret but can be prone to overfitting. Random Forest   ->An ensemble method using multiple decision trees. ->Reduces overfitting and improves accuracy by averaging multiple trees. Support Vector Machines (SVM)   ->Finds the hyperplane that best separates different classes. ->Effe...

As a part of this weekend activity, I asked Claude   to give me the steps to build a simple AI Agent   using Pega Infinity platform. For this exercise, Claude give me an idea on the agent which combines rule-based logic with Predictive Analytics   and NLP to deliver a automated solution. Not sure how far these steps can give us right result but I like this approach to build the agent in the realtime biz. Those who are interested can take a look at these 10 steps and plan to build this agent using Pega  platform 👍 Simple Example: Product Return Processing AI Agent This AI agent will: Receive product return requests Evaluate eligibility based on company policy Suggest appropriate actions (approve, deny, escalate) Generate appropriate responses to customers Track and log outcomes Step 1: Define Your AI Agent Requirements Business Problem: Streamline the product return process by automating eligibility checks and routine decisions. Business Objectives: Reduce manual...