"What AI Courses Should I Take?"

The most common question I receive isn't about AI itself—it's about how to learn AI effectively. Whether you're looking to enhance your current role or pivot into a new one, this guide will help you navigate the complex landscape of AI education and maintain your knowledge over time. I generally don’t recommend anything I don’t have personal experience with, but I will give you an approach to help you find what’s most relevant for you.

First, Know Your "Why"

Before diving into specific recommendations, let's be clear about your goals. I've found that most people fall into one of these categories:

• You want to use AI to be more effective in your current role
• You're worried about staying competitive in an AI-enhanced workplace (FOMO)
• You're looking to pivot into an AI-focused role
• You need to understand AI to lead implementation or governance for your organization

Your path will look different depending on your starting point and destination. Let's break it down.

For Business Professionals: Focus on Application

If you're looking to enhance your current role, good news: you don't need to become an AI expert. What you need is practical, hands-on experience with Generative AI tools relevant to your work.

Start Here:

1. Choose your primary AI chatbot (ChatGPT, Claude, Copilot, or Gemini). Choose what’s allowed and paid for by your company.

2. Take a foundational course in practical AI chatbot usage specific to your primary chatbot. This is not a general AI trends course, this should be hands-on practice, with projects to get you actually using AI for your work.

Good examples of this type of course:

• LinkedIn Learning’s “Copilot for Microsoft 365”
• Maven’s “AI Lab: Automate Tasks with AI”
• Your company's internal training (if you have an internal Ai chatbot at your company)

3. Take courses specific to your role and use case if you can find it. You can search on your preferred online learning platform for beginner, intermediate courses specific to your role or use case.

Good examples of this type of course:

• LinkedIn Learning’s “Marketing with Anthropic Claude”
• Section School’s “AI for Writing”

Pro Tip: Don't just watch—practice. Take what you learn and immediately apply it to your actual work. Save prompts that work well for you in a file or create a custom GPT/copilot/assistant.

These types of course will help you become more efficient in your existing role and perhaps augment your scope. To stay competitive, learn from other early adopters in a similar role and share your own experiences and learnings.

For Leaders and Decision Makers

Your focus should be on understanding AI's strategic implications and implementation considerations. You want to be able to create, discuss, and drive an AI adoption or implementation strategy based on your learnings. You do want to make sure your course cover all areas of AI, not only generative AI.

Recommended Learning Path:

1. Start with a broad foundation from reputable organizations:

• "AI for Everyone" and “Generative AI for Everyone” on DeepLearning.ai by Andrew Ng - A clear, accessible introduction
• Stanford Online's "AI for Business Professionals" program
• MIT Sloan's "Artificial Intelligence: Implications for Business Strategy"

2. Follow with industry-specific learning: there are AI overviews for Financial Services, for Retail, for Healthcare etc… available. This will get you more detailed examples specific to your industry.

3. As an ex-chief product officer, I do have some experience with direct courses targeted for AI product management:

• Reforge’s Generative AI Products course
• Section’s AI Product Mini-MBA

Key Focus: Understanding capabilities, limitations, and implementation considerations rather than technical details.

Use these courses to connect with other leaders in your industry to have a peer community to discuss your AI strategy ideas, challenges, and learnings. Even though these courses will have a lot of high-level content, make sure specific examples or live sessions from leaders implementing AI today are included.

For Technical Professionals

Your path depends on your current technical background. You may want to pivot into a technical AI role, but it’s unlikely to be the result of a few quick courses.

For Data Scientists/Engineers Looking to Specialize in AI: If you’re already working with machine learning models and algorithms today as part of your data science role, these courses can make you more relevant for new AI initiatives.

• Deep Learning Specialization on DeepLearning.ai
• Natural Language Processing specializations
• LLM finetuning courses

These need to be specific to the tech stack and AI platform your organization is on. MSFT Azure, Google Gemini, Amazon all have their own courses.

For Software Engineers Looking to Build with AI: the most practical route is to get hands on experience building applications, tools, and proof-of-concepts for specific use cases relevant to your organizations.

1. Focus on applied AI and LLM integration, good example course:

• Maven’s Building LLM Applications

1. Start with your platform's AI services:
2. Hackathons: nothing beats actually trying to build an application. Dates are not out yet for 2025, but keep an eye out for these:

Pro Tip: Build real projects. Nothing beats hands-on experience with actual applications. Your portfolio and demo of actual projects will go much further in expanding your existing role or getting a new one developing AI applications and tools.

For Governance, Risk, and Compliance Professionals

You need broad understanding across technical, ethical, and regulatory domains. This is one category where I recommend certifications and not general hands on education in addition to some valuable resources.

Recommended:

1. Ethics in AI certifications from IEEE or similar organizations such as the IAPP. Despite the extremely broad scope of the certification content, going through this ensures you’re better equipped to develop an AI goverance framework for your organization. This certification can be helpful for job and role transitions.

2. AI Risk Category Database: a good resource to keep in mind all the risks you need to be aware of.

3. AI Regulations Tracker: a great page that keeps track of all the emerging AI regulations globally.

Certification in this category is valuable in helping you move into a new role that’s focused on AI governance. Actual experience setting up and executing against an AI governance framework will still be more desirable, but the certification itself will surface you for a role that’s very high in demand and difficult to fill.

Staying Current After Your Initial Learning

The second most common question I get is “how do I keep up with AI?” The courses, certifications, and projects are just the starting point. This field changes every 6 weeks, here’s how to stay current:

1. Monitor Major Developments

- Subscribe to Last Week in AI for weekly news updates

- Follow release notes from major AI platforms (Openai, Anthropic, Meta, Google, Microsoft)

- Listen to yours truly in the monthly AI Afterhours podcast

2. Try It!

- Set aside time weekly to experiment with new capabilities

- Document what works (and what doesn't)

- Build a personal library of effective prompts and workflows

3. Teach Others

- The best way to solidify your knowledge is to share it

- Start a lunch-and-learn series at work

- Document and share your successful use cases

The Most Important Advice

Don't try to learn everything at once. Pick one area, master it, then expand. The key is to:

1. Start with your immediate needs

2. Practice regularly with real work

3. Share what you learn

4. Stay curious but focused

Remember: the best time to start learning was yesterday. The second best time is today.

If you like this content, subscribe to yuying.substack.com to have ongoing access to my full content library.

For a monthly AI overview, checkout my podcast AI Afterhours on Youtube, Spotify, and Apple podcasts with co-host Polly M Allen.