5 Questions
What is the primary goal of the data preparation step in machine learning?
To clean and preprocess the data
What type of machine learning involves an agent learning from its environment and receiving rewards or penalties for its actions?
Reinforcement learning
What is the purpose of testing for bias in machine learning models?
To ensure the model is fair and unbiased
What is the primary difference between rules-based programming and data-driven programming?
The way the program makes decisions
What is the primary purpose of the 'Evaluating the Model' step in the AI lifecycle?
To determine the model's performance
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.
This quiz covers the basics of artificial intelligence, including types of AI and machine learning, the AI lifecycle, and data preparation and testing in machine learning.
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