Welcome to Course 3: Mastering Python's Core Concepts! 🚀

You've successfully built a solid foundation in our first two courses, and now it's time to elevate your programming skills! This third course in our free Python programming series is designed to push you beyond the basics, focusing on applying core concepts to solve practical, real-world problems.

What's In Store?

This course takes a project-based approach, challenging you to use everything you've learned about variables, operators, loops, and decision structures to tackle engaging assignments. We'll be focusing on two main, practical problem domains:

• Improper Fractions Assignment: This assignment will test your mathematical and logical skills by requiring you to manipulate and work with numerical concepts like improper fractions. It's an excellent exercise in data manipulation and precise conditional logic within your Python code. You'll solidify your understanding of arithmetic operations and input validation.

• Everyone's Favourite - Taxes: Assignment: Get ready for a hands-on application of decision structures (if/else) and mathematical expressions! This challenging assignment requires you to model a simplified tax calculation system. It's a perfect example of how programming is used to manage complex rules and varying conditions—a skill that is fundamental to developing effective AI and business applications.

Your Next Step to AI

The ability to translate real-world scenarios (like financial rules or mathematical concepts) into functional code is the hallmark of a confident programmer and the necessary skill set for anyone moving into AI. In this course, you will transition from learning syntax to solving problems.

Let's sharpen those problem-solving skills and take your Python fluency to the next level!

Welcome to the World of Python Programming! 🐍

Ready to embark on a journey that will open doors to the exciting field of Artificial Intelligence? This course, "Introduction to Computer Programming in Python," is your essential first step. Designed as the prerequisite for all subsequent AI courses in this series, it will build the fundamental programming bedrock you need to succeed.

Why Python?

Python isn't just a programming language; it's the lingua franca of modern AI, data science, and machine learning. Its simple, readable syntax makes it the perfect language for beginners, allowing you to focus on logic and problem-solving rather than wrestling with complex code structures. By mastering Python, you're gaining a powerful, versatile tool used by companies and researchers globally.

What You Will Learn

This course is structured to take you from a complete beginner to a confident Python programmer. We'll cover all the core concepts necessary to write functional, efficient code:

• Fundamentals: Understanding Variables, Operators, and creating basic Mathematical Expressions.

• Control Flow: Mastering Conditional Statements (making decisions in code) and Loops (automating repetitive tasks).

• Organization: Learning to write reusable code blocks with Functions and organize complex data efficiently using various Data Structures.

• Object-Oriented Programming (OOP): An introduction to Classes, which will allow you to structure your programs in a robust, intuitive way.

• Practical Application: We'll conclude by introducing Graphing techniques and basic concepts in Mathematical Modelling—the very foundations of data visualization and the quantitative analysis central to AI.

Your Path to AI

Every line of code you write in this course will be a step toward understanding how algorithms work and, ultimately, how to build intelligent systems. Think of this course not just as learning Python, but as gaining computational fluency—the ability to think like a programmer.

Let's dive in and start coding!

No problem. Here is the revised text for the Polynomials and the ReAct Pattern lesson, explaining the concepts in a straightforward, continuous description, perfect for students.

🧩 Polynomials and the ReAct Pattern: AI as Your Research Assistant

This lesson connects the math topic of Polynomials with a powerful AI technique called ReAct Prompting.

What is a Polynomial?

A Polynomial is simply a math expression made up of variables (like x) and numbers (coefficients), combined by adding, subtracting, and multiplying. They are the essential building blocks used everywhere—from modeling a ball's trajectory to calculating interest. For instance, a linear polynomial like 2x+3 models a straight line, while a quadratic one like x2−4x+7 models a curve, like a parabola.

🤖 What is the ReAct Pattern?

The ReAct (Reasoning + Acting) Prompting pattern turns the AI into a smart research assistant that can use external tools. This is key because not every problem can be solved just by the AI thinking; sometimes, it needs to go look stuff up or use a tool like a graphing calculator.

The ReAct process has two parts:

1. Reasoning: The AI first explains its thought process (e.g., "I know I need to check an outside source to be sure.").

2. Acting: The AI then takes a specific action (e.g., "I will search on Google," or "I will use a graphing calculator.").

🔭 Applying ReAct to Polynomials

Students can use ReAct every time their work with polynomials requires information that the AI might not have or when they need to visualize the problem.

• Fact-Checking: If you need to verify a complex polynomial identity (like checking if (x+y)2 is truly equal to x2+2xy+y2), you can use ReAct. The AI's logic will be: "I must check a reliable math website," and its action will be to search for the identity on a site like Wolfram Alpha.

• Visualization: If you need to see what the graph of a polynomial (like y=x2−4x+7) looks like, the ReAct pattern is ideal. The AI's logic will be: "To understand this curve, I need a visual tool," and its action will be to plot the polynomial using a graphing tool like Desmos.

✅ The Big Benefits

Using ReAct when studying polynomials gives you huge advantages:

• Super Accuracy: By using external tools like calculators and search engines, the AI grounds its answers in facts, drastically reducing mistakes.

• Transparency: You see exactly which source the AI checked, making you trust the answer more.

• Expanded Power: The AI can now solve problems that go beyond simple math textbook questions, bridging the gap between theory and the real world.

In summary: If your polynomial problem needs the AI to think in steps, use Chain of Thought. If your polynomial problem needs the AI to use a tool or look up facts, use ReAct.

🧠 Question Refinement: Asking Smarter Questions

You already know that the quality of the AI's answer depends on the quality of your question. The Question Refinement Pattern is the technique that makes the AI a better partner by asking it to critique and improve your questions for you!

Think of it this way: When you're stuck on a tricky math problem, a good teacher doesn't just give you the answer. They suggest a new way to look at the problem, pointing out something you might have missed. The Question Refinement Pattern does the exact same thing with the AI.

How it Works: The Smart Tutor Process

1. Ask Your Question: You start with your initial question (e.g., "Tell me about climate change.").

2. AI Analysis: The AI acts like a smart tutor and analyzes your question, deciding how it could be better, more specific, or more focused.

3. Refined Suggestion: The AI suggests a smarter version of your question (e.g., "Do you mean, 'What are the top three effects of climate change on coastal ecosystems in the next 20 years?'").

4. Your Choice: You choose the refined question or stick with your original.

💻 Using Python for Next-Level Refinement

We make this process even more powerful by using the Python coding concepts you just learned. Students can integrate programming logic directly into their refinement prompts:

• Decision Structures (if statements): You can program your prompt to read the user's initial question and then automatically suggest different refined questions based on the topic.

  • Example: IF the user mentions "history," the prompt suggests adding a date range. ELSE IF they mention "science," it suggests adding a specific experiment.

• Loops (Repeating Actions): You can use a loop to force the process of refinement until you are happy.

  • Example: Your code can make the AI suggest three different ways to refine your question, allowing you to quickly compare and choose the best one before moving on.

🚀 Why Refine Your Questions?

By actively participating in the question refinement process, you get these huge advantages:

• More Accurate Answers: A clear, well-framed question always leads to a precise and actionable response.

• New Insights: A different perspective on your question can unlock knowledge and details you didn't even know to ask for.

• Save Time: You avoid frustrating back-and-forth communication with the AI, getting the detailed answer you need right away.

Remember: The AI is a tool to enhance your learning, not replace it. By actively refining your questions, you deepen your understanding and sharpen your critical thinking skills!

🎯 The Fact Check List Pattern: Your AI Quality Control

Welcome to the Fact Check List Pattern! This module is all about turning you into a super-smart editor for the AI. You already know how to make AI give you Alternative Approaches; now you'll learn how to make sure those alternatives are actually true and reliable.

Think of this pattern as a quality control checklist you force the AI to use on its own answers before it hands them over to you.

🕵️ Why We Need a Checklist

AI is incredibly fast, but that speed often means it makes mistakes. This pattern protects you from two big risks:

1. Model Hallucination (AI Making Stuff Up): You know when AI gives you an answer that sounds totally real but is actually false? That's a "hallucination." We teach the AI to self-check its facts against a list of rules you give it, so it can't just lie with confidence.

2. Outdated Information (The "Old News" Problem): AI models only know what they were taught up to a certain date (the "cutoff date"). The checklist forces the AI to either confirm the facts are current or shout a warning that the information might be old.

🛡️ What This Pattern Does for Your Solutions

Using this pattern on the solutions from your last course helps you make better decisions by ensuring the information is:

• Consistent: The checklist makes the AI follow the same rules every time, reducing messy, contradictory, or biased answers.

• Trustworthy: You’ll get reliable data, not just confident-sounding guesses.

✨ Skills You'll Master

By the end of this module, you won't just ask the AI for information—you'll demand proof! Students will learn to:

• Write "Verification Prompts": Create clear checklists that make the AI check its own sources, logic, and core facts.

• Force the AI to Be Honest: Require the AI to tell you exactly how confident it is in its answer and to flag any parts that it thinks might be shaky or uncertain.

• Combine for Power: Connect this Fact Check List with your Alternative Approaches Pattern to make sure that every diverse solution you generate is also a factually sound solution.

This course builds on the mathematics concepts developed in ALT-L Level 1 to integrate Art into the learning of mathematics and coding. Students learn to model the solar system from first principles, generate art with polar equations and even create a galaxy of stars by combining vectors, calculus and statistics formulae.

• Prompt Engineering Few Shot Prompting

  • Few-Shot Prompting: Teaching AI with a Few Tricks (Lesson)

  • Solving Basic Arithmetic Word Problems Using Few-Shot Prompting (Assignment)

  • Example 2: Finding the Median of a Set of Numbers (Assignment)

  • Example 3: Few shot prompting with action (Assignment)

  • Example 4: Few shot prompting with action and intermediate steps (Assignment)

  • Example 5: Few shot prompting with more context (Assignment)

  • Project using few shot prompting (Assignment)

  • Important concepts (Lesson)

This becomes a self-contained unit where students practice giving AI a few examples and then watching it learn to generalize.

Tired of weird symbols and cryptic commands? This course unlocks the secret world of coding with ChatGPT! Learn how to: Become a code detective: Ask ChatGPT questions and get special codes (like secret languages) to find exactly what you need in your computer files. Imagine finding all your pictures from last year's science fair in a snap! Build awesome tools: Stuck creating parts of your game? ChatGPT can help design the building blocks to get you started, like a super code toolkit! Clean up your code: Sometimes code gets messy, just like your room! ChatGPT can help you organize and understand your code, making it easier to work with. Test your code like a boss: Worried about mistakes? ChatGPT can help find errors and create like practice tests for your code! Speak different coding languages: Need to translate your code from English to computer language? ChatGPT can act as your translator! ChatGPT is your coding buddy, not your replacement! Think of it as a super helpful friend who can: Suggest solutions and answer your coding questions. Help you understand complex coding concepts. Give your confusing code nicknames and make it easier to read. Bonus! Learn about cool coding things like databases and regular expressions, and how ChatGPT can help you unlock their secrets! This course is perfect for YOU if you want to: Become a coding whiz! Make your computer do super cool things! Level up your tech skills!

Ever wondered how those chatbots you see online work? This course explains how Large Language Models (LLMs) are built and used. Imagine a game where you predict the next word in a sentence. LLMs are trained on massive amounts of text data to do this very thing. For example, if you give it the prompt "I love eating", the LLM might guess "bagels with cream cheese" or "out with friends" to complete the sentence. But how does the LLM know this? It learns from labelled training data. Here's an example: Imagine training an LLM to understand the sentiment of restaurant reviews. You might feed it reviews labelled as "positive" (e.g., "The pastrami sandwich is great!") or "negative" (e.g., "Service was slow, the food was so-so."). By processing tons of examples, the LLM learns to identify patterns in language. LLMs can be used in two main ways: Base LLMs: These predict the next word one at a time. If you prompt it with "Once upon a time there was a unicorn," it might create a story about the unicorn. However, if you ask a question like "What is the capital of France?", it might get confused. Instruction Tuned LLMs: These are trained on additional data where the output follows an instruction. For instance, you could give it examples of instructions and good responses. This helps the LLM understand what you want it to do, so it can answer your questions directly (e.g., "The capital of France is Paris"). Here's a cool trick: LLMs work with pieces of text called "tokens" instead of individual words. If you tell an LLM to reverse the letters of "lollipop," it might struggle because it sees "lol," "li," and "pop" as separate tokens. But if you add dashes between the letters ("l-o-l-l-i-p-o-p"), it can see each letter clearly and reverse them correctly. Did you know LLMs can have different conversation styles? You can specify the tone you want the LLM to use in a "system message" and then give it a specific instruction in a "user message." This lets you control how the LLM responds. Remember, LLMs are still under development. They might not always understand your requests perfectly, and it's important to use them responsibly. This is just the beginning! In the next lesson, you'll learn how to use LLMs to build a customer service assistant for an online store.

AI-Powered Teacher Support: A Comprehensive Training Course

This innovative teacher training course leverages the capabilities of a state-of-the-art Artificial Intelligence to streamline teaching tasks and enhance educational outcomes. By harnessing the power of AI, teachers can access personalized support, reduce administrative burdens, and focus on delivering high-quality instruction.

The course utilizes a versatile language model that can process various forms of input, including uploaded files, links, multimedia resources, and Google Workspace documents. This flexibility allows the model to be applied to a wide range of teaching scenarios.

Teachers can interact with the model through a personalized interface, providing prompts and inquiries related to specific teaching tasks such as lesson planning, assessment development, and student support. The model is designed to provide accurate and informative responses, helping teachers address challenges efficiently.

The training course features specialized prompts tailored to support teachers in various educational settings, including IGCSE, IB, Ontario, and more. Teachers can specify the grade level, subject matter, and desired level of difficulty, ensuring that the model's responses are aligned with their specific needs.

By utilizing this AI-powered tool, teachers can save time, improve their teaching methods, and ultimately enhance the learning experience for their students. As the field of AI continues to evolve, we will introduce new features and functionalities to further support teachers and drive educational innovation.