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Data Alchemy
38k members • Free
5 contributions to Data Alchemy
Feb 20 •
Why I liked Data Alchemy approach
Here is the comparison between the traditional AI learning path and the Alchemy AI learning path: Traditional AI Learning Path 1. Technical Focus: Emphasizes mastering technical skills like Python, ML fundamentals, deep learning, and prompt engineering. 2. Complex Concepts: Dives into advanced topics like RAG systems, LangChain, and fine-tuning LLMs. 3. Specialized Knowledge: Focuses on building specific AI applications and agents. 4. Assumes Prior Knowledge: May assume a strong foundation in programming and AI concepts. Alchemy AI Learning Path 1. Holistic Approach: Combines technical skills with practical application, portfolio building, and specialization. 2. Foundational Focus: Emphasizes setting up a work environment, learning Python, Git, and GitHub basics. 3. Project-Based Learning: Encourages working on projects and building a portfolio to apply skills. 4. Career-Oriented: Includes steps to pick a specialization, share knowledge, and monetize skills. Key Differences 1. Technical vs. Holistic: Traditional path focuses on technical skills, while Alchemy path combines technical and practical aspects. 2. Complexity: Traditional path dives into advanced topics, while Alchemy path focuses on foundational skills and practical application. 3. Career Focus: Alchemy path includes steps to build a portfolio, specialize, and monetize skills, making it more career-oriented. Target Audience 1. Traditional Path: Suitable for those with prior AI knowledge or experience, looking to deepen their technical skills. 2. Alchemy Path: Ideal for beginners or those looking to transition into AI, focusing on practical application and career development.
Feb 16 •
Microsoft just released AI agents for beginners course!
The course consists of 10 lessons that cover the fundamentals of building AI agents. Each lesson stands alone, so start wherever you like! ✨ [Link to the course in comments!] Here's what it covers: 🏁 Intro to AI Agents 🔍 Agentic Frameworks 🧠 Agentic Design Patterns 🔨 Tool Use Design Pattern 🔗 Agentic RAG 🛡️ Building Trustworthy AI Agents 🗺️ Planning Design Pattern 🤝 Multi-Agent Design Pattern 💭 Metacognition Design Pattern 🚀 AI Agents in Production
6 likes • Feb 20
Here is the comparison between the traditional AI learning path and the Alchemy AI learning path: Traditional AI Learning Path 1. Technical Focus: Emphasizes mastering technical skills like Python, ML fundamentals, deep learning, and prompt engineering. 2. Complex Concepts: Dives into advanced topics like RAG systems, LangChain, and fine-tuning LLMs. 3. Specialized Knowledge: Focuses on building specific AI applications and agents. 4. Assumes Prior Knowledge: May assume a strong foundation in programming and AI concepts. Alchemy AI Learning Path 1. Holistic Approach: Combines technical skills with practical application, portfolio building, and specialization. 2. Foundational Focus: Emphasizes setting up a work environment, learning Python, Git, and GitHub basics. 3. Project-Based Learning: Encourages working on projects and building a portfolio to apply skills. 4. Career-Oriented: Includes steps to pick a specialization, share knowledge, and monetize skills. Key Differences 1. Technical vs. Holistic: Traditional path focuses on technical skills, while Alchemy path combines technical and practical aspects. 2. Complexity: Traditional path dives into advanced topics, while Alchemy path focuses on foundational skills and practical application. 3. Career Focus: Alchemy path includes steps to build a portfolio, specialize, and monetize skills, making it more career-oriented. Target Audience 1. Traditional Path: Suitable for those with prior AI knowledge or experience, looking to deepen their technical skills. 2. Alchemy Path: Ideal for beginners or those looking to transition into AI, focusing on practical application and career development.
3 likes • Feb 20
@Mingxun Wei thanks
Nov '23 •
Unlock New Courses at Level 3
Hey everyone, I just completed a new course for you: "Data Science Accelerator". This course will be unlocked, together with "Building Applications with LLMs" at level 3. How to level up? Just interact with the group, get likes and comments, and watch your level go up!
9 likes • Feb 20
Here is the comparison between the traditional AI learning path and the Alchemy AI learning path: Traditional AI Learning Path 1. Technical Focus: Emphasizes mastering technical skills like Python, ML fundamentals, deep learning, and prompt engineering. 2. Complex Concepts: Dives into advanced topics like RAG systems, LangChain, and fine-tuning LLMs. 3. Specialized Knowledge: Focuses on building specific AI applications and agents. 4. Assumes Prior Knowledge: May assume a strong foundation in programming and AI concepts. Alchemy AI Learning Path 1. Holistic Approach: Combines technical skills with practical application, portfolio building, and specialization. 2. Foundational Focus: Emphasizes setting up a work environment, learning Python, Git, and GitHub basics. 3. Project-Based Learning: Encourages working on projects and building a portfolio to apply skills. 4. Career-Oriented: Includes steps to pick a specialization, share knowledge, and monetize skills. Key Differences 1. Technical vs. Holistic: Traditional path focuses on technical skills, while Alchemy path combines technical and practical aspects. 2. Complexity: Traditional path dives into advanced topics, while Alchemy path focuses on foundational skills and practical application. 3. Career Focus: Alchemy path includes steps to build a portfolio, specialize, and monetize skills, making it more career-oriented. Target Audience 1. Traditional Path: Suitable for those with prior AI knowledge or experience, looking to deepen their technical skills. 2. Alchemy Path: Ideal for beginners or those looking to transition into AI, focusing on practical application and career development.
Jul '23 •
Welcome to Data Alchemy - Start Here
The goal of this group is to help you navigate the complex and rapidly evolving world of data science and artificial intelligence. This is your hub to stay up-to-date on the latest trends, learn specialized skills to turn raw data into valuable insights, connect with a community of like-minded individuals, and ultimately, become a Data Alchemist. Together, let's decode the language of data and shape a future where knowledge and community illuminate our way. Rules - Don't sell anything here or use Data Alchemy as any kind of funnel - We delete low effort community posts, and posts with poor English. Proofread your post first. - Help us make the posts high quality. If you see a low quality post, then click on the 3 dots on the post and "Report To Admins". Start by checking out these links - Classroom - Introduction - Roadmap - Contribution Be Aware of Scammers - Please be aware that this is a public group. Unfortunately, some people abuse the Skool platform to send DMs or post comments to trick people. This is the internet, so always do your own due diligence. Never automatically trust someone here on the Skool platform other than @Dave Ebbelaar's official account. To kick things off, please comment below, introducing yourself. Let us know: 1. Your name and where you're from 2. What project(s) you're currently focused on See you in the comments!
11 likes • Feb 20
Here is the comparison between the traditional AI learning path and the Alchemy AI learning path: Traditional AI Learning Path 1. Technical Focus: Emphasizes mastering technical skills like Python, ML fundamentals, deep learning, and prompt engineering. 2. Complex Concepts: Dives into advanced topics like RAG systems, LangChain, and fine-tuning LLMs. 3. Specialized Knowledge: Focuses on building specific AI applications and agents. 4. Assumes Prior Knowledge: May assume a strong foundation in programming and AI concepts. Alchemy AI Learning Path 1. Holistic Approach: Combines technical skills with practical application, portfolio building, and specialization. 2. Foundational Focus: Emphasizes setting up a work environment, learning Python, Git, and GitHub basics. 3. Project-Based Learning: Encourages working on projects and building a portfolio to apply skills. 4. Career-Oriented: Includes steps to pick a specialization, share knowledge, and monetize skills. Key Differences 1. Technical vs. Holistic: Traditional path focuses on technical skills, while Alchemy path combines technical and practical aspects. 2. Complexity: Traditional path dives into advanced topics, while Alchemy path focuses on foundational skills and practical application. 3. Career Focus: Alchemy path includes steps to build a portfolio, specialize, and monetize skills, making it more career-oriented. Target Audience 1. Traditional Path: Suitable for those with prior AI knowledge or experience, looking to deepen their technical skills. 2. Alchemy Path: Ideal for beginners or those looking to transition into AI, focusing on practical application and career development.
5 likes • Feb 20
@Neil Ramrattan thanks
Jan 21 •
How to Build Effective AI Agents
Everyone’s talking about AI agents. But the truth? Most demos you see online are just that—demos. Even big players like Apple and Amazon struggle to make their AI features work in the real world due to issues like hallucinations and unreliable outputs. In this week’s video, I break down the differences between simple workflows and true AI agents and share practical strategies for building reliable AI systems, including: - How to use workflow patterns like prompt chaining and routing to solve real problems effectively - Why agent frameworks might not be the solution you think they are - The #1 thing you need to scale AI systems successfully (hint: it’s not a new tool) Learn how to move beyond the hype and build AI systems that actually work.
4 likes • Feb 19
Hi, I m new to this, things getting interesting!
4 likes • Feb 19
Thanks for sharing
1-5 of 5
@mohammed-ullah-4422
I'm excited to learn about Artificial Intelligence (AI). AI is a field of computer science that enables machines to think, learn, and act like humans.
Active 260d ago
Joined Feb 19, 2025
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