Udemy

AI for Beginners 2025 : Master the Basics of AI in 1 Hour

Enroll Now
  • 63 Students
  • Updated 3/2025
  • Certificate Available
4.1
(38 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 53 Minute(s)
Language
English
Taught by
Dhruv Jani
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.1
(38 Ratings)
1 views

Course Overview

AI for Beginners 2025 : Master the Basics of AI in 1 Hour

Learn the fundamentals of AI, Machine Learning, and Generative AI —no coding or math needed! Perfect for beginners!

Welcome to "AI for Beginners 2025 : Master the Basics of AI in 1 Hour" – your first step into the fascinating world of AI!

In this course, you'll discover:

  • What AI really is (and isn't!)

  • How Machine LearningDeep Learning powers everyday technology

  • Why Generative AI (Gen AI) like ChatGPT is transforming industries

  • The core concepts we'll unpack together

Perfect for complete beginners, this course requires no technical background – just your curiosity! We'll break down complex ideas into simple, relatable examples, preparing you to:
✓ Understand AI news and trends
✓ Speak confidently about AI terms
✓ Explore potential career paths
✓ Make informed decisions about AI tools

Here’s what you’ll learn:
We’ll start with an Introduction to AI, where we’ll explore what artificial intelligence is and how it’s shaping industries like healthcare, finance, and entertainment.
Then, we’ll dive into the Types of AI—from narrow AI that powers your favorite apps to the dream of general AI that can think like a human.

Next, we’ll explore Machine Learning, the heart of AI. You’ll learn about the steps to build a machine learning model, the importance of training data, and the difference between labeled and unlabeled data. We’ll also break down structured and unstructured data—because not all data is created equal!

Ever wondered how Netflix knows what you want to watch next? That’s Supervised Learning at work! Or how your email filters out spam? That’s Unsupervised Learning. We’ll explain these concepts in simple terms, with real-world examples to make it all click.

But we won’t stop there! We’ll introduce you to Neural Networks and Deep Learning—the technologies behind facial recognition and voice assistants. And get ready to explore the exciting world of Generative AI, where machines can create art, write stories, and even compose music!

We’ll cover Foundation Models, Large Language Models (LLMs) like ChatGPT, and even Diffusion Models—the magic behind AI-generated images. Plus, we’ll dive into Multimodal Models that can understand text, images, and more!

We’ll also explore advanced topics like Retrieval Augmented Generation (RAG) and Model Output Optimization, so you understand how AI systems deliver accurate and useful results. And because AI isn’t just about technology, we’ll discuss the Cost of Building and Running AI Systems and the Ethical Considerations—because with great power comes great responsibility!

By the end of this course, you’ll have a solid understanding of AI concepts, terminologies, and tools. You’ll be ready to take your first steps into the world of AI, whether for a career, a hobby, or just to satisfy your curiosity.

So, are you ready to unlock the power of artificial intelligence? Let’s get started!

No coding • No math • Just clear and simple explanations

Course Content

  • 5 section(s)
  • 21 lecture(s)
  • Section 1 Introduction to Artificial Intelligence
  • Section 2 Introduction to Machine Learning
  • Section 3 Introduction Deep Learning & Generative AI
  • Section 4 Introduction to Generative AI Models
  • Section 5 Cost & Ethical Considerations in AI

What You’ll Learn

  • Introduction to Artificial Intelligence
  • Introduction to Machine Learning
  • Introduction to Neural Networks & Deep Learning
  • Introduction to Generative AI (Gen AI)
  • Large Language Models (LLMs)
  • Steps in building Machine Learning Models
  • Types of Data used in Machine Learning
  • Labeled Data & Unlabelled Data
  • Structured & Unstructured Data
  • Supervised & Unsupervised Learning
  • Reinforcement & Self-Supervised Learning
  • Types of Inferencing - Batch & Real-time
  • Foundation Models in Gen AI
  • Diffusion Models
  • Multimodal Models
  • Introduction to Prompt Engineering
  • Output Optimisation & Fine-tuning
  • Retrieval Augmented Generation (RAG)
  • Cost of building & running AI systems
  • Ethical Considerations in AI


Reviews

  • F
    Francesca Bussotti
    5.0

    easy to understand

  • K
    Kovács Adrián
    4.0

    It explaned clearly the basics.

  • D
    David Nyakundi Bonyi
    4.0

    Clear explanation of basic terminology. I would appreciate having these notes for reference.

  • J
    Jason Bingham
    5.0

    He did a great job!

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed