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Interactive Audio Lesson
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Introduction to Modelling
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Today, we're diving into the Modelling phase of the AI Project Cycle. This is where we create models that help us predict outcomes based on our data.
What do you mean by 'models'? Are they like a virtual version of something?
That's a good way to think about it! Models are mathematical representations that learn from data. By exposing them to different data points, they begin to understand patterns that help predict future events. Essentially, they help us make data-driven decisions!
So, how do we start creating these models?
Great question! We start by splitting our data into two parts: the training set, which the model learns from, and the testing set, which we use to evaluate performance later.
Types of AI Models
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When it comes to AI models, we mainly deal with two types: supervised and unsupervised learning. Who can tell me the difference between them?
I think supervised learning uses labeled data, while unsupervised doesn’t?
Exactly! In supervised learning, we train our models using historical data that includes the correct answers, like if an email is spam or not. In unsupervised learning, we don’t have labels, so the model tries to identify patterns by itself, like grouping similar customers.
Can you give an example of each?
Sure! An example of supervised learning could be a model that predicts housing prices based on various features, like location and size. An example of unsupervised learning might be using clustering to categorize users based on their purchasing behaviors.
Training and Testing the Model
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After we've chosen our algorithm and split the data, our next steps are training and testing our model. Can anyone explain why we need to test our model?
So we can see if it actually works, right?
Exactly! Testing allows us to see how well our model predicts outcomes using new data it hasn't seen before. If it performs well, we can be more confident in its predictions.
But what if it performs poorly?
Good question! If the model isn’t accurate, we may need to improve the quality of our data, explore different algorithms, or fine-tune how we train it.
Would it be easier just to change the data instead of the model?
Sometimes, yes! Improving the data can be quicker and more effective than adjusting the model itself.
Practical Application: Food Waste Prediction Model
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Let’s apply what we’ve learned! Imagine we want to predict food waste in our school canteen. First, what kind of data would we need?
We would need information like the number of students and what meals are being served.
Right! And once we have our data, how would we go about building our model?
We would split the data into training and testing, choose an algorithm, and then train it.
Correct! Once trained, we would test it to see how accurately it predicts waste. If it predicts correctly, we can help the canteen reduce food waste effectively.
And if it doesn’t predict well?
Then we review our data and model to identify improvements. The goal is always to provide actionable insights!
Introduction & Overview
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Quick Overview
Standard
In the Modelling section, we explore how to create predictive models that learn from historical data using various algorithms. The process includes splitting the data, selecting the appropriate algorithm, training the model, and validating its accuracy.
Detailed
Modelling in the AI Project Cycle
The Modelling phase is a critical component of the AI Project Cycle, where the actual magic of AI happens. During this phase, we create models that can learn from the data collected and make informed predictions or decisions based on that data. The modelling process primarily consists of several key steps:
- Splitting Data: The dataset is divided into training and testing sets. The training set is used to build the model, while the testing set helps evaluate its performance.
- Choosing the Right Algorithm: Based on the nature of the problem and the type of data available, different algorithms (like regression for continuous outputs or classification for categorical outputs) are selected. Two major types of learning are covered:
- Supervised Learning: In this approach, models are trained with labeled data, such as in classification tasks where the model learns to categorize inputs.
- Unsupervised Learning: This method uses unlabelled data to find patterns, such as clustering similar items together.
- Training the Model: The selected algorithm is applied to the training data to create a model. During this process, the model learns the underlying structure or patterns in the data.
- Testing and Validating the Model: Once trained, the model is tested using the separate testing data to measure its accuracy, performance, and reliability in prediction.
For example, if we want to build a model to predict daily food waste based on student attendance and menu offerings, we would:
- Train the model using historical data on food waste, attendance rates, and types of dishes served.
- Validate the model's predictions by comparing them against actual outcomes to ensure its effectiveness.
The Modelling phase is essential for translating raw data into actionable insights, which is the foundation of any successful AI initiative.
Audio Book
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What is Modelling?
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This is the heart of AI. It involves creating a model that can learn from data and make predictions or decisions.
Detailed Explanation
Modelling is a crucial phase in the AI project cycle where we build a model based on the data we have gathered and explored. This model uses algorithms to understand patterns in the data and can make predictions or decisions based on that understanding. Think of it as creating a virtual copy of something real that can learn and improve over time based on new data.
Examples & Analogies
Imagine training a dog to fetch a ball. At first, the dog does not know what to do, but through practice (training), it learns that when you throw a ball, it should run after it and bring it back. Similarly, AI models learn from data examples, improving their performance over time.
Types of AI Models
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Types of AI models:
• Supervised Learning (with labelled data)
o Classification (e.g., spam or not)
o Regression (e.g., predicting house prices)
• Unsupervised Learning (without labels)
o Clustering (e.g., customer segmentation)
Detailed Explanation
There are primarily two types of AI models: supervised and unsupervised learning. Supervised learning involves training the model using labeled data, meaning we have input data along with the correct output. This allows the model to learn the relationship between the inputs and outputs. For instance, in classification problems, the model learns to distinguish between categories, while in regression problems, it predicts a continuous value. On the other hand, unsupervised learning does not use labeled outputs. The model identifies patterns and groupings in the data on its own, such as segmenting customers into different categories based on purchasing behavior.
Examples & Analogies
Consider supervised learning like a teacher guiding students with clear answers (labels) for practice exams. The students learn to recognize patterns and perform better on real exams. Unsupervised learning, however, is like a group of students working together without any teacher — they need to identify patterns and categorize things on their own, such as grouping similar books in a library.
Steps in Modelling
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Steps in Modelling:
1. Splitting data into training and testing sets
2. Choosing the right algorithm
3. Training the model
4. Testing and validating the model
Detailed Explanation
The modelling process involves several steps. First, we split the data into training and testing sets to ensure that the model can generalize well on unseen data. The training set is used to teach the model while the testing set evaluates its performance. Next, we choose the appropriate algorithm that fits our data and the type of problem we are solving. After that, we train the model using the training data to find patterns. Finally, we test and validate the model using the testing data to check if it performs accurately in making predictions.
Examples & Analogies
Think of it like preparing for a sports competition. First, you set aside some time to train (training set), then you practice specific skills (choosing an algorithm). After extensive training, you participate in a simulated competition (testing set) to see how well you perform. Your performance in the simulated competition lets you know if you are ready for the actual match.
Example of Modelling
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Example: Build a model that predicts how much food will be wasted each day based on attendance and menu.
Detailed Explanation
In the example given, we are tasked with predicting food waste in a school setting. The model would take into account various factors like student attendance, menu items, and perhaps even external factors such as the weather. By analyzing historical data and recognizing patterns, the model would be trained to make accurate predictions about how much food will likely be wasted each day, which can help in making decisions regarding food preparation and waste reduction strategies.
Examples & Analogies
Imagine being a chef in a busy restaurant. If you keep track of how much food is thrown away each day based on how many customers show up and what they order, you could develop a 'recipe' for success, minimizing waste by adjusting your orders and preparations based on predicted demand. The AI model does precisely this, learning from past data to forecast future needs.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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Modelling: Creating predictive models from data.
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Supervised Learning: Training models with labeled data.
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Unsupervised Learning: Training models without labeled data.
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Training Set: Data used to teach the model.
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Testing Set: Data used to test model accuracy.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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An example of a supervised learning model could be one that predicts house prices based on variables like size and location.
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An example of an unsupervised learning model could cluster customers by purchasing habits to identify distinct consumer segments.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
🎵 Rhymes Time
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Modelling's like a dream that shows, / Predicting futures from data flows.
📖 Fascinating Stories
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Imagine you're a chef with recipes (data) and you train (model) a new cook. Over time, the cook starts to make perfect dishes based on past experiences mix (training), and checks them against what diners like (testing).
🧠 Other Memory Gems
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Remember 'STTT': Split, Train, Test. These are your key steps in modelling.
🎯 Super Acronyms
M.A.P.
- Models Are Predictions. Always remember that when modelling
- we aim for predictions!
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: Modelling
Definition:
The process of creating a mathematical representation of data to make predictions or decisions.
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Term: Supervised Learning
Definition:
A type of machine learning where a model is trained on labeled data.
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Term: Unsupervised Learning
Definition:
A type of machine learning that finds patterns in unlabelled data.
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Term: Training Set
Definition:
A dataset used to train a model.
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Term: Testing Set
Definition:
A separate dataset used to evaluate the performance of a trained model.
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Term: Algorithm
Definition:
A set of rules or calculations followed by a model to process data.