All in One Artificial Intelligence (AI) Full Course 2024 | AI Tutorial for Beginners [Video]

Explore the world of Artificial Intelligence in this video. We start with the basics and history of AI. Further, the video explains the differences between AI, Machine Learning, and Deep Learning. Next, we dive into Neural Networks and Deep Learning. We’ll cover Multilayer Perceptrons and key tools like TensorFlow and Keras. You’ll also learn about Computer Vision with OpenCV and Convolutional Neural Networks. We then explore Natural Language Processing, focusing on Text Representation, TF-IDF, and models like GloVe. The video also introduces Generative AI, Large Language Models (LLMs), and Prompt Engineering. Finally, see real-world AI projects and case studies, demonstrating how Artificial Neural Networks (ANN) and Deep Learning are used in practice.

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🏁 Topics Covered:
00:00:00 Introduction
00:01:25 Introduction to Artificial Intelligence
00:03:18 History of AI
00:05:13 AI vs Machine Learning vs Deep Learning
00:09:29 Introduction to Neural Networks
00:15:51 Introduction To Deep Learning
01:20:11 What is Back Propagation
01:24:17 How does Back propagation works
01:35:12 What is Back and Forward Propagation
01:39:19 What is Feed Forward Network
01:43:46 Types of Back Propagation Network
01:49:44 Key Points of Back Propagation
01:55:44 Demo for Back Propagation Function Implementation
02:22:43 Human brain vs Neural Network
02:25:58 Introduction for TensorFlow
02:37:28 Getting Started with TensorFlow
02:50:51 How to Install TensorFlow
03:02:10 Demo 1 MNIST Character Recognition with TensorFlow
03:24:17 Introduction to Computer Vision
04:07:21 Filters
04:12:44 Features from Kernels
04:23:00 Case Study on Image Processing
04:31:11 Convolution Layer
4:44:52 Pooling and Padding
05:08:40 CNN Architecture
05:50:56 Transfer Learning
05:52:57 Case Study on Image Classifier
06:37:34 Introduction to OpenCV
06:53:35 Process of OpenCV
07:10:20 How to Install OpenCV
07:15:04 Demo on OpenCV Operations
07:29:55 Introduction to NLP
07:32:13 Tokenization
07:55:21 Sequence to Sequence Model
08:31:21 Introduction to Transformers in NLP
08:46:07 Introduction to Generative AI
09:10:29 What is Prompt Engineering
10:25:33 AI Projects and Case Studies
11:55:22 Summary

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