Fundamentals of Artificial Intelligence (Practical Focus)
Fundamentals of Artificial Intelligence (Practical Focus)
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📖 Lesson 1: AI in a Nutshell
📖 Lesson 1: AI in a Nutshell
Key Topics to Cover:
In this module, you will learn about the definition of AI, essential concepts such as Machine Learning, Deep Learning, and NLP, and finally, real-world touchpoints.
📕 Overall Learning Objetives
📕 Overall Learning Objetives
By the end of this lesson, learners will be able to:
Grasp the definition of AI and its historical evolution (symbolic vs. data-driven).
Understand why AI matters in today’s landscape (business, healthcare, finance).
Get a basic overview of Machine Learning, Deep Learning, and NLP, highlighting their real-world impact.
Recognize daily AI examples (recommendation systems, spam filters) and how they illustrate the shift to learning-based methods.
🧠 1.1 Definition of AI & Core Principles
🧠 1.1 Definition of AI & Core Principles
Brief History of AI and its Evolution
Highlight: Let´s talk about history of AI.
Why it’s useful: Overview of AI's origins and its progression over the years, highlighting key milestones in both symbolic and data-driven approaches.
What is Artificial Intelligence? In 5 minutes.
Highlight: Introduce to AI concept.
Why it’s useful: You’ve now got a clear understanding of what AI is and how it works!
🧠 1.2 Essential Concepts
🧠 1.2 Essential Concepts
AI And Machine Learning and How to used it
Highlight: AI, Machine Learning concept
Why it’s useful: You can understand the main concepts and how they can be applied in the real world
All Machine Learning algorithms
Highlight: Get an intuitive understanding of how machine learning work and how they relate to each other definitions.
Why it’s useful: Help you decide how to pick the right one for your problem.
Additional Optional Resources:
Highlight: Occasionally highlights older “symbolic” AI vs. new data-driven breakthroughs.
Why it’s useful: Short, digestible content for seeing how the field evolved quickly once neural-network–based methods took off.
Highlight: Understand how NLP enables computers to process human language.
Why it’s useful: It enhances communication, automates tasks, and improves data analysis.
🧠 1.3 Real-world Touchpoints
🧠 1.3 Real-world Touchpoints
2025 Predictions: How AI Will Transform Healthcare, Finance, and Retail
Highlight: Understand how AI can impact different areas.
Why it’s useful: It helps you see how the world is being transformed by AI.
AI has the power to transform leadership for the better—the key is in how leaders use it.
AI has the power to transform leadership for the better—the key is in how leaders use it.
Lets see some real cases:
Lets see some real cases:
🗣️ 1. Amazon
🔍 2. Google Assistant
🍎 3. Apple Siri
📞 5. Call Centers
If you're interested in expanding your technical knowledge, we invite you to explore these resources.
Why it’s useful: Quick, approachable demos illustrating the differences between a rule-based script vs. a trained model.
Harvard Business Review (AI Articles)
Why it’s useful: For a broader business audience, clarifying how data-driven AI replaced older, brittle rule systems.
Why it’s useful: offers a structured learning path for beginners and professionals looking to deepen their expertise.
fast.ai - Practical Deep Learning for Coders
Why it’s useful: Great for those wanting to build or modify networks for personal/professional projects.Hugging Face Course
Why it’s useful: Perfect for quickly learning how modern NLP dethrones older statistical or rule-based methods.
📌 Extra Materials to Help You Deepen Your Understanding
📌 Extra Materials to Help You Deepen Your Understanding
You can access a paid Udemy course here. This is an optional resource that can help you expand your understanding of the topic.
Generative AI: Fundamentals & Applications
Highlight: Opens with a fundamental explanation of AI’s evolution, from rule-based expert systems to modern deep learning.
Why it’s useful: Great for learners who need a clear timeline and want real-life success stories early on.
Practical MLOps | Deploy ML Models with CI/CD
Highlight: Though pipeline-focused, it includes a quick historical overview of AI’s shifts and the reasons behind current data-driven dominance.
Why it’s useful: Perfect for building context around how practical AI solutions emerged from historical foundations.
Generative AI: Fundamentals & Applications
Highlight: Covers tangible use cases like content generation, chatbots, and personalization that learners see daily.
Why it’s useful: Great for recognizing how deeply AI is integrated into everyday life (e.g., email filters, streaming recommendations).
Practical MLOps | Deploy ML Models with CI/CD
Highlight: Shows how teams operationalize real-world AI tasks—like Netflix’s iterative approach to recommendation algorithms.
Why it’s useful: Ties examples to the engineering behind them, illustrating the difference vs. older rule-based approaches.
ChatGPT, GPT-4 & Bard Masterclass: The Complete ChatGPT Guide
Highlight: Explains fundamentals of NLP and how large language models differ from older statistical approaches.
Why it’s useful: Perfect for an immediate, practical sense of how modern NLP (like GPT) overshadowed older rule-based text systems.
Complete GitHub Copilot & Code Assistant Guide
Highlight: Though developer-oriented, it clarifies how deep learning–based code assistants operate, bridging ML and NLP fundamentals.
Why it’s useful: Illustrates how advanced ML and NLP are being applied in coding environments daily.
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