Watch the sample class recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=mahout-machine-learning

Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions.

Topics covered in the video:
1. What is Machine Learning
2. Understanding Machine Learning with Mahout
3. Overview of Mahout

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This Edureka Video on Machine Learning Algorithms will give you a basic understanding of the Machine Learning Algorithm with examples. In this video, you will also get to see a demo on ML using Python.

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1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.


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Python is one of the best languages for machine learning. It is enabled with an extensive array of libraries and tools. Different aspects of Machine Learning with Python have been described in this descriptive video.

Related Posts:
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TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models.

#programming #deeplearning #100secondsofcode

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

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Discover how startups can benefit from adopting or creating Small Language Models and the role they have in enabling agentic AI. Learn from world-class experts Julien Simon, Chief Evangelist at Arcee, and Nicolas David, Sr. Startup Architect at AWS

MIT 22.01 Introduction to Nuclear Engineering and Ionizing Radiation, Fall 2016
Instructor: Michael Short
View the complete course: https://ocw.mit.edu/22-01F16
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A brief summary of the discovery of forms of ionizing radiation up to the 1932 discovery of the neutron. We introduce mass-energy equivalence for the first time and explain how these cutting-edge experiments (for their time) conclusively proved the existence of high-energy, ionizing radiation.

License: Creative Commons BY-NC-SA
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MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
Instructor: Andrew Lo

License: Creative Commons BY-NC-SA
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AI can feel overwhelming, but this hands-on course makes it practical, approachable, and fun. You will start with AI fundamentals and model mechanics like transformers and tokens, before moving into the architecture of tools, workflows, and true agents. You will build and experiment with four distinct agents, seeing firsthand how single agents evolve into a multi-agent system. Finally, you will learn how OpenClaw woks to gain the confidence needed to design, build, test, and deploy real-world AI agents.

Course resources: https://kode.wiki/3SrN8GO

Course created by @KodeKloud

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

Chapters
Section 1 & 2: LLM & Core Concepts
- 00:00:00 Course Welcome & Architecture Fundamentals
- 00:07:54 Hallucinations, Model Evolution, & Competitors
- 00:15:23 Developer Fundamentals & Working with APIs
- 00:17:54 Token Economics & Managing Context Windows
- 00:31:13 API Message Roles & Prompt Engineering Nuances

Section 3 & 4: Code Setup & Tool Architecture
- 00:38:29 Hands-On Lab: Practice Labs & First API Call
- 00:45:01 Multi-Turn History, API Costs, & Trade-offs
- 00:51:08 Architecture Overview: Tool Schemas & Execution Loops
- 00:58:20 Predefined AI Workflows vs. Autonomous Agents

Section 5: Building Agents from Scratch
- 01:08:16 The Core Loop: Perceive, Reason, Act
- 01:13:15 Hands-On Lab: Engineering the Standard `While` Loop
- 01:21:13 Planning Limits, Brittle Logic, & Mitigation Guardrails
- 01:26:19 Memory Managers: Sliding Windows & Vector DBs
- 01:33:56 Multi-Agent Frameworks & Inter-Agent Communication
- 01:43:08 Deconstructing Production Failures & Error Handling
- 01:52:20 Grounding Data & Prepackaged Frameworks (LangChain, Mastra)
- 02:00:25 Hands-On Lab: Building a Production Personal Assistant

Section 6: Deep Case Study (OpenClaw)
- 02:09:25 OpenClaw Codebase Tour & File Architecture
- 02:14:39 Custom Layouts, Terminal Interfaces, & Concurrency Locks
- 02:30:44 Testing Non-Deterministic Agents & LLM-as-a-Judge Evals
- 02:39:30 Observability, Tracking Performance Metrics, & Optimization Levers
- 02:51:55 Agent Security, Advanced Prompt Assemblies, & Wrap-up

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Welcome to this video course on LangGraph, the powerful Python library for building advanced conversational AI workflows. In this course, Vaibhav Mehra will teach you how to design, implement, and manage complex dialogue systems using a graph-based approach. By the end, you'll be equipped to build robust, scalable conversational applications that leverage the full potential of large language models.

Code: https://github.com/iamvaibhavm....ehra/LangGraph-Cours

Vaibhav Mehra on LinkedIn: https://www.linkedin.com/in/vaibhav-mehra-main/
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❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

⭐️ Contents ⭐️
0:00:00 Introduction
0:01:24 Type Annotations
0:08:35 Elements
0:18:39 Agent 1 Intro
0:21:08 Agent 1 Code
0:32:22 Agent 1 Exercise
0:33:07 Agent 2 Intro
0:34:02 Agent 2 Code
0:43:27 Agent 2 Exercise
0:44:29 Agent 3 Intro
0:45:18 Agent 3 Code
0:54:04 Agent 3 Exercise
0:55:36 Agent 4 Intro
0:56:37 Agent 4 Code
1:13:09 Agent 4 Exercise
1:14:14 Agent 5 Intro
1:15:45 Agent 5 Code
1:29:19 Agent 5 Exercise
1:31:01 AI Agent 1 Intro
1:32:49 AI Agent 1 Code
1:42:54 AI Agent 2 Intro
1:43:58 AI Agent 2 Code
2:03:20 AI Agent 3 Intro
2:05:00 AI Agent 3 Prerequisite
2:12:28 AI Agent 3 Code
2:28:46 AI Agent 4 Intro
2:30:59 AI Agent 4 Code
2:52:09 RAG Agent Intro
2:53:01 RAG Agent Code
3:06:15 RAG Agent Testing
3:09:04 Course Outro

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As AI agents become more capable, the skills needed for AI jobs are shifting. Bri Kopecki breaks down the 7 skills you need to move from prompt engineering to full agent engineering, including system design, retrieval, reliability, and security. Learn how to build AI agents that actually work in production 🚀.

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00:00 Intro
00:03:21 Basics of AI Agents & Automations
00:08:24 Auto Email Reply System
00:40:32 Inventory Management System
01:25:20 Jarvis System-Voice Command
02:25:54 Website Downgrade or Upgrade Management
02:33:36 AI Voice Calling Agent
03:56:39 Selling Psychology
04:23:32 Outro


In this video, Badar Munir founder of Make First Million and creator of Aaghaz – Asia’s First AI Startup & AI Master Institute, shares how smart people are preparing for the AI era — and why mastering AI Agent is becoming essential for every professional.

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Learn Agentic AI from scratch with this complete course covering the fundamentals, architecture, frameworks, and real-world concepts behind modern AI agents. Start by understanding the difference between Generative AI and Agentic AI, explore how Generative AI systems work, and learn the fundamentals of LangChain, AI Agents, and Agentic AI Design Patterns.

This course also covers Agentic Frameworks, Embeddings, Large Language Models (LLMs), and RAG Agents, helping you understand how AI systems can retrieve information, reason over context, and perform tasks autonomously. You’ll also learn about Model Context Protocol (MCP) and its role in connecting AI models with external tools and data sources.

Whether you’re a developer, AI/ML enthusiast, or someone looking to build the next generation of intelligent applications, this Agentic AI course gives you a structured foundation to understand and build AI agents. Follow the timestamps below to jump directly to the topic you want to learn.

📖 Below are the concepts covered in the video on "Agentic AI Full Course":
00:00:00 – Introduction to Agentic AI Course
00:00:37 – Generative AI vs Agentic AI
00:10:16 – What is a Generative AI System?
01:03:07 – What is LangChain
01:22:51 – AI Agents
01:41:09 – Agentic AI Design Patterns
02:32:56 – Agentic Frameworks & Basics
04:33:44 – Embeddings
05:05:12 – Large Language Model
06:53:23 – RAG Agent
07:51:25 – Model Context Protocol

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➡️ About the Course
The Agentic AI Systems & Design course by Intellipaat is an advanced program focused on building autonomous AI agents that can plan, reason, and execute tasks using tools like APIs, coding environments, and web search. Unlike traditional Generative AI courses, this program goes beyond prompt-based outputs and teaches you how to design production-grade AI systems capable of independent decision-making and complex problem-solving. It is ideal for professionals such as data scientists, AI/ML engineers, and developers who want to move from basic AI usage to creating intelligent, real-world applications powered by agentic workflows

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Is AI-assisted coding the future? 🤔 Cedric Clyburn explores spec-driven development, a game-changing approach that combines LLMs with software development best practices. Learn how it differs from vibe coding, integrates SDLC principles, and improves coding workflows with requirements-driven precision. 🚀

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Complete Agentic AI Course - AI Agents, RAG, Embeddings, Architectures, Framework, VectorDB & Memory #aiagents #agenticai #aiforbeginners #learnai #ai #2026

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🤖 Learn Agentic AI — AI Agents Explained (A Complete Powerful Course to Master It)

This is the most comprehensive Agentic AI course you'll find — completely FREE. We go from the absolute ground up — AI basics, LLMs, the Agent loop — all the way to Vector Databases, RAG, MCP, Multi-Agent Systems, and real architectures being used in production RIGHT NOW in 2026.

Whether you're a total beginner or someone who's used AI tools but never truly understood them — this video is built for you.

📌 WHAT YOU'LL LEARN IN THIS VIDEO:

✅ What AI actually is — and the 3 eras that brought us here
✅ How Transformers & the Attention Mechanism changed everything (2017 → now)
✅ How LLMs generate text — tokens, temperature, and context windows explained
✅ The EXACT difference between a Chatbot and a true AI Agent
✅ The Core Agent Loop (Perceive → Think → Act → Observe) broken down simply
✅ The ReAct Pattern — how agents reason before every single action
✅ Tools & Function Calling — how agents interact with the real world
✅ Memory systems — Sensory, Working, Episodic, and Semantic memory
✅ RAG (Retrieval-Augmented Generation) — the most important AI dev concept right now
✅ Vector Databases — Pinecone, Weaviate, Qdrant, Chroma & when to use each
✅ Embeddings & Semantic Search — the math made simple
✅ MCP (Model Context Protocol) — the "USB port" for AI, explained clearly
✅ 6 Agentic Architectures — ReAct, Chain-of-Thought, Plan-and-Execute, Tree of Thoughts, Reflexion & LATS
✅ Multi-Agent Systems — Orchestrator-Worker, Parallel, Debate Pattern & more
✅ Frameworks — LangChain, LlamaIndex, AutoGen, CrewAI
✅ Advanced patterns — Self-Modifying Agents, Stochastic Consensus, Iceberg Technique, 60-30-10 Cost Rule
✅ Safety & Guardrails — Prompt Injection, Scope Creep, Human-in-the-Loop & more
✅ Real-world use cases across Enterprise, Healthcare, Finance, Education & Dev
✅ A step-by-step 10-week roadmap to go from zero to building production agents

💡 Key Insight:

A chatbot reacts. An AI Agent plans, decides, acts, and adapts — all on its own. Understanding that difference is worth everything in today's AI landscape.

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- Tejas AI