<aside> <img src="/icons/location_lightgray.svg" alt="/icons/location_lightgray.svg" width="40px" /> Navigation

<aside> <img src="/icons/flood_orange.svg" alt="/icons/flood_orange.svg" width="40px" /> Home

</aside>

<aside> <img src="/icons/arrow-left_orange.svg" alt="/icons/arrow-left_orange.svg" width="40px" /> Previous Lesson

02-Using WhatsApp on the computer

</aside>

<aside> <img src="/icons/arrow-right_orange.svg" alt="/icons/arrow-right_orange.svg" width="40px" /> Next Lesson

04-Prompts & Prompt Templates

</aside>

Curriculum

<aside>

Untitled

</aside>

The AI Landscape and Key Terms

AI is a branch of computer science that enables machines to perform tasks that normally require human intelligence, such as seeing, understanding, and creating.

The most important type for us is Generative AI, which is a type of AI that can create new content based on the data it's been trained on. Instead of just analyzing or sorting existing data, these models generate new examples:

Types of AI. Source: AI Product Management Certification by Miqdad Jaffer (Product @ OpenAI), published with the permission of Moe Ali (Product Faculty)

Let’s look at the AI landscape and the different machine learning methods that help Generative AI make sense of data and perform these tasks:

The AI Landscape. Source: AI Product Management Certification by Miqdad Jaffer (Product @ OpenAI), published with the permission of Moe Ali (Product Faculty)

Here are four important types of machine learning every PM should know:

Supervised Learning: The model is trained on a labeled dataset, meaning each input comes with the correct output. That’s the basic approach to fine-tuning models you want to use to perform specific tasks in your product.
Unsupervised Learning: The model learns from data without labels. It has to find patterns and structures independently, like solving a puzzle without a picture.
Reinforcement Learning: The model learns by interacting with an environment. It makes decisions and receives feedback in the form of rewards or penalties. Over time, it aims to maximize the total reward. A great example of this was AlphaGo.
Deep Learning: This isn't a separate category but a subset of machine learning that uses neural networks with many layers (hence "deep"). These deep neural networks can learn complex patterns and are applied in supervised, unsupervised, and reinforcement learning.

So, let’s zoom in:

AI- notes and links to free books

<aside> <img src="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/d51f9247-0f4b-4e69-a914-76b31421c4e1/BLANK_PNG.png" alt="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/d51f9247-0f4b-4e69-a914-76b31421c4e1/BLANK_PNG.png" width="40px" /> Currently, this links to the original Google document I built as an FAQ for onboarding clients into AI. Note that this industry is moving fast, so things change as different companies expand and/or drop products and services.

If you're new to AI, I recommend starting with the free PDFs I've provided and gradually working your way into the subject.

Resources:

Free eBooks:

ChatGPT for Dummies.pdf
- Supercharge Your Workday with ChatGPT.pdf </aside>

</aside>

[https://docs.google.com/document/d/1CVFkf5kGZG92FTVMX3PxeF1LdDCSVLaKO1-AxJa1Y38/edit?usp=sharing](https://docs.google.com/document/d/1CVFkf5kGZG92FTVMX3PxeF1LdDCSVLaKO1-AxJa1Y38/preview?usp=sharing)

AI Class Notes

📚Free eBooks:

</aside>

🕧 10-minute video on how to use some of these tools.

🔀Click on the expand arrows to play full-screen

https://content.jwplatform.com/videos/kDvObopy-42TyAa1a.mp4

<aside> <img src="/icons/link_orange.svg" alt="/icons/link_orange.svg" width="40px" /> https://content.jwplatform.com/videos/kDvObopy-42TyAa1a.mp4

</aside>

Untitled