Artificial Intelligence

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍 If you’re serious about becoming a Data Scientist but don’t know where to start, these YouTube channels will take you from 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱—all for FREE! 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3QaTvdg Start from scratch, master advanced concepts, and land your dream job in Data Science! 🎯

𝐇𝐨𝐰 𝐭𝐨 𝐁𝐞𝐠𝐢𝐧 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 🔹 𝐋𝐞𝐯𝐞𝐥 𝟏: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐚𝐧𝐝 𝐑𝐀𝐆 ▪️ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development. ▪️ 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. ▪️ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs. ▪️ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks. 🔹 𝐋𝐞𝐯𝐞𝐥 𝟐: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 ▪️ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications. ▪️ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs. ▪️ Introduction to AI Agents: Get an overview of AI agents—autonomous entities that use AI to perform tasks or solve problems. ▪️ Agentic Frameworks: Explore popular tools like LangChain or OpenAI’s API to build and manage AI agents. ▪️ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent. ▪️ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops. ▪️ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency. ▪️ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents. ▪️ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently. ▪️ 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

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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 𝕏 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.✌️✌️

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Open Source LLMs

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Should we worry or relax 😂

Machine Learning Algorithms & Time Complexity

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