Gordon College Flashcards
is a subset of artificial intelligence that involves training computer algorithms to learn patterns and make predictions or decisions based on data, without being explicitly programmed. Essentially, it’s a way for computers to learn from experience and improve their performance over time.
Machine learning
5 Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
- Neural Networks
A type of machine learning where the computer is trained on labeled data, meaning that the correct output is known in advance. Examples include image recognition, speech recognition, sentiment analysis.
Supervised Learning
A type of machine learning where the computer is given unlabeled data and must identify patterns on its own. Examples include clustering, anomaly detection, and dimensionality reduction.
Unsupervised Learning
A type of machine learning where the computer learns through trial and error, receiving feedback in the form of rewards or penalties. Examples include game playing, robotics, and autonomous vehicles.
Reinforcement learning
A type of machine learning that uses neural networks to simulate the structure and function of the human brain. Examples include image recognition, natural language processing, and speech recognition.
Deep learning
A type of machine learning that combines elements of both supervised and unsupervised learning, using a small amount of labeled data and a large amount of unlabeled data. Examples include image classification, object detection, and speed recognition.
Semi-supervised learning
A type of machine learning where knowledge gained from one task is applied to another task, typically in a different domain. Example include image recognition, natural language processing, and speech recognition.
Transfer learning
9 Key steps in machine learning process
- Data Collection
- Data Preprocessing
- Feature Selection
- Model Selection
- Training the Model
- Model Evaluation
- Model Tuning
- Deployment
- Monitoring and Maintenance
gathering relevant data various sources, such as databases, APIs, or user-generated content.
Data Collection
cleaning, formatting, and transforming the data to ensure it’s consistent and usable for machine learning.
Data Preprocessing
identifying the relevant features, or characteristics, of the data that will be used to train the machine learning model.
Feature Selection
choosing the appropriate machine learning model based on the problem being solved and the available data.
Model Selection
using the selected algorithm and the labeled data to train the model to make predictions or decisions based on the input data.
Training the Model
testing the model’s performance on a separate set of data to ensure its accurate and reliable.
Model Evaluation
