AI Councel Lab

  In the world of data science, text analytics plays a crucial role in deriving insights from large volumes of unstructured text data. Whether you're analyzing customer feedback, social media posts, or web articles, natural language processing (NLP) can help you extract meaningful information. One interesting challenge in text analysis involves handling HTML content, extracting meaningful text, and performing sentiment analysis based on predefined positive and negative word lists. In this blog post, we will dive into how to use Python and NLP techniques to analyze text data from HTML links, filter out stop words, and calculate various metrics such as positive/negative ratings, article length, and average sentence length. Prerequisites To follow along with the examples in this article, you need to have the following Python packages installed: requests (to fetch HTML content) beautifulsoup4 (for parsing HTML) nltk (for natural language processing tasks) re (for regular exp...

In today’s fast-paced digital world, creating personalized experiences for customers is essential. One of the most effective ways to achieve this is through a Product Recommender System . By using Data Science , we can build systems that not only predict what users may like but also optimize sales and engagement. Here's how we can leverage ETL from Oracle , SQL , Python , and deploy on AWS to create an advanced recommender system. Steps to Build the Best Product Recommender System: 1. ETL Process with Oracle SQL The foundation of any data-driven model starts with collecting clean and structured data. ETL (Extract, Transform, Load) processes from an Oracle Database help us extract relevant product, customer, and transaction data. SQL Query Example to Extract Data: SELECT product_id, customer_id, purchase_date, product_category, price FROM sales_data WHERE purchase_date BETWEEN '2023-01-01' AND '2023-12-31'; This query fetches historical sales data, includin...

In the world of machine learning, neural networks play a crucial role in solving complex problems. They have shown remarkable performance in various domains, from image classification to natural language processing. However, one of the fundamental tasks that neural networks can perform is regression —predicting continuous values based on input features. In this blog post, we'll explore three types of neural network models— Artificial Neural Networks (ANN) , Recurrent Neural Networks (RNN) , and Convolutional Neural Networks (CNN) —and discuss how they can be used for regression tasks. Additionally, we'll walk through code examples and explain how to train these models for regression problems. What is Regression? Regression is a type of supervised learning where the model is trained to predict continuous values. Common examples of regression tasks include predicting house prices, stock market trends, or temperature forecasting. The primary goal is to find the best-fit line (...

Large Language Models (LLMs) have revolutionized the AI and machine learning landscape by enabling applications ranging from chatbots and virtual assistants to code generation and content creation. These models, which are typically built on architectures like GPT, BERT, and others, have become integral in industries that rely on natural language understanding and generation. In this blog post, we’ll walk you through the steps involved in building and deploying a large language model using popular tools and frameworks such as AWS Generative AI, LangChain, Llama, and Hugging Face. What Are Large Language Models (LLMs)? LLMs are deep learning models designed to process and generate human language. Trained on vast amounts of text data, they have the ability to understand context, answer questions, translate languages, and perform other text-based tasks. Some key attributes of LLMs: Transformers : LLMs are generally based on transformer architecture, which allows the model to focus o...

  In today’s data-driven world, the ability to analyze data is a crucial skill for businesses, researchers, and professionals across various industries. Whether you're working with sales data, customer feedback, or scientific research, analyzing data effectively can uncover valuable insights and help guide decision-making. But with so much data available, it’s easy to feel overwhelmed. Where do you start? What tools should you use? How do you ensure the analysis is accurate and meaningful? This step-by-step guide will walk you through the essential stages of analyzing data, whether you're a beginner or looking to refine your skills. Step 1: Define Your Objective Before diving into data analysis, it’s important to clearly understand why you’re analyzing the data in the first place. Having a well-defined objective ensures that your analysis is focused and relevant. Ask yourself these questions: What problem are you trying to solve? What specific questions do you want to answer...

In today’s competitive and fast-paced marketing landscape, businesses are constantly seeking ways to optimize their strategies, engage with customers more effectively, and increase ROI. Enter data science , which has proven to be a powerful tool in transforming marketing practices. By leveraging data, machine learning, and artificial intelligence (AI), marketers can extract valuable insights, predict trends, and enhance decision-making. This article will explore 25 use cases of data science in marketing and illustrate how it can help companies unlock new opportunities and drive better outcomes. 1. Customer Segmentation What it is: Data science enables marketers to categorize customers based on shared traits, behaviors, or preferences, which allows for more targeted and personalized campaigns. Example: By analyzing purchasing history and browsing patterns, data science tools can create customer segments, enabling businesses to deliver tailored marketing messages for each group....