Forwarded from Artificial Intelligence
𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
👨💻 𝟓 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐍𝐞𝐞𝐝𝐬 𝐢𝐧 𝐚𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📊
🔸𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 & 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).
🔸𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.
🔸𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
It’s important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your model’s performance.
🔸𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.
🔸𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
Once you’ve built a model, it’s important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.
🔍 𝐏𝐫𝐨 𝐓𝐢𝐩: Keep practicing by working on real projects or using online platforms to improve these skills!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
Hope this helps you 😊
#ai #datascience
🔸𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 & 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).
🔸𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.
🔸𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
It’s important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your model’s performance.
🔸𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.
🔸𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
Once you’ve built a model, it’s important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.
🔍 𝐏𝐫𝐨 𝐓𝐢𝐩: Keep practicing by working on real projects or using online platforms to improve these skills!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
Hope this helps you 😊
#ai #datascience
❤3
Forwarded from Artificial Intelligence
🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchers✅️
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchers✅️
❤2
Coding isn't easy!
It’s the art of turning ideas into functional, impactful software that shapes the world around us.
To truly excel in coding, focus on these key areas:
0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.
1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.
2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.
3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.
4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation noscripts.
5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.
6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.
7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.
8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.
9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.
💡 Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
It’s the art of turning ideas into functional, impactful software that shapes the world around us.
To truly excel in coding, focus on these key areas:
0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.
1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.
2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.
3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.
4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation noscripts.
5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.
6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.
7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.
8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.
9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.
💡 Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
❤3
Forwarded from Artificial Intelligence
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗚𝘂𝗶𝗱𝗲😍
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
❤3
Forwarded from Artificial Intelligence
𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
Creative ways to craft your data analytics portfolio
Free Data sets for Data Analytics Projects: https://news.1rj.ru/str/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project denoscriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Free Data sets for Data Analytics Projects: https://news.1rj.ru/str/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project denoscriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤1
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3Hnx3wh
𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4jCAtJ5
Enroll for FREE & Get Certified 🎓
𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3Hnx3wh
𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4jCAtJ5
Enroll for FREE & Get Certified 🎓
10 New & Trending AI Concepts You Should Know in 2025
✅ Retrieval-Augmented Generation (RAG) – Combines search with generative AI for smarter answers
✅ Multi-Modal Models – AI that understands text, image, audio, and video (like GPT-4V, Gemini)
✅ Agents & AutoGPT – AI that can plan, execute, and make decisions with minimal input
✅ Synthetic Data Generation – Creating fake yet realistic data to train AI models
✅ Federated Learning – Train models without moving your data (privacy-first AI)
✅ Prompt Engineering – Crafting prompts to get the best out of LLMs
✅ Fine-Tuning & LoRA – Customize big models for specific tasks with minimal resources
✅ AI Safety & Alignment – Making sure AI systems behave ethically and predictably
✅ TinyML – Running ML models on edge devices with very low power (IoT focus)
✅ Open-Source LLMs – Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
✅ Retrieval-Augmented Generation (RAG) – Combines search with generative AI for smarter answers
✅ Multi-Modal Models – AI that understands text, image, audio, and video (like GPT-4V, Gemini)
✅ Agents & AutoGPT – AI that can plan, execute, and make decisions with minimal input
✅ Synthetic Data Generation – Creating fake yet realistic data to train AI models
✅ Federated Learning – Train models without moving your data (privacy-first AI)
✅ Prompt Engineering – Crafting prompts to get the best out of LLMs
✅ Fine-Tuning & LoRA – Customize big models for specific tasks with minimal resources
✅ AI Safety & Alignment – Making sure AI systems behave ethically and predictably
✅ TinyML – Running ML models on edge devices with very low power (IoT focus)
✅ Open-Source LLMs – Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
❤2
Forwarded from Artificial Intelligence
𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥
MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jBNtP2
These courses offer industry-relevant skills & completion certificates at no cost✅️
🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥
MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jBNtP2
These courses offer industry-relevant skills & completion certificates at no cost✅️
❤2🔥1
Want To become a Backend Developer?
Here’s a roadmap with essential concepts:
1. Programming Languages
JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.
2. Version Control
Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.
3. Databases
Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.
4. APIs & Web Services
REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.
5. Server & Application Frameworks
Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.
6. Authentication & Authorization
JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.
7. Caching
Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.
8. Message Queues & Event-Driven Architecture
Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.
9. Microservices & Distributed Systems
Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).
10. Testing & Debugging
Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.
11. Containerization & Orchestration
Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.
12. CI/CD (Continuous Integration & Continuous Deployment)
CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.
13. Cloud Platforms
AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.
14. Logging & Monitoring
Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.
15. Security
Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.
16. Scalability & Optimization
Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.
ENJOY LEARNING 👍👍
#backend
Here’s a roadmap with essential concepts:
1. Programming Languages
JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.
2. Version Control
Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.
3. Databases
Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.
4. APIs & Web Services
REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.
5. Server & Application Frameworks
Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.
6. Authentication & Authorization
JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.
7. Caching
Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.
8. Message Queues & Event-Driven Architecture
Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.
9. Microservices & Distributed Systems
Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).
10. Testing & Debugging
Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.
11. Containerization & Orchestration
Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.
12. CI/CD (Continuous Integration & Continuous Deployment)
CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.
13. Cloud Platforms
AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.
14. Logging & Monitoring
Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.
15. Security
Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.
16. Scalability & Optimization
Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.
ENJOY LEARNING 👍👍
#backend
❤1
Forwarded from Python Projects & Resources
𝟱 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵, 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲😍
Dreaming of an MIT education without the tuition fees? 🎯
These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data science—all from the comfort of your home! 🌐✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/45cvR95
Your gateway to a smarter career✅️
Dreaming of an MIT education without the tuition fees? 🎯
These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data science—all from the comfort of your home! 🌐✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/45cvR95
Your gateway to a smarter career✅️
Evolution of Programming Languages🖥️
🔰Programming Languages🔰
1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.
2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesn’t load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javanoscript brought about a revolution in webapps.
3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.
4. C:
Its a low level language too that’s why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.
5. C++ :
Its C with more features and those features make it more complex.
6. Perl:
A language which was developed to create small noscripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.
7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.
8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.
9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.
10. PHP:
It is used to process things in a website. It is server-sided language as it doesn’t get executed in user browser, but on the server. It can be used to generate dynamic webpage content.
11. SQL:
This is not exactly a programming language. It is used to interact with databases.
➡️ This list could be long because there are too many programming language but I introduced you to the popular ones.
❓Which Language Should Be Your First Programming Language?
✅ Suggestions..
1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.
2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).
3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesn’t start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you don’t need to compile the noscript to run it, just write one and run it.
Join for more: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
🔰Programming Languages🔰
1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.
2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesn’t load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javanoscript brought about a revolution in webapps.
3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.
4. C:
Its a low level language too that’s why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.
5. C++ :
Its C with more features and those features make it more complex.
6. Perl:
A language which was developed to create small noscripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.
7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.
8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.
9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.
10. PHP:
It is used to process things in a website. It is server-sided language as it doesn’t get executed in user browser, but on the server. It can be used to generate dynamic webpage content.
11. SQL:
This is not exactly a programming language. It is used to interact with databases.
➡️ This list could be long because there are too many programming language but I introduced you to the popular ones.
❓Which Language Should Be Your First Programming Language?
✅ Suggestions..
1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.
2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).
3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesn’t start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you don’t need to compile the noscript to run it, just write one and run it.
Join for more: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
❤2
Forwarded from Python Projects & Resources
𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
Forwarded from Python Projects & Resources
𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
🎯 Want to switch careers or upgrade your skills — without spending a single rupee?
Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4e1I17a
💥 Start learning today and build the skills top companies want!✅️
🎯 Want to switch careers or upgrade your skills — without spending a single rupee?
Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4e1I17a
💥 Start learning today and build the skills top companies want!✅️
👍1