Neural Networks Basics | EDUCBA

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AI Fundamentals and Core Concepts

Introduction to Artificial Intelligence

Definition and Brief History of AI (15:31)
AI Ethics and Societal Impacts (9:40)
Importance and Applications of AI (2:31)

Foundations of Artificial Intelligence

Introduction (1:04)
Logic and Reasoning (6:42)
Probability and Statistics (5:01)
Search Algorithms (2:48)
Knowledge Representation and Reasoning (4:49)

Machine Learning of Artificial Intelligence

Introduction to Machine Learning AI (3:43)
Supervised Learning (6:14)
Unsupervised Learning (6:20)
Clustering (9:27)
Distance Measures (5:00)
Dimensionality Reduction (13:47)
Association Rule Learning (11:49)
Reinforcement Learning (11:04)
Types of Reinforcement Learning Part 1 (13:15)
Types of Reinforcement Learning Part 2 (14:42)

Deep Learning

Neural Networks Basics (14:51)
Deep learning introduction (8:16)
Convolutional Neural Networks (CNNs) (13:41)
Recurrent Neural Networks (RNN) (19:41)
Generative Models (18:31)
Transfer Learning and Fine Tunning (8:34)

AWS Certified AI Practitioner

Introduction to AWS Certified AI Practitioner (2:24)
Understanding AI and ML (3:15)
Natural Language Processing (NLP) (1:21)
Computer Vison (CV) (1:30)
Applications of AI in Various Industries (1:33)
Supervised vs Unsupervised Machine Learning (1:15)
Algorithms of Supervised and Unsupervised Machine Learning (1:37)
Reinforcement Learning (RL) (1:20)
Principal Component Analysis (PCA) (0:31)
Basic Questions (9:02)
Introduction to AWS AI Services (0:24)
Amazon SageMaker (4:14)
Aws DeepLens (3:20)
Amazon Comprehend (3:25)
Case Studies (1:40)
Intermediate Questions (15:14)
Implementing AI Solutions with AWS (4:26)
Woring with Amazon SageMaker (5:49)
Using AWS Lex (1:41)
Using AWS Polly (1:11)
AWS Rekognition (1:16)
Combining AWS Services (2:01)
Understanding Foundation Models (1:20)
Model Selection and Architecture (1:18)
Data Preperation and Preprocessing (1:19)
Model Training and Optimization (1:08)
Model Evaluation and Deployment (1:28)
Summary (0:29)
Ethical Considerations and Best Practices in AI-ML (3:17)
Introduction to Prompt Engineering (5:41)
Continuous Improvement (0:24)
Exam Overview (3:35)
Advanced Questions (17:03)

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Neural Networks Basics

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