Title: AI Fundamentals: Core Concepts and Principles

Location: US

Company: HP

About this course:
'AI Fundamentals: Core Concepts and Principles' is Course 1 of the HP-edX programme "AI Foundations, Practice, and Leadership."
Across 6 modules and 36 sections, instructor Sathish Jeyakumar takes you from the origins of AI at the 1956 Dartmouth Conference to today's machine learning landscape.
You'll learn what AI actually is (and isn't), why data quality matters more than algorithms, how supervised, unsupervised, and reinforcement learning work, and why the "black box" problem is one of AI's biggest challenges. Every concept is grounded in real-world examples.
No computer science background required. No coding. Just curiosity.
Modules included: Defining AI: From Turing to Today | Data: The Fuel of Intelligence | Supervised Learning | Unsupervised Learning | Reinforcement Learning | The Black Box Problem & Explainability
Receive a verified certificate for free using referral code:6FJGJOTX7MFH5M2H.

What Artificial Intelligence actually is — and isn't

How data quality determines AI success

The difference between supervised and unsupervised learning

How reinforcement learning trains AI through rewards

Why AI's "black box" problem matters

How to separate AI hype from reality

Understand what AI really is — no hype, no fear, no prerequisites. Course 1 of the HP AI Foundations, Practice, and Leadership programme covers AI history, data fundamentals, machine learning types, and explainability. Designed for learners at any stage.
Get a free verified certificate with referral code 6FJGJOTX7MFH5M2H

Duration: 3 weeks

Skills:
- Algorithms
- Artificial Intelligence
- Computer Science
- Data Fundamentals
- Data Quality
- Leadership
- Machine Learning
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning

Curriculum:
- Module 1: Defining AI — From Turing to Today
- Module 2: Data — The Fuel of Intelligence
- Module 3: Machine Learning 101 — Supervised Learning
- Module 4: Machine Learning 102 — Unsupervised Learning
- Module 5: Reinforcement Learning — The Pavlovian Approach

- Module 6: The Black Box Problem