Think Different

  At 50, I am proud of the extraordinary milestones I've achieved by solving and implementing complex, critical solutions that drive business success. 🌟 Experience has been my greatest teacher, and I continue to be driven by a passion for lifelong learning. I'm constantly updating my skills and knowledge to stay ahead in fields related to my work and future career. 📚✨ I thrive on taking on new domains and challenges and consistently deliver solutions faster than expected. Age is not a barrier—it's a testament to my dedication, expertise, and relentless pursuit of excellence. Even in short-term and contract opportunities, I've successfully delivered in different challenging environments, competing on an international stage. This has only strengthened my adaptability and problem-solving skills. I owe a great deal of my success to my mother, wife, and son. Their unwavering support and understanding have given me the time, effort, and focus to continue learning and achiev...

photo: pexel Are you new to SQL and preparing for your first job that requires SQL knowledge?  This blog post is designed specifically for beginners like you who are just starting out on their journey with SQL. Whether you're a student, a recent graduate, or someone looking to transition into a tech role, this guide will help you build a strong foundation in SQL concepts, understand common SQL queries, and prepare you for the types of SQL tasks you might encounter in your first job. We'll break down complex concepts into easy-to-understand steps, provide practical examples, and give you tips to succeed in interviews and on the job. Let's get you job-ready with SQL! Below is the table structure for the `customer`, `user_account`, and `contact` tables in a more visual format: Below are the SQL statements to create the `customer`, `user_account`, and `contact` tables according to the schema provided: 1. `customer` Table ```sql CREATE TABLE customer (     id INT PRIMARY KEY, ...

  12-Week + Project Study Plan for AI/ML: Module 1: Introduction to the AI Landscape (1 Week) Topics: Overview of AI, Data Analysis, Prediction, Decision Making, Causal Inference Activities: Lectures, readings, self-study Module 2: Data Exploration - Structured Data (2 Weeks) Topics: Data Exploration Techniques, Dimensionality Reduction (PCA), Clustering (K-means, DBSCAN) Activities: Hands-on exercises, data analysis projects, self-study Module 3: Prediction Methods - Regression (2 Weeks) Topics: Linear Regression, Model Evaluation, Regularization, Overfitting Activities: Hands-on exercises, building regression models, self-study Module 4: Decision Systems (2 Weeks) Topics: Decision Trees, Classification, Ensemble Learning (Random Forests) Activities: Hands-on exercises, building decision trees and random forests, self-study Module 5: Data Exploration - Unstructured Data (2 Weeks) Topics: Natural Language Processing (NLP), Sentim...