10 AI Side Hustles You Can Start Today
โ Prompt Engineering Services โ Craft prompts for businesses using ChatGPT or Claude
โ AI-Powered Resume Writer โ Help people optimize resumes using GPT + design tools
โ YouTube Script Generator โ Offer scriptwriting using LLMs for creators & influencers
โ AI Course Creation โ Build and sell niche courses powered by AI tools (ChatGPT + Canva)
โ Copywriting & SEO Services โ Use AI to generate blog posts, ad copy, and product descriptions
โ Newsletter Curation โ Launch an AI-generated niche newsletter using curated content
โ Chatbot Development โ Build custom AI chatbots for small businesses
โ Voiceover Generator โ Convert scripts into realistic voiceovers for YouTube shorts or reels
โ AI Art & Merch Store โ Design AI-generated art and sell it on print-on-demand platforms
โ Data Labeling & AI Testing โ Offer manual or semi-automated labeling to startups training models
React if youโre thinking of monetizing your AI skills!
#aiskills
โ Prompt Engineering Services โ Craft prompts for businesses using ChatGPT or Claude
โ AI-Powered Resume Writer โ Help people optimize resumes using GPT + design tools
โ YouTube Script Generator โ Offer scriptwriting using LLMs for creators & influencers
โ AI Course Creation โ Build and sell niche courses powered by AI tools (ChatGPT + Canva)
โ Copywriting & SEO Services โ Use AI to generate blog posts, ad copy, and product descriptions
โ Newsletter Curation โ Launch an AI-generated niche newsletter using curated content
โ Chatbot Development โ Build custom AI chatbots for small businesses
โ Voiceover Generator โ Convert scripts into realistic voiceovers for YouTube shorts or reels
โ AI Art & Merch Store โ Design AI-generated art and sell it on print-on-demand platforms
โ Data Labeling & AI Testing โ Offer manual or semi-automated labeling to startups training models
React if youโre thinking of monetizing your AI skills!
#aiskills
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐๐ป ๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐๐
1๏ธโฃ BCG Data Science & Analytics Virtual Experience
2๏ธโฃ TATA Data Visualization Internship
3๏ธโฃ Accenture Data Analytics Virtual Internship
๐๐ข๐ง๐ค๐:-
https://pdlink.in/409RHXN
Enroll for FREE & Get Certified ๐
1๏ธโฃ BCG Data Science & Analytics Virtual Experience
2๏ธโฃ TATA Data Visualization Internship
3๏ธโฃ Accenture Data Analytics Virtual Internship
๐๐ข๐ง๐ค๐:-
https://pdlink.in/409RHXN
Enroll for FREE & Get Certified ๐
Essential Topics to Master Data Science Interviews: ๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
Forwarded from Artificial Intelligence
๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ญ๐ฌ๐ฌ% ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐๐๐ฟ๐ฒ, ๐๐, ๐๐๐ฏ๐ฒ๐ฟ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐ & ๐ ๐ผ๐ฟ๐ฒ๐
Want to upskill in Azure, AI, Cybersecurity, or App Developmentโwithout spending a single rupee?๐จโ๐ป๐ฏ
Enter Microsoft Learn โ a 100% free platform that offers expert-led learning paths to help you grow๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4k6lA2b
Enjoy Learning โ ๏ธ
Want to upskill in Azure, AI, Cybersecurity, or App Developmentโwithout spending a single rupee?๐จโ๐ป๐ฏ
Enter Microsoft Learn โ a 100% free platform that offers expert-led learning paths to help you grow๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4k6lA2b
Enjoy Learning โ ๏ธ
How to master Python from scratch๐
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐ A collection of the good Gen AI free courses
๐น Generative artificial intelligence
1๏ธโฃ Generative AI for Beginners course : building generative artificial intelligence apps.
2๏ธโฃ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3๏ธโฃ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4๏ธโฃ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5๏ธโฃ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
๐น Generative artificial intelligence
1๏ธโฃ Generative AI for Beginners course : building generative artificial intelligence apps.
2๏ธโฃ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3๏ธโฃ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4๏ธโฃ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5๏ธโฃ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
๐๐ฒ๐ฎ๐ฟ๐ป ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ณ๐ฟ๐ผ๐บ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ โ ๐๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ!๐
Want to break into machine learning but not sure where to start?๐ป
Googleโs Machine Learning Crash Course is the perfect launchpadโabsolutely free, beginner-friendly, and created by the engineers behind the tools.๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jEiJOe
All The Best ๐
Want to break into machine learning but not sure where to start?๐ป
Googleโs Machine Learning Crash Course is the perfect launchpadโabsolutely free, beginner-friendly, and created by the engineers behind the tools.๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jEiJOe
All The Best ๐
Machine Learning โ Essential Concepts ๐
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Forwarded from Artificial Intelligence
๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
Feeling like your resume could use a boost? ๐
Letโs make that happen with Microsoft Azure certifications that are not only perfect for beginners but also completely free!๐ฅ๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4iVRmiQ
Essential skills for todayโs tech-driven worldโ ๏ธ
Feeling like your resume could use a boost? ๐
Letโs make that happen with Microsoft Azure certifications that are not only perfect for beginners but also completely free!๐ฅ๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4iVRmiQ
Essential skills for todayโs tech-driven worldโ ๏ธ
When to Use Which Programming Language?
C โ OS Development, Embedded Systems, Game Engines
C++ โ Game Dev, High-Performance Apps, Finance
Java โ Enterprise Apps, Android, Backend
C# โ Unity Games, Windows Apps
Python โ AI/ML, Data, Automation, Web Dev
JavaScript โ Frontend, Full-Stack, Web Games
Golang โ Cloud Services, APIs, Networking
Swift โ iOS/macOS Apps
Kotlin โ Android, Backend
PHP โ Web Dev (WordPress, Laravel)
Ruby โ Web Dev (Rails), Prototypes
Rust โ System Apps, Blockchain, HPC
Lua โ Game Scripting (Roblox, WoW)
R โ Stats, Data Science, Bioinformatics
SQL โ Data Analysis, DB Management
TypeScript โ Scalable Web Apps
Node.js โ Backend, Real-Time Apps
React โ Modern Web UIs
Vue โ Lightweight SPAs
Django โ AI/ML Backend, Web Dev
Laravel โ Full-Stack PHP
Blazor โ Web with .NET
Spring Boot โ Microservices, Java Enterprise
Ruby on Rails โ MVPs, Startups
HTML/CSS โ UI/UX, Web Design
Git โ Version Control
Linux โ Server, Security, DevOps
DevOps โ Infra Automation, CI/CD
CI/CD โ Testing + Deployment
Docker โ Containerization
Kubernetes โ Cloud Orchestration
Microservices โ Scalable Backends
Selenium โ Web Testing
Playwright โ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING ๐๐
C โ OS Development, Embedded Systems, Game Engines
C++ โ Game Dev, High-Performance Apps, Finance
Java โ Enterprise Apps, Android, Backend
C# โ Unity Games, Windows Apps
Python โ AI/ML, Data, Automation, Web Dev
JavaScript โ Frontend, Full-Stack, Web Games
Golang โ Cloud Services, APIs, Networking
Swift โ iOS/macOS Apps
Kotlin โ Android, Backend
PHP โ Web Dev (WordPress, Laravel)
Ruby โ Web Dev (Rails), Prototypes
Rust โ System Apps, Blockchain, HPC
Lua โ Game Scripting (Roblox, WoW)
R โ Stats, Data Science, Bioinformatics
SQL โ Data Analysis, DB Management
TypeScript โ Scalable Web Apps
Node.js โ Backend, Real-Time Apps
React โ Modern Web UIs
Vue โ Lightweight SPAs
Django โ AI/ML Backend, Web Dev
Laravel โ Full-Stack PHP
Blazor โ Web with .NET
Spring Boot โ Microservices, Java Enterprise
Ruby on Rails โ MVPs, Startups
HTML/CSS โ UI/UX, Web Design
Git โ Version Control
Linux โ Server, Security, DevOps
DevOps โ Infra Automation, CI/CD
CI/CD โ Testing + Deployment
Docker โ Containerization
Kubernetes โ Cloud Orchestration
Microservices โ Scalable Backends
Selenium โ Web Testing
Playwright โ Modern Web Automation
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING ๐๐
๐ง๐ผ๐ฝ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ณ๐ผ๐ฟ ๐ฎ๐ฌ๐ฎ๐ฑ โ ๐ฅ๐ฒ๐ฐ๐ฒ๐ป๐๐น๐ ๐๐๐ธ๐ฒ๐ฑ ๐ฏ๐ ๐ ๐ก๐๐๐
๐ Preparing for Python Interviews in 2025?๐ฃ
If youโre aiming for roles in data analysis, backend development, or automation, Python is your key weaponโand so is preparing with the right questions.๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
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Crack your next Python interviewโ ๏ธ
๐ Preparing for Python Interviews in 2025?๐ฃ
If youโre aiming for roles in data analysis, backend development, or automation, Python is your key weaponโand so is preparing with the right questions.๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZbAtrW
Crack your next Python interviewโ ๏ธ
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๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐๐๐ฒ๐ฟ๐ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ฆ๐๐ฎ๐ฟ๐ ๐ช๐ถ๐๐ต๐
๐ป Want to Learn Coding but Donโt Know Where to Start?๐ฏ
Whether youโre a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/437ow7Y
All The Best ๐
๐ป Want to Learn Coding but Donโt Know Where to Start?๐ฏ
Whether youโre a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/437ow7Y
All The Best ๐
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