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This Introduction to Artificial Intelligence Full Course by Simplilearn provides a complete roadmap to mastering AI. It begins with an introduction to artificial intelligence, its future, and essential concepts like machine learning, deep learning, and generative AI. You’ll explore AI careers, the AI engineer roadmap, and how to land a job in this field. Dive into practical tutorials on using AI for coding, creating digital products, and building websites. Advanced topics include transformers, reinforcement learning, Q-learning, and LangChain. You’ll learn about AI bias, multimodal AI, and concepts like LLMs, benchmarking, and running models privately. The course also covers prompt engineering, data analytics with AI, and AI-powered tools like Cursor AI. Additionally, you’ll get hands-on with projects for beginners, including machine learning and AI, and explore top hackathons in India. Finally, you’ll gain insights into interview preparation and how to earn money using tools like ChatGPT.

00:00:00 Introduction To Artificial Intelligence Full Course
00:08:08 Introduction To Artificial Intelligence
00:17:14 Future Of Artificial Intelligence
00:24:13 Deep Learning Tutorial For Beginners
00:36:17 Best Artificial Intelligence Coding tools
00:45:55 Top 10 Technologies To Learn
00:52:37 Artificial Intelligence Careers Tutorial
01:04:31 How Create And Sell Digital Products Using AI
01:05:16 What Is Generative AI
01:15:48 Artificial Intelligence Engineer Roadmap
01:30:53 How To Get A Job In Artificial Intelligence
02:27:30 What Is Machine Learning
02:33:11 AI Bias
02:38:53 Open AI Strawberry
02:45:04 Difference between Artificial Intelligence, Machine Learning, And Deep Learning
02:46:11 Run Lama Privately
02:54:55 Introduction To LLM
03:08:24 LLM Benchmarkeing
03:14:01 Mutimodel Artificial Intelligence
03:33:52 Chatgpt Analyse Tutorial
03:34:53 Transformers In Artificial Intelligence
06:59:07 Reinforcement Learning
06:59:57 Q Learning
07:22:58 Langchain Explained
07:37:26 What Is Prompt Engineering
07:59:49 Data Analytics Using Artificial Intelligence
08:01:31 Cursor AI
10:17:41 Recurrent Neural Network
10:35:26 Machine Learning Projects For Beginners
11:01:07 Build Website With Artificial Intelligence
11:19:18 Confusion Matrix In Machine Learning
11:27:32 Top Hackathons In india
11:40:51 Artificial Intelligence Projects With Source Code For Beginers
11:46:00 Generative AI Tools For Job Interview
13:41:50 Artificial Intelligence and Deep learning Interview Questions
13:42:50 How To Earn Money Using ChatGPT

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- Post Graduate Program certificate and Alumni Association membership
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Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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An introduction to logistic regression and the perceptron algorithm that requires very little math (no calculus or linear algebra), only a visual mind.

0:00 Introduction
0:08 Series of 3 videos
0:41 E-mail spam classifier
7:19 Classification goal: split data
11:36 How to move a line
12:21 Rotating and translating
18:47 Perceptron Trick
23:20 Correctly and incorrectly classified points
24:20 Positive and negative regions
27:18 Perceptron Error
29:40 Gradient Descent
34:36 A friendly introduction to deep learning and neural networks
37:48 Activation function (sigmoid)
38:31 Log-Loss Error
41:37 Perceptron Algorithm
42:45 Logistic regression algorithm
44:48 Thank you!

MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024
Instructor: Peter Kempthorne
View the complete course: https://ocw.mit.edu/courses/18....-642-topics-in-mathe
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

The lecture introduces linear algebra with a focus on its applications in quantitative finance, covering vector and matrix fundamentals, portfolio valuation, and concepts like short selling, arbitrage, and contingent claims. It further explores stochastic matrices and Markov chains, eigenvalues and eigenvectors, and their roles in modeling financial markets, culminating in discussions on no-arbitrage conditions, market completeness, and pricing measures essential for option pricing theory.

License: Creative Commons BY-NC-SA
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Non-technical friendly course explaining machine learning and deep learning that everyone can understand.

Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

If you want to master AI agents in 2026 and actually build systems that can reason, plan, and take action on their own- this video is your complete roadmap.

I walk you through exactly what you need to learn, in what order, and which free resources will actually get you there- without wasting months chasing hype.

The space feels overwhelming right now. Everyone's throwing around words like agents, autonomy, multi-agent systems, tool use, planning, memory- and it all sounds exciting, but also kind of chaotic.
You're not too late. But you do need a plan. That's exactly what this video gives you.

Chapters:
00:00 – Why You Need a Roadmap for AI Agents
01:08 – Prerequisites: Python, APIs, ML Fundamentals
03:25 – Month 1: Foundations & Architecture
05:17 – Month 2: Agent Frameworks & Memory
06:55 – Month 3: Tools, APIs & Multi-Agent Systems
08:06 – Month 4: Evaluation, Safety & Deployment
08:55 – Month 5-6: Specialization, Advanced Topics & Capstone
10:12 – Final Advice & Resources

Free Resources Mentioned:

Prerequisites-
Google's Python Class: https://developers.google.com/edu/python
Python for Everybody (Charles Severance): https://www.py4e.com/

Month 1: Foundations

Hugging Face Agents Course: https://huggingface.co/learn/agents-course

Month 2: Frameworks & Memory

LangGraph Documentation: https://www.langchain.com/langgraph
CrewAI Documentation: https://docs.crewai.com/

Month 3-4: Tools, Multi-Agent & Production

OpenAI Function Calling Guide: https://platform.openai.com/do....cs/guides/function-c
LangSmith (Evaluation): https://www.langchain.com/langsmith

Month 5-6: Advanced & Capstone

Berkeley LLM Agents Course: https://llmagents-learning.org/f24

Drop a comment: Where are you in your AI agents journey? Just starting, already building, or stuck somewhere in the middle?

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdbhXe

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Ever hit a wall with your biggest LLM? 🚧 Shad Griffin shows how Agentic AI, AI agents, and prompt engineering turn complex problems into smart, multi‑step solutions powered by machine learning. Discover how this workflow helps developers build more accurate AI results.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdbLhV

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While there have been recent advances in few-shot image stylization, these methods fail to capture stylistic details that are obvious to humans. Details such as the shape of the eyes, the boldness of the lines, are especially difficult for a model to learn, especially so under a limited data setting. In this work, we aim to perform one-shot image stylization that gets the details right. Given a reference style image, we approximate paired real data using GAN inversion and finetune a pretrained using that approximate paired data. We then encourage the GAN to generalize so that the learned style can be applied to all other images.

The Intelligent Inspection toolset, powered by Deep Learning technology, provides powerful and robust on-device anomaly detection as well as object classification that is not possible with rule-based machine vision.

The user-friendly interface, on-device traning and example-based approach makes it easy to ensure that inspected items fulfill required quality and sorting demands, which helps to improve yield, reliability, productivity and increase customer satisfaction.

Intelligent Inspection toolset is part of the SICK Nova 2D SensorApp and runs directly on the InspectorP6xx 2D vision sensors.

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