Hey guys,
Here are some best Telegram Channels for free education in 2025
๐๐
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
ChatGPT Prompts
AI Projects
Free access to all the Paid Channels
๐๐
https://t.me/addlist/4q2PYC0pH_VjZDk5
Do react with โฅ๏ธ if you need more content like this
ENJOY LEARNING ๐๐
Here are some best Telegram Channels for free education in 2025
๐๐
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
ChatGPT Prompts
AI Projects
Free access to all the Paid Channels
๐๐
https://t.me/addlist/4q2PYC0pH_VjZDk5
Do react with โฅ๏ธ if you need more content like this
ENJOY LEARNING ๐๐
โค11๐4
Waiting for that HR
Who is looking for DeepSeek expert with 5 years of experience. ๐ ๐
Who is looking for DeepSeek expert with 5 years of experience. ๐ ๐
๐คฃ11๐7
If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
๐8โค1
How to revolutionize Hollywood with AI.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
๐4
Developer: I trained AI. (2015) โ
AI: Now I train you. (2024) ๐๐ฅ
Free AI Resources: ๐
https://lnkd.in/dyEZQwXv
AI: Now I train you. (2024) ๐๐ฅ
Free AI Resources: ๐
https://lnkd.in/dyEZQwXv
๐11๐4โค2๐ฅด1๐ญ1
๐ โ AI/ML Engineer
Stage 1 โ Python Basics
Stage 2 โ Statistics & Probability
Stage 3 โ Linear Algebra & Calculus
Stage 4 โ Data Preprocessing
Stage 5 โ Exploratory Data Analysis (EDA)
Stage 6 โ Supervised Learning
Stage 7 โ Unsupervised Learning
Stage 8 โ Feature Engineering
Stage 9 โ Model Evaluation & Tuning
Stage 10 โ Deep Learning Basics
Stage 11 โ Neural Networks & CNNs
Stage 12 โ RNNs & LSTMs
Stage 13 โ NLP Fundamentals
Stage 14 โ Deployment (Flask, Docker)
Stage 15 โ Build projects
ENJOY LEARNING ๐๐
Stage 1 โ Python Basics
Stage 2 โ Statistics & Probability
Stage 3 โ Linear Algebra & Calculus
Stage 4 โ Data Preprocessing
Stage 5 โ Exploratory Data Analysis (EDA)
Stage 6 โ Supervised Learning
Stage 7 โ Unsupervised Learning
Stage 8 โ Feature Engineering
Stage 9 โ Model Evaluation & Tuning
Stage 10 โ Deep Learning Basics
Stage 11 โ Neural Networks & CNNs
Stage 12 โ RNNs & LSTMs
Stage 13 โ NLP Fundamentals
Stage 14 โ Deployment (Flask, Docker)
Stage 15 โ Build projects
ENJOY LEARNING ๐๐
๐9โค2๐1
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.me/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.me/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.me/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.me/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
๐4โค1
๐๐จ๐ฐ ๐ญ๐จ ๐๐๐ ๐ข๐ง ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://t.me/machinelearning_deeplearning
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://t.me/machinelearning_deeplearning
โค4๐3