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Introduction to Artificial Intelligence (AI)
Rating: 3.0 out of 5(1 rating)
3 students
Created bySrini Vanamala
Last updated 12/2025
English

What you'll learn

  • How AI really works (without jargon)
  • The evolution from traditional programming to AI
  • How AI sees, understands, and generates content
  • Training vs inference: how AI learns and responds
  • The full AI lifecycle and real-world usage

Course content

1 section • 8 lectures • 1h 45m total length

  • The Problems with Traditional Computer World16:15
  • Traditional Computer
  • Need For Shift to Intelligence-Based Systems18:54

    Explore why we shift from rule-based computing to pattern-based artificial intelligence, learning from data to build intelligent systems that predict, adapt, and improve through feedback.

  • Quiz 2
  • How AI Is Built17:36

    Explore how artificial intelligence learns from data by training models, adjusting parameters, and predicting outcomes through feedback loops, with emphasis on data quality and learning types.

  • Quiz 3
  • The Core Logic Behind Artificial Intelligence18:25
  • Quiz 4
  • Layers of Artificial Intelligence6:01
  • Quiz 5
  • Six Core Pillars of Modern AI Systems17:21
  • Key Concepts in AI7:53

    Explore key AI concepts like large language models, tokenization, vectorization, embeddings, attention, semantic search, self-supervised learning, and fine tuning to understand how predictions drive language intelligence.

  • Real World Use Cases of AI3:26

    Explore real world AI use cases across language, image, audio, video, and personalization by examining large language models, inference, image generation, voice and video creation, and vision AI.

Requirements

  • No prior AI or programming knowledge required
  • Basic computer usage familiarity
  • No mathematics or statistics background needed
  • Suitable for all backgrounds

Description

Artificial Intelligence is everywhere today — but most people learn it in the wrong order.

They jump straight into tools, buzzwords, or coding, without understanding how AI actually works under the hood. This course fixes that problem.

What this course is about

This course is a concept-first, beginner-friendly journey into Artificial Intelligence.
Instead of teaching AI as magic or complex mathematics, you’ll learn how intelligence is built step by step, starting from traditional computers and moving toward modern AI systems.

You’ll understand:

  • Why traditional computers fail in real-world problems

  • How AI shifts from rules to learning

  • How machines learn from data

  • Why AI predictions are probabilistic, not “right or wrong”

  • How concepts like features, vectors, similarity, and probability work together

Everything is explained using simple language, real-world analogies, mental models, and visuals — not academic theory.

What you will learn

By the end of this course, you will clearly understand:

  • How traditional computers work and why they are not intelligent

  • Why the world moved from rule-based systems to AI

  • How machines learn patterns from data

  • What “prediction” really means in AI

  • The difference between prediction and decision-making

  • How AI uses numbers, vectors, and similarity to think

  • The relationship between AI, Machine Learning, and Deep Learning

  • Where AI succeeds — and where it fails

You will not just memorize terms — you will understand the logic behind them.

Who this course is for:

  • Complete beginners curious about AI
  • Students and non-technical learners
  • Working professionals from any domain
  • Educators, trainers, and content creators
  • Entrepreneurs and decision-makers

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