Ai Fundamentals: Core Concepts And Theories For Beginners

Ai Fundamentals: Core Concepts And Theories For Beginners
Published 1/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

What you’ll learn

Understand the impact of data quality on machine learning model performance

Learn how data quantity influences AI accuracy and reliability

Explore techniques for effective data collection and preprocessing

Evaluate the importance of balanced datasets for successful AI applications

Requirements

Basic Computer Literacy: Familiarity with using a computer and navigating the internet

No Coding Knowledge Required: This course is theoretical and does not require programming skills

High School Mathematics: Basic understanding of linear algebra and probability will be helpful but is not mandatory

Description

Overview

Lecture 1 Understand the core concepts and history of AI

Lecture 2 Lesson 2: Brief History of AI: From Turing to Modern AI

Lecture 3 Lesson 3: Types of AI: Narrow, General, and Superintelligence

Lecture 4 Lesson 4: Key Applications of AI in the Real World

Section 2: Module 2: Core Concepts in AI

Lecture 5 Lesson 1: Machine Learning (ML): Types and Overview

Lecture 6 Lesson 2: Knowledge Representation and Reasoning

Lecture 7 Lesson 3: Problem Solving and Search Algorithms

Lecture 8 Lesson 4: Decision Making in AI Systems

Section 3: Module 3: Machine Learning Theory

Lecture 9 Lesson 1 Outline Supervised, Unsupervised, and Reinforcement Learning

Lecture 10 Lesson 2: Mathematical Foundations of ML: Linear Algebra and Probability

Lecture 12 Lesson 4: The Role of Data in ML: Quality and Quantity