Whether for career growth or company success, AI is unavoidable now — and I’ll admit I’m a little late to the party. Better than running around clueless like I used to, but still: before I start learning, I need to get the basics right. At minimum, I should understand the vocabulary. 😅

After hearing so many recommendations, I picked Coursera’s AI For Everyone by Andrew Ng. I’d just finished Chapter 1 and started into Chapter 2 when I realized I finally had a clear picture of how these terms relate to each other.

Key Terms Explained

AI (Artificial Intelligence) can be divided into:

• ANI (Artificial Narrow Intelligence): Specialized in a specific domain — like AlphaGo mastering Go. Because it’s narrow, it doesn’t generalize to other areas.
• Generative AI: The explosive growth in recent years. More versatile and helpful across domains — think ChatGPT and Gemini.
• AGI (Artificial General Intelligence): AI that can do anything a human can do. We might think we’re close, but Andrew Ng believes we’re still very far away.

Machine Learning is a subset of AI. In short, it maps Input → Output (A → B), and is especially well-suited for supervised learning — training on labeled data.

For example: label a bunch of dog photos Yes/No to indicate whether a dog appears, and you can train a model to detect dogs in images. Or use house size, number of bathrooms, and bedrooms to predict rental price.

Deep Learning, often used interchangeably with Neural Networks, is a subset of Machine Learning. It’s particularly good at unstructured data (images, audio, text) as opposed to structured data like spreadsheets.

Deep Learning is also Input → Output, but the input passes through many neurons before reaching the output. It’s a bit abstract — think of house price being influenced by several interacting factors, all combining to produce a predictable output.

Data Science, in Andrew Ng’s framing, sits adjacent to AI with some overlap. It uses data to compute and surface insights, but isn’t the same as the Input → Output mapping of ML.

For example: from house size, bathroom count, and rental price data, you might discover that adding more rooms correlates with higher rent. Or an advertiser finds that travel ads outperform others, so they invest more in travel content.

A Quick Note

Writing this out makes it sound abstract, but Andrew Ng explains everything really clearly in the course. If you want to understand more, it’s genuinely one of the best courses I’ve found.

It’s in English, but there are Chinese subtitles (though I didn’t use them — not sure how accurate they are). Accessible and thorough. I think you’ll enjoy it. 🙂

One warning: don’t watch it at 10pm like I did. I got so hyped I woke up in the middle of the night unable to sleep. 😂

Thanks for reading :D

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