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https://digitalcollegelibrary.com/c-language-interview-questions/
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❤20👍9
800 plus SQL Swerver Interview Questions by Vikas Ahlawat.pdf
2.1 MB
800+ plus SQL server interview questions and answers 🔥🚀
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120+ Python Projects with source code
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CRACK_YOUR_NEXT_DATA_SCIENCE_INTERVIEW_With_These_100_Questions.pdf
12.1 MB
CRACK YOUR NEXT DATA SCIENCE INTERVIEW With These 100 Questions and Answers ✅
👍20❤3
Python code snippet for interviews.pdf
6.6 MB
Python code snippet for interviews 🔥🚀
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Ages of Operating Systems👨🏻💻😎
📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)
@coding_knwledge01
📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)
@coding_knwledge01
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Dynamic Programming-1.pdf
11.9 MB
✅ Dynamic programming Handwritten Notes part 1
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PySpark Notes.pdf
5.3 MB
PySpark Complete Notes ✅
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Websites to Practice Your Coding Skills🔥
📌 LeetCode
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❤45👍22👏4
10 Tools for SQL Developers 🛠📊 -
📄 SQL Server Management Studio (SSMS) - Manage and query SQL Server databases
🌐 phpMyAdmin - Web-based tool for MySQL database management
🔍 DBeaver - Universal database management tool
📊 Tableau - Data visualization and BI tool
⚙️ SQL Workbench/J - Cross-platform SQL query tool
🔐 pgAdmin - Management tool for PostgreSQL
🚀 Azure Data Studio - Lightweight and extensible data tool
📦 Toad for SQL - Database development and administration
📈 Datagrip - JetBrains SQL IDE for various databases
📂 HeidiSQL - Lightweight MySQL and MSSQL client
#SQLTools #DataAnalysis
📄 SQL Server Management Studio (SSMS) - Manage and query SQL Server databases
🌐 phpMyAdmin - Web-based tool for MySQL database management
🔍 DBeaver - Universal database management tool
📊 Tableau - Data visualization and BI tool
⚙️ SQL Workbench/J - Cross-platform SQL query tool
🔐 pgAdmin - Management tool for PostgreSQL
🚀 Azure Data Studio - Lightweight and extensible data tool
📦 Toad for SQL - Database development and administration
📈 Datagrip - JetBrains SQL IDE for various databases
📂 HeidiSQL - Lightweight MySQL and MSSQL client
#SQLTools #DataAnalysis
👍25❤7
AI/ML Roadmap👨🏻💻👾🤖 -
==== Step 1: Basics ====
📊 Learn Math (Linear Algebra, Probability).
🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
🔢 Clean & Visualize Data (Pandas, Matplotlib).
🏋️♂️ Learn Core Algorithms (Linear Regression, Decision Trees).
📦 Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
💡 Understand Neural Networks.
🖼️ Learn TensorFlow or PyTorch.
🤖 Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
🌳 Study Advanced Algorithms (Random Forest, XGBoost).
🗣️ Dive into NLP or Computer Vision.
🕹️ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
🎨 Create real-world projects.
🌍 Deploy with Flask, FastAPI, or Cloud Platforms.
#techinfo @coding_knwledge01
==== Step 1: Basics ====
📊 Learn Math (Linear Algebra, Probability).
🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
🔢 Clean & Visualize Data (Pandas, Matplotlib).
🏋️♂️ Learn Core Algorithms (Linear Regression, Decision Trees).
📦 Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
💡 Understand Neural Networks.
🖼️ Learn TensorFlow or PyTorch.
🤖 Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
🌳 Study Advanced Algorithms (Random Forest, XGBoost).
🗣️ Dive into NLP or Computer Vision.
🕹️ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
🎨 Create real-world projects.
🌍 Deploy with Flask, FastAPI, or Cloud Platforms.
#techinfo @coding_knwledge01
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