Module : 3 Get Started with Machine Learning in Azure Machine learning drives today’s AI by turning data into insights for predictions and recommendations. Creating an ML solution requires key decisions that affect cost, speed, and quality. Using Microsoft Azure, the process follows six main steps: Define the Problem: Decide what to predict and how to measure success. Get the Data: Find and access reliable data sources. Prepare the Data: Clean, explore, and format the data for modeling. Train the Model: Choose algorithms and adjust parameters. Integrate the Model: Deploy it to generate predictions. Monitor the Model: Track performance and retrain as needed. Machine learning is an ongoing process. Models evolve as data changes. Step 1: Define the Problem The first step in building a machine learning (ML) solution is to define the problem. You need to clearly understand what you want the model to predict and how you will measure success. Key Considerations Model output: What result shoul...
Module : 12 Overview of Information Extraction * Information extraction is a process that uses AI to pull structured data from unstructured or semi-structured documents, like receipts, invoices, forms, and scanned files. It combines computer vision, OCR, and machine learning or generative AI. * Key Steps in an Information Extraction Pipeline 1. Text Detection & Extraction (OCR) - It uses computer vision to find text areas in an image. - The system extracts raw text from scanned or image-based documents. 2. Value Identification & Mapping It maps the OCR text to specific data fields using: - Machine learning - Rule-based mapping - Generative AI (in newer solutions) Example From a scanned receipt, the system extracts fields such as: - Vendor - Date - Subtotal - Tax - Total * Choosing the Right Approach When designing an information extraction solution, consider: Document Characteristics - Layout consistency - Fixed templates lead to simple, rule-based extraction - Many forma...
Module : 10 Introduction to Computer Vision Concepts Computer Vision Tasks & Techniques * What is Computer Vision? Computer vision uses AI techniques to process and understand visual input such as: - Images - Videos - Live camera streams - It enables machines to "see" and interpret visual information. 1. Image Classification - This predicts a single label for an entire image. - The model is trained using many labeled images. - For example, a smart grocery checkout can identify an apple, orange, or banana from a single item placed on a scale. - Use case: Recognizing the main subject in an image. 2. Object Detection - This identifies multiple objects in a single image. It returns: - Object labels - Bounding box coordinates - For example, a checkout system can detect all fruits placed together....
Comments
Post a Comment