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Machine Learning AISC Flashcards | StudBud
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Machine Learning AISC
What is front-end development
What is reinforcement learning
Machine Learning AISC
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1
What is reinforcement learning
Reinforcement learning doesn't involve feedback after actions are taken.
Where an agent learns to make decisions by interacting with an environment.
When a model is trained using a labeled dataset to predict outcomes.
Reinforcement learning is only used for sorting data into clusters.
2
What is front-end development
Writing server-side code to manage databases.
The funtional code of the site.
UI/UX design
Web development that focuses on building UI/UX of a website or application.
3
What is a good way to source datasets
Wikipedia
Chat GPT
Kaggle
Github
4
What is the typical split of data used to train/test a model
50/50
30/70
80/20
70/30
5
What is supervised learning
Supervised learning is another name for reinforcement learning.
Supervised learning is only used for clustering similar data points.
A type of machine learning where the model is trained on a labeled dataset.
When a human oversees the training process to approve each prediction.
6
How do you choose which model to use
Space complexity
Speed
Cool name
Performance
7
What is data cleaning
Data cleaning is only necessary for small datasets.
Process of removing inaccurate data from a dataset to improve its quality.
Data cleaning means deleting all missing values regardless of context.
Involves generating synthetic data to train machine learning models.
8
What is unsupervised learning
Unsupervised learning requires human feedback after every prediction.
Algorithm trained on unlabeled data to find hidden patterns and structures.
Used to reward agents based on their actions in an environment.
Uses labeled data to train models to predict specific outcomes.
9
What is machine learning
The main goal is to avoid using data and rely solely on logic.
Method of teaching computers to learn from data without being explicitly
Machine learning is just a fancy name for writing if-else statements.
Machine learning requires explicit programming of every rule and decision.
10
What are examples of bad data types
Skewed data
Mean values
Missing data
Outliers
