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ML Algorithms that we use in present days.
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Machine Learning Algorithms
1.
Machine Learning Machine Learning
Algorithms Elias Hasnat | 2017-8-31 1 / 14
2.
Bayesian 1. Naive Bayes 2.
Gaussian Naive Bayes 3. Multinomial Naive Bayes 4. Average One-Dependence Estimators (AODE) 5. Bayesian Belief Network (BBN) 6. Bayesian Network (BN) 7. Hidden Markov Model (HMM) 8. Markov Chain 9. Monte Carlo Simulation 10. Conditional Random Fields (CRF) 2 / 14
3.
Decision Tree 1. Classi
cation and Regression Tree (CART) 2. Interactive Dichotomiser 3 (ID3) 3. C4.5 and C5.0 4. Chi-Squared Automatic Interaction Detection (CHAID) 5. Decision Stump 6. M5 7. Random Forests 8. Conditional Decision Trees 3 / 14
4.
Clustering 1. Single Linkage
Clustering 2. K-Means 3. K-Medians 4. Expectation maximization (EM) 5. Hierarchical Clustering 6. DBSCAN 7. OPTICS Algorithm 8. Non Negative Matrix Factorization 9. Latent Dirichlet Allocation (LDA) 4 / 14
5.
Neural Networks 1. Self
Organizing Map (SOM) 2. Perceptron 3. Back Propagation 4. Hop eld Network 5. Radial Basis Function Network (RBFN) 6. Back Propagation 7. Auto Encoders 5 / 14
6.
Neural Networks (cntd.) 1.
Hop eld Networks 2. Boltzman Machines 3. Restricted Boltzman Machine 4. Spiking Neural Network 5. Learning Vector Quantization (LVQ) 6 / 14
7.
Ensemble 1. Boosting (Logit
Bost) 2. Bootstrapped Aggregation (Bagging) 3. AdaBoost 4. Stacked Generalization (Blending) 5. Gradient Boosting Machine (GBM) 6. Gradient Boosting Regression Trees (GBRT) 7. Random Forest 7 / 14
8.
Association Rule 1. Apriori 2.
Eclat 3. FP-Growth 8 / 14
9.
Deep Learning 1. Deep
Boltzman Machine (DBM) 2. Deep Belief Network (DBN) 3. Convolution Neural Network (CNN) 4. Generative Adverserial Network (GAN) 5. Stacked Auto Encoders 9 / 14
10.
Dimensionality Reduction 1. Principal
Component Analysis (PCA) 2. Principal Component Regression (PCR) 3. Partial Least Square Regression (PLSR) 4. Sammon Mapping 5. Multidimentional Scalling (MDS) 6. Projection Persuit 7. Discriminant Analysis (LDA, MDA, QDA, FDA) 10 / 14
11.
Instance Based Cake Based
or Memory Based 1. k-Nearest Neighbours (kNN) 2. Learning Vector Quantization (LVQ) 3. Self Organizing Map (SOM) 4. Locally Wighted Learning (LWL) 11 / 14
12.
Regularization 1. Ridge Regression 2.
Least Absolute Shrinkage and Selection Operation (LASSO) 3. Elastic Net 4. Least-Angle Regression (LARS) 12 / 14
13.
Regression 1. Ordinary Least
Squares Regression (OLSR) 2. Linear Regression 3. Logistic Regression 4. Stepwise Regression 5. Multivariate Adaptive Regression Splines (MARS) 6. Locally Estimated Scatterplot Smoothing (LOESS) 7. Jckknife Regression 13 / 14
14.
Thank You 14 /
14
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