As an AI/ML practitioner, having a solid grasp of fundamental algorithms is crucial. Here’s your go-to reference guide for the most important ML algorithms, organized by learning approach:
๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
Classification:
โข Naive Bayes – Perfect for text classification
โข Logistic Regression – The go-to for binary problems
โข KNN – Simple yet powerful pattern recognition
โข Random Forest – Ensemble learning at its finest
โข SVM – Excellent for complex decision boundaries
โข Decision Trees – When interpretability matters
Regression:
โข Linear Regression – The foundation of predictive modeling
โข Multivariate Regression – For complex variable relationships
โข Lasso Regression – When feature selection counts
๐จ๐ป๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
Clustering:
โข K-Means – The clustering workhorse
โข DBSCAN – Density-based clustering champion
โข PCA – Dimensionality reduction master
โข ICA – When independence matters
Pattern Mining:
โข Association Rules – Market basket analysis
โข Frequent Pattern Growth – Efficient pattern discovery
โข Anomaly Detection – Finding the needles in the haystack
๐ฆ๐ฒ๐บ๐ถ-๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โข Self-Training – Learning from limited labeled data
โข Co-Training – When two views are better than one
๐ฅ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โข Model-Free Methods (Q-Learning) – Learning through experience
โข Model-Based Approaches – Planning ahead
โข Policy Optimization – Direct strategy learning
Pro Tips:
1. Master the fundamentals before diving into deep learning
2. Understand when to use each algorithm
3. Know their strengths and limitations
4. Practice implementing from scratch
5. Keep up with modern implementations
Key Learning Resources:
โข Scikit-learn documentation
โข Research papers
โข Hands-on projects
โข Real-world applications
Remember: The best algorithm depends on your:
– Data type
– Problem complexity
– Performance requirements
– Computational resources
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