🚀 Become an Agentic AI Builder — Free 12‑Week Certification by Ready Tensor

Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.

The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.

You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.

👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/

𝟯 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to earn free certificates and badges from Microsoft? 🚀

These courses are your golden ticket to mastering in-demand tech skills while boosting your resume with official Microsoft credentials🧑‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4mlCvPu

These certifications will help you stand out in interviews and open new career opportunities in tech✅️

7 Useful Python One-Liners

1. Reverse a string

print("Python"[::-1]) # Output: nohtyP

2. Check for Palindrome

is_palindrome = lambda s: s == s[::-1]
print(is_palindrome("madam")) # Output: True

3. Get all even numbers from a list

print([x for x in range(20) if x % 2 == 0])

4. Flatten a nested list

print([item for sublist in [[1,2],[3,4]] for item in sublist])

5. Find factorial of a number

import math; print(math.factorial(5)) # Output: 120

6. Count frequency of elements

from collections import Counter
print(Counter("banana")) # Output: {'a': 3, 'b': 1, 'n': 2}

7. Swap two variables

a, b = 5, 10
a, b = b, a
print(a, b) # Output: 10 5

For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz

Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

Hope it helps :)

𝗧𝗼𝗽 𝟱 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗠𝗮𝘀𝘁𝗲𝗿𝘆😍

Want to become a Data Analyst but don’t know where to start? 🧑‍💻✨️

You don’t need to spend thousands on courses. In fact, some of the best free learning resources are already on YouTube — taught by industry professionals who break down everything step by step.📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/47f3UOJ

Start with just one channel, stay consistent, and within months, you’ll have the confidence (and portfolio) to apply for data analyst roles.✅️

Best way to prepare for a SQL interviews 👇👇

1. Review Basic Concepts: Ensure you understand fundamental SQL concepts like SELECT statements, JOINs, GROUP BY, and WHERE clauses.

2. Practice SQL Queries: Work on writing and executing SQL queries. Practice retrieving, updating, and deleting data.

3. Understand Database Design: Learn about normalization, indexes, and relationships to comprehend how databases are structured.

4. Know Your Database: If possible, find out which database system the company uses (e.g., MySQL, PostgreSQL, SQL Server) and familiarize yourself with its specific syntax.

5. Data Types and Constraints: Understand various data types and constraints such as PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints.

6. Stored Procedures and Functions: Learn about stored procedures and functions, as interviewers may inquire about these.

7. Data Manipulation Language (DML): Be familiar with INSERT, UPDATE, and DELETE statements.

8. Data Definition Language (DDL): Understand statements like CREATE, ALTER, and DROP for database and table management.

9. Normalization and Optimization: Brush up on database normalization and optimization techniques to demonstrate your understanding of efficient database design.

10. Troubleshooting Skills: Be prepared to troubleshoot queries, identify errors, and optimize poorly performing queries.

11. Scenario-Based Questions: Practice answering scenario-based questions. Understand how to approach problems and design solutions.

12. Latest Trends: Stay updated on the latest trends in database technologies and SQL best practices.

13. Review Resume Projects: If you have projects involving SQL on your resume, be ready to discuss them in detail.

14. Mock Interviews: Conduct mock interviews with a friend or use online platforms to simulate real interview scenarios.

15. Ask Questions: Prepare questions to ask the interviewer about the company's use of databases and SQL.

Best Resources to learn SQL 👇

SQL Topics for Data Analysts

SQL Udacity Course

Download SQL Cheatsheet

SQL Interview Questions

Learn & Practice SQL

Also try to apply what you learn through hands-on projects or challenges.

Please give us credits while sharing: -> https://news.1rj.ru/str/free4unow_backup

ENJOY LEARNING 👍👍

𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗡𝗲𝗲𝗱𝗲𝗱!)😍

Ready to Upgrade Your Skills for a Data-Driven Career in 2025?📍

Whether you’re a student, a fresher, or someone switching to tech, these free beginner-friendly courses will help you get started in data analysis, machine learning, Python, and more👨‍💻🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4mwOACf

Best For: Beginners ready to dive into real machine learning✅️

5 beginner-to-intermediate projects you can build if you're learning Programming & AI


1. AI-Powered Chatbot (Using Python)

Build a simple chatbot that can understand and respond to user inputs. You can use rule-based logic at first, and then explore NLP with libraries like NLTK or spaCy.

Skills: Python, NLP, Regex, Basic ML

Ideas to include:

- Greeting and small talk

- FAQ-based responses

- Sentiment-based replies

You can also integrate it with Telegram or Discord bot


2. Movie Recommendation System

Create a recommendation system based on movie genre, user preferences, or ratings using collaborative filtering or content-based filtering.

Skills: Python, Pandas, Scikit-learn

Ideas to include:

- Use TMDB or MovieLens datasets

- Add filtering by genre

- Include cosine similarity logic


3. AI-Powered Resume Parser

Upload a PDF or DOCX resume and let your app extract name, skills, experience, education, and output it in a structured format.

Skills: Python, NLP, Regex, Flask

Ideas to include:

- File upload option

- Named Entity Recognition (NER) with spaCy

- Save extracted info into a CSV/Database


4. To-Do App with Smart Suggestions

A regular to-do list but with an AI assistant that suggests tasks based on previous entries (e.g., you often add "buy milk" on Mondays? It suggests it.)

Skills: JavaScript/React + AI API (like OpenAI or custom model)

Ideas to include:

- CRUD functionality

- Natural Language date/time parsing

- AI suggestion module


5. Fake News Detector

Given a news headline or article, predict if it’s fake or real. A great application of classification problems.

Skills: Python, NLP, ML (Logistic Regression or TF-IDF + Naive Bayes)


Ideas to include:

- Use datasets from Kaggle

- Preprocess with stopwords, lemmatization

- Display prediction result with probability

React with ❤️ if you want me to share source code or free resources to build these projects

Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

Software Developer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L

ENJOY LEARNING 👍👍

𝟯 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱😍

If you’ve ever thought, “Can I actually build something useful with AI?” — the answer is yes, and you don’t need to be a genius to start.✨️📊

These 3 open-source projects on GitHub are proof of what you can build with just basic coding knowledge and a passion for learning.🧑‍💻💥

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45jKiXe

Build your own AI agent that remembers conversations and gets smarter over time.✅️

𝐏𝐘𝐓𝐇𝐎𝐍 𝐅𝐎𝐑 𝐄𝐕𝐄𝐑𝐘𝐓𝐇𝐈𝐍𝐆!

Python Projects for Beginners

Best free resources to learn AI 😻🙌

𝟓 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐨𝐝𝐢𝐧𝐠😍

Want to Create AI Automations & Agents Without Writing a Single Line of Code?🧑‍💻

These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.🧑‍🎓✨️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4lhYwhn

Just pure, actionable automation skills — for free.✅️

If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍

𝗦𝘁𝗲𝗽 𝗜𝗻𝘁𝗼 𝗮 𝗕𝗖𝗚 𝗔𝗻𝗮𝗹𝘆𝘀𝘁’𝘀 𝗦𝗵𝗼𝗲𝘀: 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 + 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲😍

💼 Ever Wondered How Data Shapes Real Business Decisions at a Top Consulting Firm?🧑‍💻✨️

Now you can experience it firsthand with this interactive simulation from BCG (Boston Consulting Group)📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45HWKRP

This is a powerful resume booster and a unique way to prove your analytical skills✅️

𝐒𝐭𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — 𝟏𝟎𝟎% 𝐅𝐫𝐞𝐞 & 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲😍

Want to dive into data analytics but don’t know where to start?🧑‍💻✨️

These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/47oQD6f

No prior experience needed — just curiosity✅️