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Questions and Answers
What is the primary goal of the data preparation stage in the AI lifecycle?
What type of machine learning involves training a model on labeled data to learn the relationship between the input and output?
What is the primary goal of testing a machine learning model?
What is the term for the phenomenon where a model reflects the biases present in the training data?
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What type of AI is designed to perform a specific task, such as playing chess or recognizing faces?
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What is the main difference between rules-based programming and data-driven programming?
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What is the term for machine learning models that learn from data without human supervision?
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What is the purpose of evaluating a machine learning model?
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What type of AI is designed to perform any intellectual task that a human can?
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What is the purpose of cleaning data in machine learning?
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Study Notes
AI Experience
- Artificial Intelligence (AI) involves creating intelligent systems that can perform tasks that typically require human intelligence.
Types of AI
- Machine Learning (ML): a subset of AI that involves training models on data to make predictions or decisions.
- Narrow AI (Weak AI): designed to perform a specific task, such as facial recognition or language translation.
- General AI (Strong AI): designed to perform any intellectual task that a human can.
Types of Machine Learning
- Supervised Learning: the model is trained on labeled data to make predictions on new data.
- Unsupervised Learning: the model is trained on unlabeled data to identify patterns or relationships.
- Reinforcement Learning: the model learns through trial and error by receiving rewards or punishments.
- Semi-supervised Learning: combines supervised and unsupervised learning approaches.
AI Lifecycle
- Defining the Problem: identify a problem or opportunity and define a goal for the AI model.
- Preparing Data: collect, clean, and preprocess data for the model.
- Training: train the model on the prepared data.
- Testing: evaluate the model's performance on a test dataset.
- Evaluating the Model: assess the model's accuracy, confidence, and bias.
Machine Learning: Data Preparation (Cleaning)
- Duplicates: identify and remove duplicate data points to prevent biases.
- Missing Data: handle missing data points, such as through imputation or interpolation.
- Invalid Data: detect and remove invalid or outlier data points.
Machine Learning: Testing
- Testing for Bias: evaluate the model's fairness and identify biases.
- Measuring Accuracy and Confidence: assess the model's performance using metrics such as accuracy, precision, and recall.
Decision Trees
- How Decision Trees are Made: a supervised learning approach that involves splitting data into subsets based on features.
- Solving Problems with ML Models: decision trees can be used for classification and regression tasks.
Bias in AI
- Bias in, Bias out: biased data can lead to biased models, which can perpetuate and amplify existing social inequalities.
AI Experience
- Artificial Intelligence (AI) involves creating intelligent systems that can perform tasks that typically require human intelligence.
Types of AI
- Machine Learning (ML): a subset of AI that involves training models on data to make predictions or decisions.
- Narrow AI (Weak AI): designed to perform a specific task, such as facial recognition or language translation.
- General AI (Strong AI): designed to perform any intellectual task that a human can.
Types of Machine Learning
- Supervised Learning: the model is trained on labeled data to make predictions on new data.
- Unsupervised Learning: the model is trained on unlabeled data to identify patterns or relationships.
- Reinforcement Learning: the model learns through trial and error by receiving rewards or punishments.
- Semi-supervised Learning: combines supervised and unsupervised learning approaches.
AI Lifecycle
- Defining the Problem: identify a problem or opportunity and define a goal for the AI model.
- Preparing Data: collect, clean, and preprocess data for the model.
- Training: train the model on the prepared data.
- Testing: evaluate the model's performance on a test dataset.
- Evaluating the Model: assess the model's accuracy, confidence, and bias.
Machine Learning: Data Preparation (Cleaning)
- Duplicates: identify and remove duplicate data points to prevent biases.
- Missing Data: handle missing data points, such as through imputation or interpolation.
- Invalid Data: detect and remove invalid or outlier data points.
Machine Learning: Testing
- Testing for Bias: evaluate the model's fairness and identify biases.
- Measuring Accuracy and Confidence: assess the model's performance using metrics such as accuracy, precision, and recall.
Decision Trees
- How Decision Trees are Made: a supervised learning approach that involves splitting data into subsets based on features.
- Solving Problems with ML Models: decision trees can be used for classification and regression tasks.
Bias in AI
- Bias in, Bias out: biased data can lead to biased models, which can perpetuate and amplify existing social inequalities.
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Description
Learn about the basics of Artificial Intelligence, including types of AI and its applications. Explore machine learning, narrow AI, and general AI.
