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Machine Learning and Artificial Intelligence Use Cases
Use cases for Regression Algorithms
- Digital Customer experience
- Weather data analysis
- Home prices
- Sales revenue
- Length of hospital stay
- Engine performance
- Energy supply
- Life expectancy
- Market research
- Customer survey
- Astronomy
- Other Artificial Intelligence use cases
What is Regression?
Regression analysis is a way of mathematically sorting out which variables have an impact, which variables matter most, and how the variables interact with each other. Linear regression is used when you have predictors that have a linear relationship with your target dependent variable. Logistic regression is where the dependent variable is categorical.
https://hbr.org/2015/11/a-refresher-on-regression-analysis
CLASSIFICATION
Use cases for Classification Algorithms
- Customer behavior and churn prediction
- Medical diagnosis
- Document classification - Machine Learning
- Spam Filtering
- Image classification
- Web text classification
- Ad click-through rate prediction
- Product categorization
- Malware classification
- Image sentiment analysis
- Anomaly detection
- Fraud detection
- Sentiment analysis
- Other Artificial Intelligence use cases
What is Classification?
A classification model attempts to draw some conclusion from observed values. Machine learning classification models can predict or estimate the response variable or class given input data.
Classification algorithms include Decision trees, Random forest, Artificial Neural Networks, Naive Bayes, Support Vector Machines and Logistic Regression.
https://vitalflux.com/classification-problems-real-world-examples/
DEEP LEARNING
Use cases for Deep Learning Algorithms
- Virtual assistants
- Translation
- Autonomous driving
- Chatbots and service bots
- Image colorization
- Facial recognition
- Personalized shopping and entertainment
- Medical diagnosis
- Other Artificial Intelligence use cases
What is Deep Learning?
Deep learning is a subset of machine learning where artificial neural networks (algorithms inspired by the human brain) learn from large amounts of data. Deep learning is a more complex algorithm used for classifying images, identifying objects in images, and enhancing images, sensor data and signals. Deep learning models takes time to train such as deep neural networks and reinforcement learning.
https://bernardmarr.com/what-is-deep-learning-ai-a-simple-guide-with-8-practical-examples/
NATURAL LANGUAGE PROCESSING
Use cases for NLP Algorithms
- GPS systems
- Digital assistants
- Speech recognition
- Chatbots and customer service bots
- Speech tagging
- Word sense disambiguation
- Name entity recognition
- Co-reference resolution
- Sentiment analysis and Machine Learning
- Natural language generation
- Other Artificial Intelligence use cases
What is NLP?
Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text & voice data sets.
https://www.ibm.com/cloud/learn/natural-language-processing
TIME SERIES
Use cases for Time Series Algorithms
- Weather data
- Temperature readings
- Heart rate monitoring
- Sales data
- Stock prices
- Industry forecasts
- Interest rates
- Other Artificial Intelligence use cases
What is Time Series?
Time series data is data that is recorded over consistent intervals of time. Time series forecasting is predictive analytics that can help businesses understand the underlying causes of trends or systemic patterns over time. Machine Learning models can analyze time series data to extract meaningful patterns in time and make accurate future predictions.