Machine learning is transforming how we solve complex problems. Here’s a quick overview of the fundamentals:
Supervised Learning
- Uses labeled data to train models
- Common algorithms: Linear Regression, Decision Trees, SVM
- Applications: Spam detection, Image classification
Unsupervised Learning
- Works with unlabeled data
- Common algorithms: K-means, PCA, DBSCAN
- Applications: Customer segmentation, Anomaly detection
Key Concepts
- Feature Engineering
- Model Evaluation
- Overfitting/Underfitting
- Cross-validation
The field is constantly evolving, but these fundamentals remain essential for building effective ML solutions.