Degree IT Academy

  AI in Commerce – Transforming the Consumer Experience Artificial Intelligence (AI) is transforming modern commerce by improving customer experiences and helping businesses make smarter decisions. AI enables personalized shopping, automates customer support, predicts future demand, and optimizes inventory management. Introduction AI in commerce refers to the use of intelligent computer systems to automate business processes, understand customer behavior, and improve decision-making. Popular e-commerce platforms like Amazon and Flipkart use AI to recommend products, answer customer queries, and forecast market demand. Recommendation Engines Recommendation engines suggest products based on customer interests. Collaborative Filtering This technique recommends products based on the purchasing behavior of similar users. Content-Based Filtering This method recommends products that are similar to those a customer has already liked or purchased. Chatbots and Virtual Assistants ...

Feeding AI Models in Artificial Intelligence | Easy Tutorial  Feeding an AI Model:  Artificial Intelligence has become an essential part of modern technology. From voice assistants to recommendation systems, AI powers many of the tools we use every day. However, an AI model cannot perform intelligent tasks unless it is trained with data. This process is known as feeding an AI model . What is Feeding an AI Model? Feeding an AI model is the process of providing data so that the model can learn patterns, identify relationships, and make predictions. The better the quality of the data, the better the AI model performs. For example, if we want an AI system to recognize cats, we provide it with thousands of cat images. By studying these images, the model learns the common characteristics of cats and can identify new images accurately. Steps in Feeding an AI Model The process begins with collecting relevant data from reliable sources. After collection, the data is cleaned by re...

  What is Data Transformation?     Before using data in machine learning, it often needs preparation.     Data Transformation is the process of converting data into a suitable format for analysis and machine learning.     It helps improve data quality and makes machine learning models more effective.     Data Transformation Techniques, including Normalization, Data Transformation, and Feature Engineering.     These techniques help prepare data for machine learning and improve model accuracy. 1.    Normalization     Normalization is a technique used to scale numerical values into a common range, usually between 0 and 1.     For example, if age values range from 18 to 60, normalization converts them into values within a smaller and consistent range.     This prevents larger numbers from dominating smaller numbers and improves model performanc...