I thought I knew most of the ML algorithms until I saw this list on Wikipedia. I hope you will also share the same anxiety after skimming it ðŸ˜›

How about printing the whole list and tick them off by trying?

Machine Learning Methods

- Instance-based algorithm
- K-nearest neighbors algorithmÂ (KNN)
- Learning vector quantizationÂ (LVQ)
- Self-organizing mapÂ (SOM)

- Regression analysis
- Regularization algorithm
- Classifiers

- Canonical correlation analysisÂ (CCA)
- Factor analysis
- Feature extraction
- Feature selection
- Independent component analysisÂ (ICA)
- Linear discriminant analysisÂ (LDA)
- Multidimensional scalingÂ (MDS)
- Non-negative matrix factorizationÂ (NMF)
- Partial least squares regressionÂ (PLSR)
- Principal component analysisÂ (PCA)
- Principal component regressionÂ (PCR)
- Projection pursuit
- Sammon mapping
- t-distributed stochastic neighbor embeddingÂ (t-SNE)

### Ensemble learning

- AdaBoost
- Boosting
- Bootstrap aggregatingÂ (Bagging)
- Ensemble averagingÂ â€“ process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models “average out.”
- Gradient boosted decision treeÂ (GBRT)
- Gradient boostingÂ machine (GBM)
- Random Forest
- Stacked GeneralizationÂ (blending)

### Meta learning

### Reinforcement learning

- Q-learning
- Stateâ€“actionâ€“rewardâ€“stateâ€“actionÂ (SARSA)
- Temporal difference learningÂ (TD)
- Learning Automata

### Supervised learning

- AODE
- Artificial neural network
- Association rule learningÂ algorithms
- Case-based reasoning
- Gaussian process regression
- Gene expression programming
- Group method of data handlingÂ (GMDH)
- Inductive logic programming
- Instance-based learning
- Lazy learning
- Learning Automata
- Learning Vector Quantization
- Logistic Model Tree
- Minimum message lengthÂ (decision trees, decision graphs, etc.)
- Probably approximately correct learningÂ (PAC) learning
- Ripple down rules, a knowledge acquisition methodology
- Support vector machines
- Random Forests
- Ensembles of classifiers
- Bootstrap aggregatingÂ (bagging)
- Boosting (meta-algorithm)

- Ordinal classification
- Information fuzzy networksÂ (IFN)
- Conditional Random Field
- ANOVA
- Quadratic classifiers
- k-nearest neighbor
- Bayesian networks
- Hidden Markov models

#### Bayesian

- Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Averaged One-Dependence EstimatorsÂ (AODE)
- Bayesian Belief NetworkÂ (BBN)
- Bayesian NetworkÂ (BN)

#### Decision tree algorithms

- Decision tree
- Classification and regression treeÂ (CART)
- Iterative Dichotomiser 3Â (ID3)
- C4.5 algorithm
- C5.0 algorithm
- Chi-squared Automatic Interaction DetectionÂ (CHAID)
- Decision stump
- ID3 algorithm
- Random forest
- SLIQ

#### Linear classifier

- Fisher’s linear discriminant
- Linear regression
- Logistic regression
- Multinomial logistic regression
- Naive Bayes classifier
- Perceptron
- Support vector machine

### Unsupervised learning

- Expectation-maximization algorithm
- Vector Quantization
- Generative topographic map
- Information bottleneck method

#### Artificial neural networks

#### Association rule learning

#### Hierarchical clustering

#### Cluster analysis

- BIRCH
- DBSCAN
- Expectation-maximization (EM)
- Fuzzy clustering
- Hierarchical Clustering
- K-means algorithm
- K-means clustering
- K-medians
- Mean-shift
- OPTICS algorithm

#### Anomaly detection

### Semi-supervised learning

- Active learningÂ â€“ special case of semi-supervised learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.
^{[5]}Â^{[6]} - Generative models
- Low-density separation
- Graph-based methods
- Co-training
- Transduction

### Deep learning

- Deep belief networks
- Boltzmann machines
- Convolutional neural networks
- Recurrent neural networks
- Hierarchical temporal memory
- Deep Boltzmann MachineÂ (DBM)
- Stacked Auto-Encoders

### Other machine learning methods and problems

- Anomaly detection
- Association rules
- Bias-variance dilemma
- Classification
- Clustering
- Data Pre-processing
- Empirical risk minimization
- Feature engineering
- Feature learning
- Learning to rank
- Occam learning
- Online machine learning
- PAC learning
- Regression
- Reinforcement Learning
- Semi-supervised learning
- Statistical learning
- Structured prediction
- Unsupervised learning
- VC theory

### Persons influential in machine learning

- Alberto Broggi
- Andrei Knyazev
- Andrew McCallum
- Andrew Ng
- Armin B. Cremers
- Ayanna Howard
- Barney Pell
- Ben Goertzel
- Ben Taskar
- Bernhard SchÃ¶lkopf
- Brian D. Ripley
- Christopher G. Atkeson
- Corinna Cortes
- Demis Hassabis
- Douglas Lenat
- Eric Xing
- Ernst Dickmanns
- Geoffrey HintonÂ â€“ co-inventor of the backpropagation and contrastive divergence training algorithms
- Hans-Peter Kriegel
- Hartmut Neven
- Heikki Mannila
- Jacek M. Zurada
- Jaime Carbonell
- Jerome H. Friedman
- John D. Lafferty
- John PlattÂ â€“ invented SMO and Platt scaling
- Julie Beth Lovins
- JÃ¼rgen Schmidhuber
- Karl Steinbuch
- Katia Sycara
- Leo BreimanÂ â€“ invented bagging and random forests
- Lise Getoor
- Luca Maria Gambardella
- LÃ©on Bottou
- Marcus Hutter
- Mehryar Mohri
- Michael Collins
- Michael I. Jordan
- Michael L. Littman
- Nando de Freitas
- Ofer Dekel
- Oren Etzioni
- Pedro Domingos
- Peter Flach
- Pierre Baldi
- Pushmeet Kohli
- Ray Kurzweil
- Rayid Ghani
- Ross Quinlan
- Salvatore J. Stolfo
- Sebastian Thrun
- Selmer Bringsjord
- Sepp Hochreiter
- Shane Legg
- Stephen Muggleton
- Steve Omohundro
- Tom M. Mitchell
- Trevor Hastie
- Vasant Honavar
- Vladimir VapnikÂ â€“ co-inventor of the SVM and VC theory
- Yann LeCunÂ â€“ invented convolutional neural networks
- Yasuo Matsuyama
- Yoshua Bengio
- Zoubin Ghahramani

An AI evangelist and a multi-disciplinary engineer. Loves to read business and psychology during leisure time. Connect with him any time on LinkedIn for a quick chat on AI!