🤗 HuggingFace is offering 9 AI courses for FREE!

These 9 courses covers LLMs, Agents, Deep RL, Audio and more

1️⃣ LLM Course:
https://huggingface.co/learn/llm-course/chapter1/1

2️⃣ Agents Course:
https://huggingface.co/learn/agents-course/unit0/introduction

3️⃣ Deep Reinforcement Learning Course:
https://huggingface.co/learn/deep-rl-course/unit0/introduction

4️⃣ Open-Source AI Cookbook:
https://huggingface.co/learn/cookbook/index

5️⃣ Machine Learning for Games Course
https://huggingface.co/learn/ml-games-course/unit0/introduction

6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction

7️⃣ Vision Course:
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome

8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction

9️⃣ Hugging Face Diffusion Models Course:
https://huggingface.co/learn/diffusion-course/unit0/1

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

Join @free4unow_backup for more free resources.

ENJOY LEARNING👍👍

𝟱 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍

🎓 You don’t need to break the bank to break into AI!🪩

If you’ve been searching for beginner-friendly, certified AI learning—Google Cloud has you covered🤝👨‍💻

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3SZQRIU

📍All taught by industry-leading instructors✅️

🚨 Google dropping three models today:

- Gemini 2.5 Pro (stable)
- Gemini 2.5 Flash (stable)
- Gemini 2.5 Flash Lite (preview)

Let's go 🚀

𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗞𝗮𝗴𝗴𝗹𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗝𝘂𝗺𝗽𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍

Want to break into Data Science but not sure where to start?🚀

These free Kaggle micro-courses are the perfect launchpad — beginner-friendly, self-paced, and yes, they come with certifications!👨‍🎓🎊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4l164FN

No subnoscription. No hidden fees. Just pure learning from a trusted platform✅️

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗮𝗿𝗲𝗲𝗿 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

Ready to upgrade your career without spending a dime?✨️

From Generative AI to Project Management, get trained by global tech leaders and earn certificates that carry real value on your resume and LinkedIn profile!📲📌

𝐋𝐢𝐧𝐤👇:-

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Designed to equip you with in-demand skills and industry-recognised certifications📜✅️

10 Free Resources to Learn AI in 2025

✅ Google AI Hub – Crash courses, tutorials, and tools straight from Google
✅ Fast.ai – Practical deep learning for coders, no PhD required
✅ DeepLearning.AI’s YouTube – Short, high-quality videos on ML & AI concepts
✅ Hugging Face Course – Learn to work with Transformers hands-on
✅ MIT OpenCourseWare (AI & ML) – Free college-level AI courses
✅ Kaggle Learn – Interactive, notebook-based tutorials on ML, Python & SQL
✅ Microsoft Learn (AI Track) – Modules on Azure AI, Python, and more
✅ Stanford CS229/CS231n Lectures – Deep dives into ML and deep learning
✅ DataSimplifier – Free Data Analytics Resources
✅ OpenAI Cookbook – Real-world GPT examples & best practices

Free Resources: https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g

ENJOY LEARNING 👍👍

𝟱 𝗙𝗥𝗘𝗘 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗗𝗮𝘁𝗮 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

Want to break into Data Analytics or Data Science—but don’t know where to begin?🚀

Harvard University offers 5 completely free online courses that will build your foundation in Python, statistics, machine learning, and data visualization — no prior experience or degree required!👨‍🎓💫

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3T3ZhPu

These Harvard-certified courses will boost your resume, LinkedIn profile, and skills✅️

AI Myths vs. Reality

1️⃣ AI Can Think Like Humans – ❌ Myth
🤖 AI doesn’t "think" or "understand" like humans. It predicts based on patterns in data but lacks reasoning or emotions.

2️⃣ AI Will Replace All Jobs – ❌ Myth
👨‍💻 AI automates repetitive tasks but creates new job opportunities in AI development, ethics, and oversight.

3️⃣ AI is 100% Accurate – ❌ Myth
⚠ AI can generate incorrect or biased outputs because it learns from imperfect human data.

4️⃣ AI is the Same as AGI – ❌ Myth
🧠 Generative AI is task-specific, while AGI (which doesn’t exist yet) would have human-like intelligence.

5️⃣ AI is Only for Big Tech – ❌ Myth
💡 Startups, small businesses, and individuals use AI for marketing, automation, and content creation.

6️⃣ AI Models Don’t Need Human Supervision – ❌ Myth
🔍 AI requires human oversight to ensure ethical use and prevent misinformation.

7️⃣ AI Will Keep Getting Smarter Forever – ❌ Myth
📉 AI is limited by its training data and doesn’t improve on its own without new data and updates.

AI is powerful but not magic. Knowing its limits helps us use it wisely. 🚀

𝟱 𝗙𝗥𝗘𝗘 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗜𝗕𝗠, 𝗨𝗱𝗮𝗰𝗶𝘁𝘆 & 𝗠𝗼𝗿𝗲😍

Looking to learn Python from scratch—without spending a rupee? 💻

Offered by trusted platforms like Harvard University, IBM, Udacity, freeCodeCamp, and OpenClassrooms, each course is self-paced, easy to follow, and includes a certificate of completion🔥👨‍🎓

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3HNeyBQ

Kickstart your career✅️

𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

I failed my first data interview — and here’s why:⬇️

❌ No structured learning
❌ No real projects
❌ Just random YouTube tutorials and half-read blogs

If this sounds like you, don’t repeat my mistake✨️
Recruiters want proof of skills, not just buzzwords📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ka1ZOl

All The Best 🎊

List of Top 12 Coding Channels on WhatsApp:

1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L


2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17


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


4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X


5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s


6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32


7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z


8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E


9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y


10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D


11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v


12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

ENJOY LEARNING 👍👍

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗤𝗟 𝗖𝗮𝗻 𝗕𝗲 𝗙𝘂𝗻! 𝟰 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗧𝗵𝗮𝘁 𝗙𝗲𝗲𝗹 𝗟𝗶𝗸𝗲 𝗮 𝗚𝗮𝗺𝗲😍

Think SQL is all about dry syntax and boring tutorials? Think again.🤔

These 4 gamified SQL websites turn learning into an adventure — from solving murder mysteries to exploring virtual islands, you’ll write real SQL queries while cracking clues and completing missions📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4nh6PMv

These platforms make SQL interactive, practical, and fun✅️

Tools & Languages in AI & Machine Learning

Want to build the next ChatGPT or a self-driving car algorithm? You need to master the right tools. Today, we’ll break down the tech stack that powers AI innovation.

1. Python – The Heartbeat of AI

Python is the most widely used programming language in AI. It’s simple, versatile, and backed by thousands of libraries.
Why it matters: Readable syntax, massive community, and endless ML/AI resources.


2. NumPy & Pandas – Data Handling Pros

Before building models, you clean and understand data. These libraries make it easy.

NumPy: Fast matrix computations

Pandas: Smart data manipulation and analysis


3. Scikit-learn – For Traditional ML

Want to build a model to predict house prices or classify emails as spam? Scikit-learn is perfect for regression, classification, clustering, and more.


4. TensorFlow & PyTorch – Deep Learning Giants

These are the two leading frameworks used for building neural networks, CNNs, RNNs, LLMs, and more.

TensorFlow: Backed by Google, highly scalable

PyTorch: Preferred in research for its flexibility and Pythonic style


5. Keras – The Friendly Deep Learning API

Built on top of TensorFlow, it allows quick prototyping of deep learning models with minimal code.


6. OpenCV – For Computer Vision

Want to build face recognition or object detection apps? OpenCV is your go-to for processing images and video.


7. NLTK & spaCy – NLP Toolkits

These tools help machines understand human language. You’ll use them to build chatbots, summarize text, or analyze sentiment.


8. Jupyter Notebook – Your AI Playground

Interactive notebooks where you can write code, visualize data, and explain logic in one place. Great for experimentation and demos.


9. Google Colab – Free GPU-Powered Coding

Run your AI code with GPUs for free in the cloud — ideal for training ML models without any setup.


10. Hugging Face – Pre-trained AI Models

Use models like BERT, GPT, and more with just a few lines of code. No need to train everything from scratch!


To build smart AI solutions, you don’t need 100 tools — just the right ones. Start with Python, explore scikit-learn, then dive into TensorFlow or PyTorch based on your goal.

Artificial intelligence learning series: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y