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AI Essentials: A Beginner's Guide to Artificial Intelligence

Name: AI Essentials: A Beginner's Guide to Artificial Intelligence
Rating: 4.1 (38 reviews)

Learn AI fundamentals, machine learning, deep learning, data, AI projects, tools, and responsible AI.

Created byArtemakis Artemiou

Last updated 10/2026

English

English [Auto],

What you'll learn

Understand the relationship between AI, machine learning, neural networks, and deep learning.
Understand how data quality and preparation affect an AI project.
Follow the stages of project selection, preparation, model training, and deployment.
Identify the roles of common AI tools and platforms.
Discuss ethical concerns and the purpose of explainable AI.
Explore selected technical examples and options for continuing your AI learning.

Course content

9 sections • 56 lectures • 6h 15m total length

Welcome to the Course! What Will you Learn?2:34
Discover what you’ll learn, the skills you’ll build, and how this course will benefit you.
What is AI?2:30
Learn the fascinating story of how AI started and where it’s headed.
Why is AI Important Today?2:53
See how AI shapes industries and enriches our daily lives.
Types of AI: Narrow vs. General AI vs. Artificial General Intelligence6:10
Understand the different types of AI and how they work in the real world.
Key AI Terms for Beginners3:41
Master essential AI terms and concepts in a beginner-friendly way.
Section Recap2:35
This lecture, is a recap of what you have learned in this section.
Introduction to Artificial Intelligence

Section Overview1:37
Get a clear picture of what you’ll learn and accomplish in this section.
Machine Learning vs. AI: What's the Difference?3:04
Learn the difference between AI and machine learning with simple examples and visuals.
The Role of Data in AI3:37
Discover why data is the fuel for AI.
Algorithms and Decision Making4:12
Understand how algorithms help AI make decisions using relatable examples.
Introduction to Neural Networks4:48
Learn how neural networks work and why they’re essential in AI.
Section Recap1:32
This lecture, is a recap of what you have learned in this section.
Core AI Concepts

Section Overview2:09
This lecture, is an overview of what you will learn in this section.
Selecting a Real-world Project Idea15:10
Learn how to identify and choose a real-world problem for your AI project.
Data Collection and Preprocessing16:23
Find and prepare data for your AI project through practical steps.
Model Development and Training27:21
Build and evaluate your first AI model using beginner-friendly tools.
Project Showcase and Deployment32:01
In this lesson, we'll emphasize the significance of showcasing your AI project and make it accessible via deployment. Effective visualization and strong communication skills are key components to convey your project's value.
Section Recap2:39
This lecture, is a recap of what you have learned in this section.
Building Your AI Project

Section Overview1:13
This lecture, is an overview of what you will learn in this section.
Emerging Trends in AI5:51
Explore emerging AI trends, including healthcare innovations, autonomous systems, NLP, and AI's role in finance and education. Stay ahead with insights into AI's latest advancements and applications.
The Societal Impact of AI12:06
Discover AI's societal impact, from daily life to workplaces. Learn how AI transforms industries, reshapes job roles, and introduces ethical challenges. Explore its future potential and role in shaping society.
Ethics and Governance in AI20:57
Explore the key ethical challenges and governance in AI. Learn about frameworks that ensure responsible AI development and deployment while promoting transparency and accountability.
Preparing for the AI-Driven Future16:17
Discover how to prepare for an AI-driven future. This lesson covers the impact of AI on industries and daily life, the skills you need to thrive, and how policies shape AI use. Learn practical steps to integrate AI into your personal and professional life effectively.
Human Brain vs. Neural Networks5:55
In this lecture, we’ll be comparing the human brain and neural networks, in an attempt to better understand how AI achieves this great functionality, that is deep learning.
Explainable AI: Decoding the Mechanics Behind AI Decisions6:53
In this lecture, we’ll explore why explainability in AI is essential, the techniques available to demystify AI, and the real-world applications and implications of explainable AI. Let’s dive in and uncover the insights behind the AI black box.
Building a Simple Text-to-Speech App with Sentiment Analysis in Python18:44
In this lecture and live demonstration, we enter a more practical level in the AI field, where we develop step-by-step a simple yet effective Python application that converts text to speech while adjusting the tone based on sentiment analysis, that is a technology of Natural Language Processing (NLP).
Agentic AI in 2025: Definition, Trends, Impact, and Practical Strategies8:31
In this lecture, we’ll explore what Agentic AI is, its defining trends, characteristics, and how it enhances automation. We’ll also examine the sectors it will revolutionize and the challenges we must address to implement it responsibly.
Using LangChain with Microsoft Learn Docs MCP Server9:53
In this lecture, I’ll walk you through a demo implementation of how to use LangChain to connect to the Microsoft Learn Docs MCP server, discover available tools, and invoke a semantic documentation search.
Advanced Topics

Section Overview1:47
This lecture, is an overview of what you will learn in this additional module.
Career Opportunities in AI14:08
In this lesson, we'll delve into the exciting world of career opportunities in AI. You'll discover various AI career paths and the skills and qualifications needed to excel in this dynamic field. We'll also explore the potential for career growth and development in AI, paving the way for your own success. Additionally, we'll guide you in building a personalized AI career plan, ensuring you're on the right path to a fulfilling and thriving AI career. Join us to unlock the vast potential that AI offers in shaping your professional journey.
Joining AI Communities15:54
In this lesson, we'll explore the power of joining AI communities, understanding the key advantages they offer. You'll discover the immense benefits of knowledge sharing, networking opportunities, and community support. These connections will not only foster professional growth but also enhance your skills in real-world problem-solving. Join us to harness the collective wisdom of AI communities and propel your journey in the AI landscape.
Next Steps in your AI Journey2:04
In this lesson, we'll chart the next steps in your AI journey, guiding you towards a successful future in this dynamic field. We'll emphasize the importance of continual learning and specialization, ensuring you stay at the cutting edge of AI. You'll also learn how to apply your knowledge practically, establish valuable networking connections, and explore the significance of mentorship and leadership. Join us to build a roadmap for your continued growth and impact in the world of AI.
Section Recap2:51
This lecture, is a recap of what you have learned in this section.
Your AI Journey Ahead

Latest Developments in AI Transforming Team Dynamics2:05
Explore innovative concepts in human-AI collaboration, shaping the future of teamwork.
AI Ethics and Responsible AI0:18
In this lecture, I share a useful infographic I created, regarding the ethical dilemmas of Artificial Intelligence, as well as techniques that can lead to a more responsible development of Artificial Intelligence systems.
Types of Learning in Artificial Intelligence (Infographic)0:55
Explore AI's diverse learning methods in our enlightening lecture. From supervised to self-supervised learning, discover key concepts with this engaging infographic.
The Evolution from Data to Big Data and Artificial Intelligence5:05
In this lecture, we'll be exploring the evolution of data processing from mainframes to the era of Big Data, culminating in the transformative impact of Artificial Intelligence and Machine Learning in the 2020s.
Enhancing Financial Services with AI6:54
In this special topic, we’ll be discussing about how AI can enhance Financial Services.
The Evolution of Artificial Intelligence (AI)4:13
In this lecture, you'll be learning how AI evolved over the years, to the transformative technology as we know it today.
How Collaborative Intelligence is Transforming the Workplace7:00
In this lecture, we’ll be discussing about how collaborative intelligence is transforming the workplace.
Prompt Engineering: How to Get AI to Do What You Want3:23
In this lecture, we'll be discussing about prompt engineering techniques and how they affect the quality of chatbot responses.
Understanding Inference - The Key to AI's Intelligence4:24
In this lecture, we dive into AI inference - the process that enables AI to analyze new, unforeseen data and make intelligent choices.
AI Agents Explained: Beyond If/Else Logic and Hype2:41
In this lecture, we'll be demystifying AI agents by answering a common question: What’s an AI agent, and how is it different from regular software?
A Practical Guide to the Layers of Artificial Intelligence2:56
In this lecture, we'll be discussing about the different layers of Artificial Intelligence, their main characteristics, and why is important to identify them.
Making AI Work for You—Not Instead of You2:11
In this lecture, we are diving into a crucial topic, that is how we can harness AI as a powerful teammate, not just a tool.
The Evolution of Collaborative Intelligence6:58
In this lecture, we’re exploring the past, present, and future of Collaborative Intelligence — the journey from static tools to dynamic human–AI ecosystems.
The Power of Explainability in AI Leadership2:34
In this lecture, we'll be discussing about the power of explainability in AI leadership and how it turns numbers into strategy.
The Hidden Layer in AI Architecture: Governance1:49
In this lecture, we’ll explore what it really takes to build trustworthy AI systems—from the raw data all the way to responsible governance. This is the true architecture of modern intelligence.
AI in IT — 5 Hard Truths Leaders Must Face2:55
In this session, I’ll share five practical truths that determine whether AI adoption creates value — or remains just an experiment.
GenAI Programming: The Core Building Blocks4:54
In this video, I’ll walk you through the core building blocks you’ll find in almost every GenAI solution, regardless of the tools you use.
Human Review That Can Change the Outcome2:57
In this lecture, we’ll explore human review that can change the outcome. We’ll follow a small example and pause for a decision along the way.

Requirements

Basic computer skills and an interest in learning AI are required.
No previous AI knowledge is assumed for the introductory lessons.
The course includes technical examples; reproducing them may require the tools and basic coding knowledge used in those lessons.

Description

Build a clear and practical understanding of Artificial Intelligence and learn how the main concepts fit together.

This beginner-friendly course introduces you to the foundations of Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, data, AI tools, and the AI project life cycle.

Rather than focusing only on terminology, the course helps you understand how the different pieces of an AI solution connect, from selecting a problem and preparing data to training, evaluating, and deploying a model.

Understand the Foundations of Artificial Intelligence

Artificial Intelligence is a broad field, and many of its terms are often used interchangeably.

This course helps you understand the differences and relationships between concepts such as:

Artificial Intelligence.
Machine Learning.
Neural Networks.
Deep Learning.
Data and training data.
AI models.
AI tools and platforms.
Model training and deployment.
Explainability and responsible AI.

The goal is to give you a solid mental model of how modern AI systems work and where each concept fits.

Understand How an AI Project Comes Together

Knowing individual AI terms is useful, but understanding the full process is even more important.

A guided project walkthrough connects the concepts and introduces the main stages of an AI initiative, including:

Selecting an AI problem.
Understanding the role of data.
Preparing and working with data.
Training a model.
Evaluating results.
Deploying an AI solution.
Considering responsible and ethical use.

This gives you a broader understanding of what happens behind an AI model and how different activities contribute to a complete solution.

Understand the Role of Data in AI

Data is one of the most important components of any AI system.

You will explore why the quality, relevance, and preparation of data can significantly influence AI outcomes and why an AI solution involves much more than selecting an algorithm or model.

This foundation will help you better understand topics such as machine learning, analytics, model training, and AI development as you continue your learning journey.

Explore AI Tools, Technologies, and Applications

The course introduces the broader technology environment surrounding AI and explores how AI is being applied across different industries and use cases.

You will gain context around:

AI development tools and platforms.
Machine Learning technologies.
Neural networks and Deep Learning.
Practical AI applications.
Selected Python examples.
Emerging AI concepts and approaches.

These topics help you connect fundamental AI theory with the technologies and applications you may encounter in real-world environments.

Understand Responsible AI

AI systems can influence decisions, users, organizations, and society.

For this reason, understanding AI also means understanding some of the challenges associated with its use.

The course introduces important topics such as:

AI ethics.
Bias.
Explainability.
Responsible use of AI.
The impact of AI systems.

These concepts provide an important foundation for thinking about AI beyond technical performance alone.

Build a Foundation for Your AI Journey

Artificial Intelligence covers many different career paths and areas of specialization.

After building the fundamentals, you will explore directions you can continue into, including:

Machine Learning.
Deep Learning.
Generative AI.
Python for AI.
Data Science.
AI engineering.
AI architecture.
Responsible AI and governance.

The aim is to help you move from a broad interest in Artificial Intelligence toward a clearer understanding of which areas you may want to explore next.

Who Is This Course For?

This course is suitable for:

Complete beginners who want to understand Artificial Intelligence.
Professionals who work with AI initiatives but are not AI specialists.
Students exploring AI, Machine Learning, or Data Science.
Developers and IT professionals beginning their AI journey.
Business and technology professionals who want to communicate more confidently about AI.
Anyone interested in understanding how AI systems and AI projects work.

No previous AI or Machine Learning experience is required.

Basic computer knowledge and curiosity about Artificial Intelligence are enough to get started.

Some later lessons include technical examples, but the core concepts can be followed without an extensive programming background.

Build Your AI Foundation

By the end of the course, you will understand the main concepts behind Artificial Intelligence, how Machine Learning and Deep Learning relate to AI, why data is critical, and how the main stages of an AI project fit together.

You will also have a clearer understanding of AI tools, responsible AI, practical applications, and the different paths available for continuing your learning.

If you want to understand Artificial Intelligence from the ground up before moving into more specialized topics, this course provides a structured and approachable starting point.

Who this course is for:

Beginners seeking a broad foundation in AI concepts and project stages.
Students and professionals who want to understand how data and models come together.
Learners who want a guided AI project walkthrough before choosing more specialized training.

AI Essentials: A Beginner's Guide to Artificial Intelligence

What you'll learn

Explore related topics

Course content

Introduction to Artificial Intelligence6 lectures • 20min

Core AI Concepts6 lectures • 19min

AI Tools and Platforms (New)3 lectures • 11min

Building Your AI Project6 lectures • 1hr 36min

Advanced Topics (Updated)10 lectures • 1hr 46min

Your AI Journey Ahead5 lectures • 37min

Special Topics (Updated)18 lectures • 1hr 4min

Putting it All Together1 lecture • 14min

Learning More1 lecture • 8min

Requirements

Description

Who this course is for: