http://ghafoorsblog.com/tags/statistics/Recent content in Statistics on Ghafoor's Personal BlogHugoennoreply@example.com (AG Sayyed)noreply@example.com (AG Sayyed)Copyright © 2024-2026 AG Sayyed. All Rights Reserved.Thu, 16 Apr 2026 17:23:48 +0100http://ghafoorsblog.com/courses/ibm/ml-content/ml-pcert/01-data-analysis-for-ml/02-module/002-retrieving-data-part-2/Sun, 30 Mar 2025 20:47:51 +0000noreply@example.com (AG Sayyed)http://ghafoorsblog.com/courses/ibm/ml-content/ml-pcert/01-data-analysis-for-ml/02-module/002-retrieving-data-part-2/<p class="lead text-primary"> This document explains methods for retrieving data from SQL and NoSQL databases, APIs, and Cloud data sources, highlighting practical considerations and Python code examples for seamless data integration. </p> <hr> <h2 id="retrieving-data-from-different-sources">Retrieving Data from Different Sources</h2> <ol> <li><strong>SQL Databases</strong>: Structured Query Language databases are relational databases with fixed schemas. They are widely used for data storage and retrieval.</li> <li><strong>NoSQL Databases</strong>: Non-relational databases that offer flexibility in data storage and retrieval. They are often faster and more scalable than SQL databases.</li> <li><strong>APIs</strong>: Application Programming Interfaces allow access to data from various providers, enabling seamless integration with external data sources.</li> <li><strong>Cloud Data Sources</strong>: Cloud platforms provide data storage and retrieval services, allowing users to access data from anywhere with an internet connection.</li> </ol> <h2 id="working-with-sql-databases">Working with SQL Databases</h2> <p>SQL (Structured Query Language) databases are relational databases with fixed schemas. Examples include Microsoft SQL Server, Postgres, MySQL, AWS Redshift, Oracle DB, and IBM Db2. Python libraries such as <code>sqlite3</code>, <code>SQLAlchemy</code>, <code>Psycopg2</code> (for Postgres), and <code>ibm_db</code> (for Db2) can be used to connect to these databases.</p>http://ghafoorsblog.com/courses/ibm/ml-content/ml-pcert/01-data-analysis-for-ml/01-module/002-ml-deep-learning/Wed, 20 Nov 2024 03:33:25 +0000noreply@example.com (AG Sayyed)http://ghafoorsblog.com/courses/ibm/ml-content/ml-pcert/01-data-analysis-for-ml/01-module/002-ml-deep-learning/<p class="lead text-primary"> This document provides an overview of Machine Learning (ML) and Deep Learning (DL), including their definitions, differences, and applications. It also discusses the importance of features and targets in ML, as well as the challenges associated with image data. The document concludes with a brief introduction to the history of AI and its evolution into the current state of technology. </p> <hr> <h2 id="1-introduction-to-machine-learning">1. Introduction to Machine Learning</h2> <p>Machine Learning (ML) is a branch of Artificial Intelligence (AI) focused on developing algorithms that learn from data over time, rather than being explicitly programmed. By analysing data, ML algorithms identify patterns and improve their performance as more data becomes available. However, after a certain point, the performance gains from additional data diminish, leading to a plateau. Additionally, ML algorithms can be categorized into different types based on their learning approach, such as supervised, unsupervised, and reinforcement learning.</p>