# Every Single ML Algorithm Ever

Every machine learning article I read begins with 'Machine Learning has become a buzzword...'. It certainly has. But nowhere did I find a comprehensive list of all the machine learning algorithms that are out there. I shall not write about ML in this article, because you are going to skip it anyway. So here's a list of all the ML algorithms that you'll ever need to know.

Note: I'm only naming them. In my future articles, I'll explain each one in detail.

### Supervised Learning Algorithms

1. Linear regression
    
2. Logistic regression
    
3. Decision trees
    
4. Random forests
    
5. Support vector machines
    
6. k-Nearest neighbors
    
7. Naive Bayes
    
8. Gradient boosting machines
    
9. Neural networks
    
    1. Convolutional neural networks
        
    2. Feedforward neural networks
        
    3. Recurrent neural networks
        
    4. Long short-term memory
        
    5. Logic learning machine
        
    6. Extreme learning machine
        
10. Information fuzzy networks
    
11. Learning vector quantization
    
12. Quadratic classifier
    
13. Multinomial Naive Bayes
    

### Semi-Supervised Learning Algorithms

1. Generative Models
    
    1. Generative adversetial networks (GANs)
        
    2. Autoencoders
        
    3. Variational autoencoders
        
    4. Boltzmann machines
        
    5. Restricted Boltzmann machines
        
2. Label Propagation
    
3. Multi-view Learning
    
4. Co-training
    
5. Low-density separation
    
6. Graph-based models
    

### Unsupervised Learning Algorithms

1. Clustering
    
    1. K-Means
        
    2. K-Medians
        
    3. Mean-shift
        
    4. DBSCAN
        
    5. Conceptual clustering
        
    6. Hierarchical clustering
        
    7. Single-linkage clustering
        
    8. Fuzzy clustering
        
2. Dimensionality reduction
    
    1. Principal component analysis
        
    2. Principal component regression
        
    3. Independent component analysis
        
    4. Feature extraction
        
    5. Feature selection
        
    6. Linear discriminant analysis
        
    7. Factor analysis
        
    8. Partial least squares regression
        
3. Vector quantization
    
4. Expectation-maximization algorithm
    
5. Markov models
    
6. Bayesian Belief Networks
    

### Reinforcement Learning

1. Q-learning
    
2. State-action-reward-state-action (SARSA)
    
3. Actor-critic models
    
4. Policy optimization or policy-iteration methods
    
5. Model-based Value Expansion
    

### Others

1. Neural style transfer
    
2. Self-organizing maps
    
3. Dynamic time warping
    
4. Ensemble learning (other than random forest)
    
5. Instance-based learning
    

I hope you find this helpful. Feel free to reach out to me if you find any discrepancies in my list. I'll be happy to discuss it. Cheers!