🔴 How to MASTER a programming language using ChatGPT: 📌

1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

2. What are some commonly asked interview questions about [lang]?

3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.

4. Give me some practice questions along with solutions for [concept] in [lang].

5. What are some common mistakes that people make in [lang]?

6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

7. How can I optimize the performance of my code in [lang]?

8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?

9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?

10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?

11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?

12. How can I effectively collaborate with other developers in [lang] on a project?

13. What are some common data structures and algorithms that I should be familiar with in [lang]?

How to Create Resume using ChatGPT 👇👇
https://t.me/free4unow_backup/687

Master DSA 👇👇
https://t.me/dsabooks/156

Like for more ❤️

#ai

Hard Pill To Swallow: 💊

Robots aren’t stealing your future - they’re taking the boring jobs. 

Meanwhile:

- Some YouTuber made six figures sharing what she loves. 
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.

Here’s the thing:

Hard work still matters. But the rules of the game have changed. 

The real money is in solving problems, spreading ideas, and building cool stuff.

Call it evolution. Call it disruption. Whatever.

Crying about the old world won't help you thrive in the new one.

Create something.✨

#ai

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.

#ai #ml

Master AI (Artificial Intelligence) in 10 days 👇👇

#AI

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Free Resources: https://t.me/machinelearning_deeplearning

Share for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

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

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

Master AI (Artificial Intelligence) in 10 days 👇👇

#AI

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Free Resources: https://t.me/machinelearning_deeplearning

Share for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

🔥 MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence

🔉 𝐌𝐮𝐬𝐭-𝐖𝐚𝐭𝐜𝐡 𝐀𝐈 𝐓𝐞𝐝 𝐓𝐚𝐥𝐤𝐬

⏩ The inside story of ChatGPT's astonishing potential by Greg Brockman. https://youtu.be/C_78DM8fG6E?si=kdGNA1PvO1lb7L8t

⏩ How AI could save (not destroy) education by Sal Khan
https://youtu.be/hJP5GqnTrNo?si=wlD-SOjr5ZxLQ0vQ

⏩ How to keep AI under control by Max Tegmark
https://youtu.be/xUNx_PxNHrY?si=e8JDz9up3IRYmBo5

⏩ How to think computationally about AI, the universe, and everything by Stephen Wolfram
https://youtu.be/fLMZAHyrpyo?si=5O1b63qgga89rEOb

⏩ The dark side of competition in AI by Liv Boeree
https://youtu.be/WX_vN1QYgmE?si=QDMlKkrxqrSCdFkr

⏩ How AI art could enhance humanity's collective memory by Refik Anadol
https://youtu.be/iz7diOuaTos?si=iyQOF20jZp78hfo2

⏩ Why AI is incredibly smart and shockingly stupid by Yejin Choil
https://youtu.be/SvBR0OGT5VI?si=rLhDzmohC_dPfrtM

⏩ Will superintelligent AI end the world by Eliezer Yudkowsky
https://youtu.be/Yd0yQ9yxSYY?si=JqN2yNgP0IOTnjN1

#ai

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

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

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

Master AI (Artificial Intelligence) in 10 days 👇👇

#AI

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Free Resources: https://t.me/machinelearning_deeplearning

Share for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

Free Artificial Intelligence Courses
👇👇
https://academy.openai.com/public/content

#ai