Please note lessons with hands on practices are in separate courses.
Mastery of Advanced Skills
As you grow more comfortable with using AI models, it’s time to take your skills further. Instead of just following examples, you’ll learn to decide when AI is truly useful for a task, review the data needed, choose the best coding approach, and even fine-tune your models. Here’s how you can develop these advanced skills:
Learning Objectives
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Evaluate AI Appropriateness:
- Learn to assess if AI is the right tool for a given problem.
- Check whether the data you have is enough and high quality.
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Choose the Right Coding Approach:
- Decide between using low-code tools (like App Inventor or Power Apps) and full-code programming (using Python libraries).
- Understand the pros and cons of each method.
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Fine-Tuning Models:
- Get hands-on experience adjusting model settings like hyperparameters (e.g., epochs, learning rate) or network structure.
- Learn how these changes affect model accuracy and performance.
Example Activities
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Technology Suitability Debate:
- Task: Look at different AI use cases—such as medical diagnosis, image filtering, or chatbots.
- Discussion:
- “Is AI really necessary for this problem?”
- “Is the available data sufficient and of high quality?”
- Goal: Critically analyze when and why to use AI for specific tasks.
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Hands-On Coding Level Selection:
- Task: Complete the same project using both a low-code tool (like App Inventor or Power Apps) and a full-code solution (using Python libraries).
- Comparison:
- Discuss the advantages and disadvantages of each approach.
- Determine which method best fits different situations.
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Hyperparameter Tuning Practice:
- Task: Train an image classification model.
- Experiment:
- Adjust parameters such as epochs, learning rate, and batch size.
- Record how changes affect the model’s accuracy.
- Goal: Find the optimal settings and understand the impact of each parameter on your model’s performance.
Key Takeaways
- Critical Evaluation: Learn to determine if AI is necessary and effective for solving specific problems.
- Flexible Coding Choices: Understand when to use low-code tools for quick solutions versus full-code methods for deeper control.
- Model Fine-Tuning: Gain practical experience in adjusting AI models to improve their accuracy and performance.
- Real-World Application: These advanced skills will prepare you for more complex AI projects and help you think critically about technology in the real world.
By engaging in these activities, you’ll not only enhance your technical skills but also build a mindset that can evaluate, adapt, and improve AI solutions—an essential ability for the future.
