AI Complete OneShot Course for Beginners | Learn AI & ML Fundamentals from Scratch
About this course
๐New | Complete AI/ML Job Ready Batch :https://www.apnacollege.in/course/prime-ai Batch starting from 15th Nov 2025 -------------------------------------------------------------------------- Link for International students : https://rzp.io/rzp/prime-ai Eligibility for Prime (AI/ML batch) : - B.T...
๐New | Complete AI/ML Job Ready Batch :https://www.apnacollege.in/course/prime-ai Batch starting from 15th Nov 2025 -------------------------------------------------------------------------- Link for International students : https://rzp.io/rzp/prime-ai Eligibility for Prime (AI/ML batch) : - B.Tech students preparing for Tech Placement/Internships. - working professionals switching to AI roles - M.Tech/MCA/BCA who are preparing for Tech Placements/Internships Class Schedule : - Mentioned in video & on website. - Lectures will come on weekends - Lecture duration : upto 2.5 - 3 Hours - Some weekends will have additional mentorship sessions. - It will take us 4+ months for completion - Additional projects will be provided in 5th month for more practice. Share your progress on Twitter : https://x.com/ShradhaKhapra_ DSA Series full playlist : https://www.youtube.com/playlist?list=PLfqMhTWNBTe137I_EPQd34TsgV6IO55pt Shradha Khapra Ma'am community Instagram : https://www.instagram.com/shradhakhapra LinkedIn : https://www.linkedin.com/in/shradha-khapra/
What learners say
AI summaryLearners widely praise this one-shot as a clear, beginner-friendly introduction to AI, ML, deep learning, and generative AI, with many saying it made confusing terminology finally click. The main recurring criticism is that the pace is too fast, and several viewers want a longer, more detailed follow-up series.
What learners praise
- Simple, beginner-friendly explanations of AI/ML/DL/GenAI hierarchy
- Helpful practical examples and analogies
- Clear structure and coverage of fundamentals
- Accessible to non-technical viewers
Common caveats
- Pace is too fast for some learners
- Only an overview; lacks depth and hands-on practice
- Requests for a full, detailed course or bootcamp
- A few minor factual slips (e.g., mispronouncing y-axis as x-axis)
AI-generated from 100 viewer comments on YouTube โ it summarizes outside comments and is not a CourseShelf review.
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IntermediateEuron - https://euron.one/ Course Link : https://euron.one/course/rag-masters For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Step into the world of Retrieval-Augmented Generation (RAG) pipelines with this complete guide! This video breaks down RAG concepts into easy-to-follow steps while showing you how to build real, working pipelines. Whether youโre just starting your AI journey or looking to strengthen your expertise in retrieval-based systems, this video is packed with practical insights and hands-on coding. What youโll discover in this tutorial: - RAG Fundamentals: Learn how RAG works, from architecture to workflow. - Tools in Action: Explore vector databases, embeddings, LangChain, and other essentials. - Hands-On Projects: Apply RAG to solve real-world use cases. - Prompt Engineering: Learn how to optimize responses and handle private datasets. - Deployment Made Simple: Deploy RAG apps using Streamlit, Render, or AWS Elastic Beanstalk. Why this video is worth your time: - Beginner-friendly yet detailed enough for intermediate learners. - Hands-on coding and project-based learning. - Clear, structured explanations to help you actually build and deploy a RAG system. CHAPTERS: 00:00 - Announcements 01:36 - What is Retrieval Augmented Generation (RAG) 05:09 - How RAG Works 10:47 - Understanding Retrieval Augmented Generation 14:20 - Problems Solved by RAG 21:13 - Overview of RAG Pipeline 22:58 - RAG Pipeline Explained 27:56 - Generating Embeddings 38:35 - Preparing Your Own Data for RAG 41:02 - Creating Text Files for Data 49:53 - Creating Embeddings from Data 1:11:40 - Querying from Vector Database 1:14:47 - Final Operation: How RA Works 1:31:56 - Deploying Code in Streamlit 1:32:51 - Setting Up Application Directory 1:33:14 - Creating app.py File 1:37:00 - Developing app.py 1:41:34 - Creating Environment for Streamlit 1:46:28 - Testing Streamlit Application 1:50:04 - Deploying Application on Streamlit 1:50:34 - Deploying Application on Render 1:50:40 - Deploying Application on AWS Elastic Beanstalk 2:00:24 - Streamlit Deployment Hands-On 2:29:54 - Introduction to Document Loading 2:32:41 - Text Loader Overview 2:35:21 - Loading CSV Files 2:36:00 - Loading PDF Files 2:46:29 - Chunking and Splitting Data 3:05:14 - Lecture 2 Overview 3:10:28 - Cosine Similarity and Normalization 3:19:59 - Practical Cosine Similarity 3:26:01 - Introduction to Vector Databases 3:31:52 - Understanding Vector Representation 3:37:19 - Cosine Similarity Explained 4:11:28 - Step 2: Creating Embeddings 4:18:25 - Step 3: Creating Embedding Arrays 4:40:29 - Inserting Data into ChromaDB 4:43:14 - Querying ChromaDB 4:46:38 - Updating Records in ChromaDB 4:49:24 - Adding Metadata Information 4:56:22 - Persisting Collections in ChromaDB 5:03:20 - Pinecone Insert Operations 5:31:16 - Connecting to BayesVector 6:13:20 - Lecture 2: End to End ALM Chain 6:18:44 - Project Setup Process 6:24:08 - System Setup for ALM Chain 6:25:36 - Accessing LM and Embeddings 7:10:07 - Multi-Agent System with Self-Routing 7:12:05 - Accessing LLM in ALM 7:19:55 - Creating a Tool for ALM 7:22:08 - Creating an Agent in ALM 7:23:41 - Creating a Routing Agent 7:34:01 - Introduction to (LCEL) 8:03:02 - Setting the Entry Point in ALM 8:10:33 - Multi-Agent System Overview 8:16:08 - Creating Context Files 8:18:37 - Researcher Node in ALM 8:24:45 - Synthesizer Node Overview 8:27:20 - Classifier Node in ALM 8:28:45 - Finalizer Node Overview 8:42:01 - Understanding Prompting Techniques 8:43:20 - Crafting Effective Prompts 8:53:20 - Few-Shot Prompting Techniques 9:00:48 - Output Format Instructions 9:04:27 - Chain of Thought (COT) Prompting 9:09:24 - Explicit Anchoring Techniques 9:50:37 - Project Setup Process 9:54:41 - Obtaining URI API Key 9:57:48 - Storing and Retrieving Vectors 10:43:58 - Deploying the Chatbot Application 10:45:58 - Testing the Deployed Chatbot Roadmap for you : AI /Data Science Pro Level Expert Roadmap - https://euron.one/roadmap/c9361831-c806-45e2-b65c-c3f4c6cd2fa4 NLP expert Roadmap - https://euron.one/roadmap/300bc526-ed55-42e3-9072-43aca6c3ba4f Data Analytics / Business Analytics Expert Roadmap - https://euron.one/roadmap/920278e8-e3c0-4763-a135-ebed66074853 Big Data / Data Engineering Expert Roadmap - https://euron.one/roadmap/98c8db49-2eab-44b7-8fba-7f2b2575ec83 Computer Vision Roadmap - https://euron.one/roadmap/d8281277-5cfd-4498-bbff-4135aa178897 Deep Learning Roadmap - https://euron.one/roadmap/1495a7ba-4297-4cc9-8d68-5460cafb90ca Generative AI Roadmap - https://euron.one/roadmap/2380f611-7475-4343-b7f7-22b765710604 Machine Learning Expert Roadmap - https://euron.one/roadmap/ff514391-328e-4863-b810-0a5c5db6a170 Android- https://play.google.com/store/apps/details?id=com.euron.one&hl=en IOS - https://apps.apple.com/in/app/euron-your-learning-app/id6741360597
Excel Zero to Hero Tutorial with AI ๐ | 18 Hours Detailed Tutorial
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