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Problem Scoping
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Welcome class! Today we're discussing the first stage of the AI Project Cycle: Problem Scoping. Can anyone tell me what this entails?
Isn't it about figuring out what problem we want to solve?
Exactly! It's about understanding the context of the problem, identifying stakeholders, setting clear goals, and assessing the potential impact. Remember this acronym: 'IDEA' – Identify, Define, Evaluate, Assess. Can someone give an example of a stakeholder in a healthcare AI project?
Patients would be a main stakeholder since they are directly affected.
Great point! As we narrow the problem, we ensure we stay relevant and focused. Let’s recap: Problem Scoping involves understanding the problem, identifying effects on stakeholders, and setting goals.
Data Acquisition
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Now we move on to Data Acquisition. Why is acquiring high-quality data crucial for AI projects?
Because the AI model needs good data to train effectively!
Exactly! We can collect both structured and unstructured data. Let's remember 'SUDS' – Sources, Unstructured, Data Quality, and Sources again. What are some sources from where we can acquire data?
Public datasets, surveys, and even APIs!
Perfect! Additionally, we must consider data quality factors—accuracy, consistency, and timeliness. Quality data prevents bias in our models.
Data Exploration
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Let’s dive into Data Exploration. What is its purpose?
To understand the data better and find patterns?
Correct! We use descriptive statistics and visualization tools. Does anyone know what tools we can utilize for visualization?
We can use Python libraries like Matplotlib or Tableau.
Excellent! Exploring our data helps us identify outliers and relationships, keeping our analysis focused.
Modeling
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Modeling is when we train our AI algorithm. Who can name the different types of machine learning?
Supervised, unsupervised, and reinforcement learning!
Exactly! Remember 'SUR' for these types. Can someone explain supervised learning?
That’s when we train the model using labeled data.
Spot on! When training the model, we evaluate performance with metrics like accuracy and F1 Score. Can anyone explain why cross-validation is important?
It helps us avoid overfitting!
Exactly! Crossing validation is crucial in assessing how our model will perform on unseen data.
Deployment
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Now, we’re in the Deployment stage. Why is this step important?
Because it’s when the model becomes useful for real users!
Exactly! Deployment can involve web apps and APIs. Remember the acronym 'SUM' for Scalability, User Experience, and Maintenance. Can anyone give an example of a feedback mechanism?
Collecting user feedback for continuous learning?
Perfect! Continuous feedback is essential for developing a sustainable AI solution.
Introduction & Overview
Read a summary of the section's main ideas. Choose from Basic, Medium, or Detailed.
Quick Overview
Standard
This section details the various stages of the AI Project Cycle, including problem scoping, data acquisition, exploration, modeling, evaluation, and deployment. Each stage is essential for ensuring the successful development and implementation of AI solutions.
Detailed
AI Project Cycle
The AI Project Cycle is a structured methodology essential for developing AI solutions. It mirrors traditional software development processes but emphasizes AI-specific challenges, allowing project teams to systematically approach real-world problems.
Stages of the AI Project Cycle
Problem Scoping
This stage involves defining the AI problem clearly, identifying stakeholders, establishing goals, and assessing potential impacts, ensuring focus throughout the project.
Data Acquisition
Data is pivotal for training AI models. This section discusses how to collect quality data—structured and unstructured—from various sources while considering ethical standards such as privacy and consent.
Data Exploration
Analyzing and visualizing data helps in understanding its structure and identifying patterns and anomalies via descriptive statistics and data cleaning techniques.
Modeling
Here, AI algorithms are trained based on the cleaned data. We explore different types of models (supervised, unsupervised, and reinforcement learning) and emphasize key concepts like overfitting and the bias-variance tradeoff.
Evaluation
The evaluation phase assesses model performance using metrics like accuracy, precision, and recall, and the confusion matrix helps to summarize results. This step validates the model's readiness for real-world application and its fairness.
Deployment
The final phase integrates the AI model into a usable environment, considering aspects like scalability and user experience while establishing feedback mechanisms for continuous learning.
This cycle facilitates a holistic approach to AI projects, ensuring well-defined goals, quality data, robust models, and practical solutions for real-world problems.
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Audio Book
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Introduction to the AI Project Cycle
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The AI Project Cycle is the structured process followed to develop AI-based solutions to real-world problems. Just like any software development process, an AI project requires a systematic approach to identify the problem, gather data, train models, evaluate outcomes, and deploy the solution effectively. This chapter provides a detailed understanding of each stage in the AI Project Cycle to help students design, build, and present AI projects in a well-organized manner.
Detailed Explanation
In this introduction, we learn that the AI Project Cycle is similar to software development. It involves several important steps, from identifying a problem in the real world to deploying an AI solution. We can think of each of these steps as building blocks that, when stacked together correctly, create a sturdy structure—here, a successful AI project.
Examples & Analogies
Imagine a chef preparing a new dish. First, any chef must identify what dish they want to create (the problem). They’ll then gather all necessary ingredients (data) and follow specific cooking steps (model training) to eventually serve the dish (deployment).
Problem Scoping
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Problem scoping is the process of understanding, defining, and narrowing down the problem to be solved using AI. It ensures that the project remains focused and relevant.
Detailed Explanation
Problem scoping is the first critical step in the AI project. It involves understanding the specific issue at hand and determining its scope. This ensures that the project is focused and that efforts do not stray into areas that may not be relevant. Clarity at this stage saves time and leads to more effective solutions.
Examples & Analogies
Think of it as planning a road trip: you need to decide your destination first (the problem) before you can decide which route to take and what stops you may want along the way.
Steps in Problem Scoping
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- Understanding the Problem Identify the domain (e.g., healthcare, education, environment) and key challenges.
- Identifying the Stakeholders Determine the people affected by the problem (e.g., patients, students, government).
- Defining Goals What exactly do we want to achieve? For example: Reduce pollution, detect diseases, improve productivity.
- Impact Assessment Predict the outcomes and side effects—both positive and negative—of solving the problem.
Detailed Explanation
Let's break down the steps involved in problem scoping:
1. Understanding the Problem: Dive deep into the specifics of the challenge within a chosen domain.
2. Identifying Stakeholders: Recognize who will be affected by the project—these are the people or groups that have a stake in the outcome.
3. Defining Goals: Clearly outline what you want the project to accomplish. This will direct your efforts as you move forward.
4. Impact Assessment: Evaluate potential consequences, both positive and negative, of addressing the problem. This helps prepare for unforeseen issues.
Examples & Analogies
Consider a community aiming to improve air quality. First, they identify pollution sources (understanding the problem), recognize affected residents (stakeholders), decide they want cleaner air (goals), and forecast the potential improvements in health (impact assessment).
Tools and Techniques in Problem Scoping
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• SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats)
• Problem Statements
• Need vs. Feasibility Matrix
Detailed Explanation
Various tools can assist in problem scoping:
- SWOT Analysis: This helps you comprehend the project’s strengths, weaknesses, opportunities, and threats.
- Problem Statements: Writing clear problem statements helps clarify the issues being addressed.
- Need vs. Feasibility Matrix: This assists in evaluating whether the needs are achievable with the resources available, ensuring that the project is practical.
Examples & Analogies
Using the community air quality example, a SWOT analysis might reveal strengths like community support, weaknesses such as limited funding, opportunities like grants, and threats like political opposition. Having a clear problem statement can direct efforts to ensure the project's relevance.
Data Acquisition
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Data Acquisition refers to the collection of relevant data that will be used to train the AI model.
Detailed Explanation
Data Acquisition is crucial as it forms the foundation for AI model training. It involves collecting various types of data that are relevant to the problem you’re trying to solve. The data quality is critical for ensuring the effectiveness of the AI solution. Without data, models cannot learn, and the entire project may falter.
Examples & Analogies
Think of data acquisition like gathering ingredients before a cooking session. Just as the quality of ingredients affects the dish, the quality of data directly affects how well your AI can perform.
Types of Data
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- Structured Data: Data in tabular format (e.g., Excel files, CSV files).
- Unstructured Data: Data in the form of text, images, audio, or video.
Detailed Explanation
Data comes in two main types:
1. Structured Data: This is organized data that fits into a defined model, easily searchable and analyzable (like rows and columns in a spreadsheet).
2. Unstructured Data: This includes data that does not follow a specific format, like images, text files, or videos, making it harder to collect and work with.
Recognizing the type of data is essential for deciding how to process it.
Examples & Analogies
Consider structured data to be like a library catalog, where everything is neatly organized. In contrast, unstructured data is like a stack of books with no clear order, requiring more effort to sift through.
Sources and Quality of Data
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Sources of Data:
• Public datasets (Kaggle, UCI Repository)
• APIs
• Surveys and Questionnaires
• Web Scraping
• Government Portals
Data Quality Considerations:
• Accuracy
• Completeness
• Consistency
• Timeliness
Ethical Considerations:
• Privacy of individuals
• Consent for data collection
• Bias in data
Detailed Explanation
In this section, you learn about where to find data and what to look for in terms of quality. Sources include public datasets and APIs, and considering quality aspects like accuracy, completeness, and consistency is necessary. Additionally, ethical considerations such as individual privacy and data bias are vital in responsible data collection.
Examples & Analogies
Gathering data from reliable sources is similar to getting ingredients from trusted suppliers. If you don't check the quality of your ingredients (data), your final dish (AI model) may not turn out well. Furthermore, just like respecting food safety and dietary restrictions, ethical considerations regarding data are essential.
Data Exploration
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Data Exploration involves analyzing and visualizing the data to understand its structure, patterns, and anomalies.
Detailed Explanation
Data exploration is the step where analysts dive into the dataset to reveal its insights. By applying descriptive statistics and visualization tools, you get a clearer picture of patterns, anomalies, and relationships that are crucial for the model training phase. It allows data scientists to understand what the data is telling them, setting the stage for effective modeling.
Examples & Analogies
Imagine looking at the ingredients you’ve collected. You might taste an ingredient (analyzing) or arrange them visually on a table (visualization) to decide which combinations work best to create your dish.
Techniques in Data Exploration
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Techniques Used:
1. Descriptive Statistics – Mean, Median, Mode, Range
2. Data Cleaning – Handling missing or duplicate data
3. Visualization Tools – Charts, histograms, scatter plots
Objectives:
• Identify patterns and trends
• Detect outliers
• Check data quality and relevance
• Understand feature relationships
Tools:
• Python libraries like Pandas, Matplotlib, Seaborn
• MS Excel
• Tableau
Detailed Explanation
Exploratory techniques are crucial for data understanding. Descriptive statistics provide basic insights through numerical summaries. Data cleaning addresses issues like missing values, and visualization tools help to illustrate data patterns clearly. The objective is to glean as much understanding from data as possible, using tools that facilitate analysis.
Examples & Analogies
Think about piecing together a puzzle. Descriptive statistics help you identify shapes and colors (patterns), data cleaning fixes missing or damaged pieces (data issues), and visualization gives you an overview of what the complete picture may look like.
Modeling
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Modeling is the process of training an AI algorithm using the acquired and cleaned data to predict or classify future data points.
Detailed Explanation
In the modeling phase, data scientists train AI algorithms using previously gathered data. The goal is to develop a model capable of making predictions or classifications based on new, unseen data. This phase is where you see the practical application of all previous steps as you start to create something that can learn.
Examples & Analogies
Just like a student learns from their teacher (trained) using books and resources (data), the AI model learns from the data to perform future tasks. The better the education, the more proficient the student becomes.
Types of AI Models
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Types of AI Models:
1. Supervised Learning – Labeled data used for prediction/classification
2. Unsupervised Learning – Patterns discovered from unlabeled data
3. Reinforcement Learning – Learning through rewards and penalties
Detailed Explanation
AI models can be categorized primarily into three types:
1. Supervised Learning: This type of model learns from labeled data (like teaching a child the difference between a cat and a dog).
2. Unsupervised Learning: This model looks for hidden patterns in data without any labels (like clustering similar items together).
3. Reinforcement Learning: Involves learning by interactions and getting rewards or penalties (like training a pet to perform tricks based on positive reinforcement).
Examples & Analogies
Imagine a student preparing for a test. With some practice exams (supervised), they can predict answers. Another student may explore and organize their notes without guidance (unsupervised). A third student may receive rewards for completing studies (reinforcement), learning to associate studying with success.
Steps in Modeling
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- Splitting Data – Training and Testing sets
- Choosing the Algorithm – Decision Trees, SVM, KNN, etc.
- Training the Model
- Evaluating the Model – Accuracy, Precision, Recall, F1 Score
Detailed Explanation
Modeling involves several critical steps:
1. Splitting Data: This is about dividing your data into training and testing sets to measure the model's performance accurately.
2. Choosing the Algorithm: Picking the right algorithm is essential as different ones have different strengths.
3. Training the Model: This is where the actual learning happens through the training dataset.
4. Evaluating the Model: After training, we evaluate performance using metrics like accuracy, precision, and recall, which help us understand how the model performs in real-world scenarios.
Examples & Analogies
Think of this phase as preparing a recipe. First, you gather your ingredients (data), then choose the right cooking method (algorithm), cook it (training), and finally taste it to see how it turned out (evaluation).
Important Concepts in Modeling
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• Overfitting and Underfitting
• Cross-validation
• Bias-Variance Tradeoff
Detailed Explanation
In modeling, there are several important concepts:
- Overfitting: This occurs when a model learns too much detail from the training data, losing the ability to generalize.
- Underfitting: This happens when a model is too simple to capture the underlying trend of the data.
- Cross-validation: A technique used to assess how the results of a statistical analysis will generalize to an independent dataset.
- Bias-Variance Tradeoff: The balance between errors introduced by bias (error due to overly simplistic assumptions) and variance (error due to excessive sensitivity to fluctuations in the training set).
Examples & Analogies
Think of a student preparing for exams. If they remember every detail of their notes (overfitting), they might struggle with questions on different topics. Conversely, if they skim through without understanding the basics (underfitting), they won't perform well. Cross-validation acts as a mock test to help gauge learning.
Evaluation
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Evaluation involves assessing the performance of the AI model on unseen data.
Detailed Explanation
Evaluation is a critical phase where the performance of the trained AI model is assessed using new, unseen data. This helps to understand how well the model performs in real-world applications and whether it can be deployed successfully. Effective evaluation ensures that the model is both accurate and reliable.
Examples & Analogies
It’s like taking a driving test after completing lessons. You apply everything you've learned in real-world conditions to show you can drive safely and effectively.
Key Evaluation Metrics
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Key Metrics:
1. Accuracy – Correct predictions over total predictions
2. Precision – Correct positive predictions out of all predicted positives
3. Recall – Correct positive predictions out of all actual positives
4. F1 Score – Harmonic mean of precision and recall
Confusion Matrix:
A table that summarizes model prediction results, showing:
• True Positives (TP)
• True Negatives (TN)
• False Positives (FP)
• False Negatives (FN)
Detailed Explanation
To evaluate an AI model, we often use specific metrics:
1. Accuracy measures the overall correctness of predictions.
2. Precision indicates how many predicted positives were correct (important in scenarios where false positives are costly).
3. Recall measures how many actual positives were correctly identified (important in situations where missing a positive is critical).
4. F1 Score provides a balance between precision and recall. The Confusion Matrix further details performance, displaying true positives, true negatives, false positives, and false negatives.
Examples & Analogies
Think of evaluation metrics as the grading system for a student’s test performance. Accuracy reflects overall performance, while precision and recall dive into specific areas of performance, such as how many right answers were provided, especially in critical topics.
Why Evaluation Matters
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• Helps in improving the model
• Checks for bias or unfairness
• Guides real-world deployment readiness
Detailed Explanation
Evaluation is crucial for several reasons. It allows teams to improve models by identifying weaknesses, ensures the model operates fairly without bias, and provides insight into whether the model is ready for deployment in a real-world setting. This step must not be overlooked, as it directly influences the success of the AI solution.
Examples & Analogies
It's similar to getting feedback after a job performance review. Evaluating a model tells you where you excel and where you need to improve, ensuring you're prepared to tackle future challenges effectively.
Deployment
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Deployment is the stage where the final AI model is integrated into a real-world environment for use by stakeholders.
Detailed Explanation
The deployment phase is where the final AI model comes to life in a practical setting. It involves the integration of the model into applications where users can interact with it. Successful deployment also requires attention to the environment in which it will operate and making sure it meets user needs.
Examples & Analogies
Imagine launching a new app on the App Store. This is the deployment stage where users can download and use it in their daily lives, reflecting all the hard work put into conceptualizing and building it.
Methods of Deployment
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Deployment Methods:
• Web Applications
• Mobile Apps
• Embedded Systems
• Cloud-based APIs
Considerations:
• Scalability
• User Interface and Experience (UI/UX)
• Maintenance and Updates
• Data Security and Privacy
Detailed Explanation
Various methods exist for deployment. Depending on the intended use, models can be integrated as web applications, mobile apps, embedded systems, or cloud-based APIs. Considerations during deployment are vast, including ensuring the model can scale, creating a user-friendly interface, planning for maintenance, and addressing data security/privacy.
Examples & Analogies
Deploying an AI model is akin to launching a major movie. You want it to reach a broad audience (scalability), be appealing and engaging (UI/UX), get regular updates (maintenance), and ensure no spoilers (data privacy).
Feedback Mechanism in Deployment
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Feedback Mechanism:
• Continuous learning from real-world data
• Gathering user feedback to improve the system
Detailed Explanation
Once deployed, it's crucial to have a feedback mechanism in place. This can involve using real-world data to continuously refine the model and actively seeking feedback from users to enhance the system. Continuous learning ensures adaptability to changing conditions.
Examples & Analogies
Think of a gardener who keeps evaluating plants. Over time, they adjust care based on how the plants are growing in response to environmental changes; similarly, feedback helps in adapting the AI model to better serve its purpose.
AI Project Cycle Summary
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Phase Key Focus
Problem Scoping Define and narrow down the problem
Data Acquisition Collect relevant, high-quality data
Data Exploration Analyze and understand data patterns
Modelling Train an AI model on data
Evaluation Assess model performance and fairness
Deployment Implement the model in real-world context
Detailed Explanation
This summary outlines the key focus areas for each phase of the AI Project Cycle. Understanding this cycle is crucial for systematically developing AI solutions and ensuring a successful outcome from problem discovery to deployment.
Examples & Analogies
Think of the AI Project Cycle as a roadmap for a journey. Each phase represents a distinct milestone, helping travelers know their direction and assess readiness at each point before reaching their destination.
Final Thoughts on the AI Project Cycle
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The AI Project Cycle is essential for systematically developing and deploying AI solutions. It begins with clearly scoping the problem and ends with deploying the model for real-world use. Each step — from data collection and analysis to model training and evaluation — plays a vital role in ensuring the success of the project. A well-executed AI project not only solves the problem at hand but also aligns with ethical standards, ensures user satisfaction, and has long-term impact and scalability.
Detailed Explanation
In conclusion, the AI Project Cycle is a comprehensive framework guiding the development and deployment of AI solutions. It emphasizes the importance of each step, from defining the problem to evaluating and deploying the model, ensuring that ethical considerations and user needs are met throughout the process.
Examples & Analogies
Consider this cycle as constructing a building. Each phase is essential—from planning (scoping) to laying the foundation (data collection), building the walls (modeling), and finally ensuring the building is safe and functional (deployment). Proper execution leads to a structure that serves its intended purpose well.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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Problem Scoping: The process of clearly identifying the problem to be solved.
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Data Acquisition: Collecting relevant data essential for AI training.
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Data Exploration: Analyzing and visualizing data to understand its patterns.
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Modeling: Training AI algorithms based on clean data.
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Evaluation: Assessing the performance of the AI model using metrics.
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Deployment: Integrating the AI model into a real-world environment for use.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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In healthcare, an AI model might be developed to detect diseases by first defining the problem, collecting medical data, then training the model based on that data.
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In climate change, a project might search for ways to reduce pollution, which starts with scoping the problem and understanding its societal impact.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
🎵 Rhymes Time
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In the AI cycle, don't be slow, / Problem scope, let clarity grow. / Data collected by various means, / Explore it well to know what it means.
📖 Fascinating Stories
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Imagine an AI team tasked with solving traffic congestion. They begin by understanding the problem (Problem Scoping) and speaking to affected commuters (Stakeholder Identification). Next, they gather data from traffic cameras (Data Acquisition), explore patterns of rush hour traffic (Data Exploration), develop a forecasting model (Modeling), evaluate its predictions (Evaluation), and finally, integrate the solution into traffic lights to improve flow (Deployment).
🧠 Other Memory Gems
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Remember 'P-D-E-M-E-D' for each phase: Problem Scoping, Data Acquisition, Exploration, Modeling, Evaluation, Deployment.
🎯 Super Acronyms
Use 'IDEA' for Problem Scoping
- Identify the problem
- Define it
- Evaluate impacts
- Assess solutions.
Flash Cards
Review key concepts with flashcards.
Glossary of Terms
Review the Definitions for terms.
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Term: Problem Scoping
Definition:
The process of understanding and defining the specific problem to be solved using AI.
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Term: Data Acquisition
Definition:
The methods and processes used to collect relevant data required for training AI models.
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Term: Data Exploration
Definition:
The phase where data is analyzed and visualized to uncover insights.
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Term: Modeling
Definition:
The process of training AI algorithms using cleaned and prepared data.
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Term: Evaluation
Definition:
The assessment of the AI model's performance against specific metrics.
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Term: Deployment
Definition:
The stage where the trained AI model is integrated and put into use in real-world settings.
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Term: Supervised Learning
Definition:
A type of machine learning that uses labeled data for training.
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Term: Unsupervised Learning
Definition:
A type of machine learning that identifies patterns in unlabeled data.
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Term: Reinforcement Learning
Definition:
A type of machine learning where an agent learns to make decisions through rewards and penalties.