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Mumtaz Academy / ሙምታዝ አካዳሚ Building Knowledge, Character & Future Leaders. Mumtaz Academy invites qualified, dedicated, and hardworking professionals to join our team. Position: 1. Amharic Teacher - 3 Vacancies ※ Requirement: Amharic teaching and classroom management skills 2. PAV Teacher - 3 Vacancies ※ Requirement: PAV teaching and student support skills 3. ICT Teacher - 3 Vacancies ※ Requirement: ICT teaching and classroom management skills 4. Secretary - 3 Vacancies ※ Requirement: School administration and students’ roster management experience General Requirements (All Positions) ※ Qualification: Diploma and above ※ Experience: 2+ years How to Apply? Telegram: t.me/MuhammedYimam1 Phone: +251 913 412 926 Location: Kara Kore, behind Mabrathayl Salary: Attractive and negotiable based on qualifications, experience, and position. For more information: +251913412926 NB: Read The Details Carefully! #ለእርስዎ_ካልሆነ_ለሌሎች_ያጋሩ! === Halal Jobs ቴሌግራም👉 t.me/HalalJobsEth

🧭 QUICK PROMPT-SELECTION GUIDE Which ChatGPT Prompt Should You Use? Choose the prompt that matches your current challenge. I want to improve my overall problem-solving ability. → Prompt 1: Become a Better Problem Solver 🧩 I want to think logically and question assumptions. → Prompt 2: Master Critical Thinking 🔍 I feel overwhelmed by a complicated problem. → Prompt 3: Break Down Complex Problems ⚙️ I need fresh ideas or creative solutions. → Prompt 4: Unlock Creative Thinking 💡 I have several options and need to make a decision. → Prompt 5: Make Smarter Decisions 🎯 I keep solving the same problem repeatedly. → Prompt 6: Find the Root Cause of Problems 🌱 I want to plan ahead and anticipate consequences. → Prompt 7: Think Strategically Like an Expert ♟️ Something is not working, and I need to find out why. → Prompt 8: Improve Troubleshooting Skills 🛠️ I need to solve a practical problem at work, school, or in daily life. → Prompt 9: Solve Real-Life Problems More Effectively 🌍 I want to build my skills through consistent practice. → Prompt 10: Train Your Brain with Daily Challenges 🚀 ⚡ THE 3-STEP RULE Step 1: Choose the prompt that matches your problem. Step 2: Think through the challenge yourself before asking ChatGPT for the answer. Step 3: Improve your reasoning using feedback, hints, and follow-up exercises. 🎯 NOT SURE WHICH PROMPT TO CHOOSE? Start with Prompt 1 to assess your current abilities. Then use the other prompts to strengthen specific skills. Remember: Don't just ask ChatGPT to solve problems for you. Ask it to help you become the person who can solve them independently.

Goal: Apply problem-solving skills to real situations. Prompt: Be my practical problem-solving mentor. Give me realistic challenges involving teaching, communication, time management, teamwork, technology, and everyday responsibilities. Ask me to analyze each situation, identify constraints, consider different perspectives, and propose solutions. Evaluate my reasoning, practicality, ethics, and likely results. Help me revise weak solutions and apply the lessons to new scenarios. 10. Train Your Brain with Daily Challenges 🚀 Goal: Build lasting problem-solving habits. Prompt: Create a personalized 30-day problem-solving training program for me. Give me one challenge each day, progressing from beginner to advanced. Include logical puzzles, real-world cases, decision-making, creative thinking, and complex problems. Require me to explain my reasoning before receiving feedback. Track my performance using clear criteria, revisit my weaknesses, and give me weekly assessments with targeted exercises. 🏆 HOW TO GET THE BEST RESULTS 1. Think Before You Ask for Answers. Attempt every challenge yourself first. Your effort is what develops your reasoning skills. 2. Explain Your Reasoning. Tell ChatGPT not only what you decided, but why you decided it. 3. Learn from Mistakes. Ask for feedback, revise your answer, and try a similar problem. 4. Measure Your Progress. Review your performance weekly and focus on the skills that need improvement. 5. Practice Consistently. Start with 15–20 minutes daily. Consistent practice is more valuable than trying to master everything in one day. 💎 FINAL ADVICE Start with Prompt 1, then practice Prompts 3, 5, and 6 to deepen your skills. Remember: The goal is not to depend on ChatGPT to solve your problems. The goal is to train yourself to solve problems better—even without ChatGPT. 🧠💪

🧠 TOP 10 CHATGPT PROMPTS TO IMPROVE YOUR PROBLEM-SOLVING SKILLS Think Smarter • Analyze Better • Solve Problems Creatively Use these 10 powerful ChatGPT prompts to train your brain, improve critical thinking, make better decisions, and solve everyday and professional problems. 1. Become a Better Problem Solver 🧩 Goal: Build your overall problem-solving ability. Prompt: Act as my personal problem-solving coach. Assess my current skills through realistic scenarios. Ask me one problem at a time, let me explain my reasoning, identify weaknesses in my approach, and teach me how to improve. Give hints instead of immediate answers, introduce effective frameworks, and gradually increase the difficulty as I improve. Start with my first challenge. 2. Master Critical Thinking 🔍 Goal: Think logically and avoid misleading conclusions. Prompt: Act as my critical-thinking trainer. Give me real-world claims, arguments, and situations to analyze. Ask me to identify facts, assumptions, evidence, biases, and logical errors. Challenge my conclusions with counterarguments. Do not reveal the answer until I have explained my reasoning. Help me develop stronger, evidence-based thinking. 3. Break Down Complex Problems ⚙️ Goal: Turn overwhelming problems into manageable steps. Prompt: Teach me to break complex problems into smaller, manageable parts. Give me a challenging situation and coach me to define the real problem, identify its components, determine priorities, and create an actionable step-by-step plan. Introduce techniques such as decomposition, the 5 Whys, and root-cause analysis. Let me try each step before giving feedback. 4. Unlock Creative Thinking 💡 Goal: Discover original ideas and alternative solutions. Prompt: Act as my creative-thinking coach. Present problems that have several possible solutions. Help me challenge conventional assumptions, use brainstorming, reverse thinking, analogies, and the SCAMPER technique. Ask me to generate at least five possible solutions before evaluating them. Encourage originality while helping me distinguish useful ideas from impractical ones. 5. Make Smarter Decisions 🎯 Goal: Compare options and choose wisely. Prompt: Act as my decision-making coach. Give me realistic decisions involving competing priorities, limited resources, uncertainty, and possible risks. Teach me to define my objective, establish criteria, compare alternatives, estimate consequences, and recognize cognitive biases. Ask me to choose and justify my decision before showing how an experienced decision-maker might approach it. 6. Find the Root Cause of Problems 🌱 Goal: Solve the real problem instead of treating symptoms. Prompt: Teach me root-cause analysis through realistic problems from education, technology, work, and daily life. Guide me through the 5 Whys, cause-and-effect analysis, and evidence gathering. Ask questions that help me distinguish symptoms from underlying causes. After I propose a cause, challenge it and help me test whether the evidence supports my explanation. 7. Think Strategically Like an Expert ♟️ Goal: Anticipate consequences and plan ahead. Prompt: Act as my strategic-thinking mentor. Give me scenarios in which my choices affect future outcomes. Train me to consider short-term and long-term consequences, second-order effects, risks, opportunities, and other people's possible responses. Ask me to develop multiple strategies and anticipate what could go wrong. Reveal how an expert might plan only after I present my approach. 8. Improve Troubleshooting Skills 🛠️ Goal: Diagnose errors systematically. Prompt: Act as my troubleshooting coach. Present realistic technical and everyday problems with incomplete information. Train me to gather evidence, form hypotheses, test one possibility at a time, interpret results, and verify the final solution. Do not jump to conclusions or give me the fix immediately. Provide hints when needed and explain how to avoid repeating the same mistake. 9. Solve Real-Life Problems More Effectively 🌍

Mumtaz Academy / ሙምታዝ አካዳሚ Position: 1. School Secretaries ♦️Quantity: 4 ※ Salary: Attractive & Negotiable - Based on Qualification, Experience and position. Location: Kera Kore, Behind Mebrathayl How to Apply: Telegram: t.me/MuhammedYimam1 Phone: +251913412926 N.B. Read The Details Carefully! #ለእርስዎ_ካልሆነ_ለሌሎች_ያጋሩ!

chatGPTን የሆነ ነገር መጠየቅ ፈልጋችሁ ነገር ግን ትክክለኛ prompt ማስገባት ከከበዳችሁ በዚ መሰረት ብታስተካክሉት ራሱ አስተካክሎ ትክክለኛ መልስ ይሰጣችኋል። chatGPT ክፈቱና 2 ሰረዙን ነክታችሁ የProfile ምልክታችሁን ነክታችሁ ግቡ። ከዛ personalization የሚለውን ምረጡ። መጨረሻ ላይ ታች improved version የሚል ቦታ ታገኛላችሁ ይህንን ወይም ሌላ የተመቻችሁን ትዕዛዝ አስገቡ።

Before answering any question I ask, please correct my grammar and improve my prompt to make it clearer, more natural, and well-structured...

የ✔️ ምልክት ነክታችሁ ከወጣችሁ በኋላ ማንኛውም ጥያቄ በምትጠይቁበት ጊዜ መጀመርያ promptን ካስተካከለ በኋላ መልስ ይሰጣችኋል። 🔥🔥🔥🔥🔥🔥🔥🔥 ⚫Telegram Premium ⚫Gemini Pro 18 months የምትፈልጉ ከሆነ @bighabesha ላይ inbox አድርጉልኝ። @bighabesha_softwares

ጠቃሚ የGmail ሚስጥሮች 1. Confidential email Gmail ስትልኩ የምትልኩላቸው ሰዎች ኢሜሉን copy, forward ወይም screenshot እንዳያደርጉት ከፈለጋቸው confidential mode on አድርጋቸሁ መላክ ትችላላችሁ። ይህን ለማድረግ compose የሚለውን ነክታችሁ ኢሜሉን ከፃፋችሁ በኋላ 3 ነጥቡን ነክታችሁ confidential mode የሚለውን ትመርጣላችሁ። ከዛ expire የሚያደርግበትን የጊዜ ገደብና የpasscode አማራጭ መርጣችሁ save አድርጋችሁ ትልኩላቸዋላችሁ። 2. Lebel ሙሉ ሚሴጁን ፊትለፊት ከምትደረድሩት በጣም አስፈላጊ የሆኑትን ለይታችሁ label ማድረግ ትችላላችሁ። ይህን ለማድረግ 3 ሰረዙን ነክታችሁ create label የሚለውን በመምረጥ label create ታደርጋላችሁ። ከዛ ዋናው የGmail list ውስጥ ገብታችሁ label ማድረግ የምትፈልጉትን message ትመርጡና 3 ነጥቡን ነክታችሁ  label as ብላችሁ የምትፈልጉት label ውስጥ ማስገባት ትችላላችሁ። 3. Keywords Search bar ላይ specific የሆኑ የተለያዩ ኪ-ዎርዶችን በማስገባት ሙሉ message filter ማድረግ ትችላላችሁ። ለምሳሌ "has:attachment" ብላችሁ search ብታደርጉ የተለያዩ የፋይል attachment ያላቸውን ኢሜሎች filter ያደርግላችኋል። ሌሎች ተጨማሪ keywords ⚫ from:client@example.com = Emails from a sender ⚫ to:client@example.com = Emails you sent to someone ⚫ subject:proposal = Emails with a word in the subject ⚫ has:attachment = Emails with attachments ⚫ filename:pdf = Emails with PDF attachments ⚫ larger:10M = Emails larger than 10 MB ⚫ older_than:1y = Emails older than a year ⚫ after:2026/09/01 before:2026/10/01 = Emails within this date range ⚫ is:unread = Unread emails ⚫ is:starred = Starred emails ⚫ in:snoozed = Snoozed emails

ጠቃሚ የGmail ሚስጥሮች 1. Confidential email Gmail ስትልኩ የምትልኩላቸው ሰዎች ኢሜሉን copy, forward ወይም screenshot እንዳያደርጉት ከፈለጋቸው confidential mode on አድርጋቸሁ መላክ ትችላላችሁ። ይህን ለማድረግ compose የሚለውን ነክታችሁ ኢሜሉን ከፃፋችሁ በኋላ 3 ነጥቡን ነክታችሁ confidential mode የሚለውን ትመርጣላችሁ። ከዛ expire የሚያደርግበትን የጊዜ ገደብና የpasscode አማራጭ መርጣችሁ save አድርጋችሁ ትልኩላቸዋላችሁ። 2. Lebel ሙሉ ሚሴጁን ፊትለፊት ከምትደረድሩት በጣም አስፈላጊ የሆኑትን ለይታችሁ label ማድረግ ትችላላችሁ። ይህን ለማድረግ 3 ሰረዙን ነክታችሁ create label የሚለውን በመምረጥ label create ታደርጋላችሁ። ከዛ ዋናው የGmail list ውስጥ ገብታችሁ label ማድረግ የምትፈልጉትን message ትመርጡና 3 ነጥቡን ነክታችሁ label as ብላችሁ የምትፈልጉት label ውስጥ ማስገባት ትችላላችሁ። 3. Keywords Search bar ላይ specific የሆኑ የተለያዩ ኪ-ዎርዶችን በማስገባት ሙሉ message filter ማድረግ ትችላላችሁ። ለምሳሌ "has:attachment" ብላችሁ search ብታደርጉ የተለያዩ የፋይል attachment ያላቸውን ኢሜሎች filter ያደርግላችኋል። ሌሎች ተጨማሪ keywords ⚫from:client@example.com = Emails from a sender ⚫to:client@example.com = Emails you sent to someone ⚫subject:proposal = Emails with a word in the subject ⚫has:attachment = Emails with attachments ⚫filename:pdf = Emails with PDF attachments ⚫larger:10M = Emails larger than 10 MB ⚫older_than:1y = Emails older than a year ⚫after:2026/09/01 before:2026/10/01 = Emails within this date range ⚫is:unread = Unread emails ⚫is:starred = Starred emails ⚫in:snoozed = Snoozed emails @bighabesha_softwares

MS-Word Full Course Part 4 በቲክቶክ አካውንቲችን ተለቋል። ተጠቀሙበት። https://vt.tiktok.com/ZSbU4puPV/

🤖 MACHINE LEARNING ROADMAP From Fundamentals → Intelligent Solutions 01. FUNDAMENTALS Mathematics Linear Algebra Calculus — Gradients & Optimization Probability & Statistics Matrix Operations Programming Python — NumPy, Pandas, Scikit-learn R — Optional for Statistical Modeling SQL — Data Extraction 02. DATA PREPROCESSING Data Cleaning Feature Engineering Encoding Categorical Data Feature Scaling Standardization Normalization Handling Missing Values Dimensionality Reduction PCA LDA 03. SUPERVISED LEARNING Regression Linear Regression Polynomial Regression Ridge & Lasso Regression Classification Logistic Regression Decision Trees Support Vector Machines — SVM Ensemble Methods Random Forest Gradient Boosting XGBoost 04. UNSUPERVISED LEARNING Clustering K-Means Hierarchical Clustering DBSCAN Dimensionality Reduction Principal Component Analysis — PCA t-SNE Association Rules Apriori FP-Growth 05. REINFORCEMENT LEARNING Markov Decision Processes — MDPs Q-Learning Deep Q-Learning Policy Gradient Methods 06. MODEL EVALUATION & OPTIMIZATION Model Validation Train-Test Split Cross-Validation Performance Metrics Accuracy Precision Recall F1-Score ROC-AUC Mean Squared Error — MSE R-Squared Hyperparameter Tuning Grid Search Random Search Bayesian Optimization 07. DEEP LEARNING Neural Networks Perceptrons Backpropagation Convolutional Neural Networks — CNN Image Classification Object Detection YOLO SSD Recurrent Neural Networks — RNN LSTM GRU Transformers Attention Mechanisms BERT GPT Tools & Frameworks TensorFlow PyTorch 08. ADVANCED TOPICS Transfer Learning Generative Adversarial Networks — GANs Reinforcement Learning with Neural Networks Explainable AI — XAI SHAP LIME 09. APPLICATIONS OF MACHINE LEARNING Recommender Systems Collaborative Filtering Content-Based Filtering Fraud Detection Sentiment Analysis Predictive Maintenance Autonomous Vehicles 10. DEPLOYMENT OF MODELS APIs & Web Deployment Flask FastAPI Cloud Deployment AWS SageMaker Azure Machine Learning Containerization Docker Kubernetes Production Management Model Monitoring Model Retraining 🚀 THE LEARNING PATH Fundamentals ↓ Data Preprocessing ↓ Supervised Learning ↓ Unsupervised Learning ↓ Reinforcement Learning ↓ Model Evaluation & Optimization ↓ Deep Learning ↓ Advanced Topics ↓ ML Applications ↓ Model Deployment 📱 CONNECT WITH US TikTok: www.tiktok.com/@techaihub2 Telegram: t.me/TechAIHub1 Facebook: www.facebook.com/TechandAIHub 💡 TECH & AI HUB Learn • Build • Innovate

Machine Learning Roadmap | |-- Fundamentals |   |-- Mathematics |   |   |-- Linear Algebra |   |   |-- Calculus (Gradients, Optimization) |   |   |-- Probability and Statistics |   |   |-- Matrix Operations |   | |   |-- Programming |   |   |-- Python (NumPy, Pandas, Scikit-learn) |   |   |-- R (Optional for Statistical Modeling) |   |   |-- SQL (For Data Extraction) | |-- Data Preprocessing |   |-- Data Cleaning |   |-- Feature Engineering |   |   |-- Encoding Categorical Data |   |   |-- Feature Scaling (Standardization, Normalization) |   |   |-- Handling Missing Values |   |-- Dimensionality Reduction (PCA, LDA) | |-- Supervised Learning |   |-- Regression |   |   |-- Linear Regression |   |   |-- Polynomial Regression |   |   |-- Ridge and Lasso Regression |   |-- Classification |   |   |-- Logistic Regression |   |   |-- Decision Trees |   |   |-- Support Vector Machines (SVM) |   |   |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost) | |-- Unsupervised Learning |   |-- Clustering |   |   |-- K-Means |   |   |-- Hierarchical Clustering |   |   |-- DBSCAN |   |-- Dimensionality Reduction |   |   |-- Principal Component Analysis (PCA) |   |   |-- t-SNE |   |-- Association Rules (Apriori, FP-Growth) | |-- Reinforcement Learning |   |-- Markov Decision Processes |   |-- Q-Learning |   |-- Deep Q-Learning |   |-- Policy Gradient Methods | |-- Model Evaluation and Optimization |   |-- Train-Test Split and Cross-Validation |   |-- Performance Metrics |   |   |-- Accuracy, Precision, Recall, F1-Score |   |   |-- ROC-AUC |   |   |-- Mean Squared Error (MSE), R-squared |   |-- Hyperparameter Tuning |   |   |-- Grid Search |   |   |-- Random Search |   |   |-- Bayesian Optimization | |-- Deep Learning |   |-- Neural Networks |   |   |-- Perceptrons |   |   |-- Backpropagation |   |-- Convolutional Neural Networks (CNN) |   |   |-- Image Classification |   |   |-- Object Detection (YOLO, SSD) |   |-- Recurrent Neural Networks (RNN) |   |   |-- LSTM |   |   |-- GRU |   |-- Transformers (Attention Mechanisms, BERT, GPT) |   |-- Tools and Frameworks (TensorFlow, PyTorch) | |-- Advanced Topics |   |-- Transfer Learning |   |-- Generative Adversarial Networks (GANs) |   |-- Reinforcement Learning with Neural Networks |   |-- Explainable AI (SHAP, LIME) | |-- Applications of Machine Learning |   |-- Recommender Systems (Collaborative Filtering, Content-Based) |   |-- Fraud Detection |   |-- Sentiment Analysis |   |-- Predictive Maintenance |   |-- Autonomous Vehicles | |-- Deployment of Models |   |-- Flask, FastAPI |   |-- Cloud Deployment (AWS SageMaker, Azure ML) |   |-- Containerization (Docker, Kubernetes) |   |-- Model Monitoring and Retraining