Machine learning is a subfield of artificial intelligence that involves training algorithms to make predictions or decisions based on data.
- Supervised Learning: Training algorithms to make predictions based on labeled data.
- Unsupervised Learning: Training algorithms to identify patterns or structure in unlabeled data.
- Reinforcement Learning: Training algorithms to make decisions based on rewards or penalties.
| Algorithm | Description |
|---|---|
| Linear Regression | A linear regression algorithm is used to predict a continuous output variable based on one or more input features. |
| Decision Trees | A decision tree algorithm is used to classify data into different categories based on a set of rules. |
| Random Forest | A random forest algorithm is an ensemble method that combines multiple decision trees to improve accuracy. |
| Support Vector Machines (SVMs) | An SVM algorithm is used to classify data into different categories by finding the best hyperplane that separates the classes. |
Machine learning has a wide range of applications across various industries, including:
- Image Recognition: Training algorithms to recognize objects in images.
- Natural Language Processing (NLP): Training algorithms to understand and generate human language.
- Predictive Maintenance: Training algorithms to predict when equipment or machinery will fail.
- Recommendation Systems: Training algorithms to suggest products or services based on user behavior.
This repository consists of fundamentals of machine learning that one must know.
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