ML Prediction: Generated values from an algorithm trained on historical data for unknown variables in new data.
Initial stage: Gather, prepare data from diverse sources, format, split, and apply necessary transformations for modeling.
1. Data Collection
Choose and train a mathematical function, considering various types and adjusting parameters to match data and goals. Evaluate performance using metrics.
2. Model Selection and Traing
Conclude by testing, comparing, analyzing, and deploying the trained model for real-world applications, with ongoing monitoring and updates.
3. Evaluation and Deployment
Prediction, a frequent machine learning task, involves estimating unknown values from known data, aiding decision-making and resource optimization.
4. Prediction examples
Recommendation, a typical machine learning task, involves suggesting items or actions based on preferences, behavior, and interests.
5. Recommendation
Classification, another frequent machine learning task, entails labeling items based on their features, assisting in data organization and filtering.
6. Classification examples
Machine learning uses different algorithms tailored to tasks: Supervised learning with labeled data, unsupervised for patterns, and reinforcement via feedback.
7. Learning Algorithms
During prediction on new data, the trained model utilizes learned patterns and relationships to produce predictions or classifications.
8. Making Predictions
The journey of how Machine Learning models make predictions blends math, stats, and computer science, enabling AI-driven predictions' growth.