Machine learning is a subset of artificial intelligence that focuses on the development of algorithms that can learn from and make predictions or decisions based on data. This guide will provide an overview of machine learning, its different types, and key concepts.
Types of Machine Learning
Supervised Learning: This involves training a model on labeled data to make predictions.
- Regression: Predicting a continuous value.
- Classification: Predicting a categorical value.
Unsupervised Learning: This involves training a model on unlabeled data to find patterns and structures.
- Clustering: Grouping data points based on similarities.
- Association: Finding relationships between different data items.
Reinforcement Learning: This involves training a model to make decisions based on feedback from the environment.
Key Concepts
- Feature Engineering: The process of using domain knowledge to extract features from raw data.
- Model Evaluation: The process of assessing the performance of a model.
- Overfitting/Underfitting: Overfitting occurs when a model is too complex and performs well on training data but poorly on unseen data. Underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.
Useful Resources
Machine Learning Process
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