Machine learning algorithms

AI dennis

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Zifuatazo ni machine learning algorithms ambazo data scientists (individuals computer professional, corporate employees, and any one interest in AI) anazitumia kutengeneza machine learning models tofauti tofauti kwa ajili ya kutatua matatizo tofauti tofauti kupitia data

Regression Algorithms
  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)
2. Instance-based Algorithms
  • k-Nearest Neighbour (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
3. Regularization Algorithms
  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)
4. Decision Tree Algorithms
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees
5. Bayesian Algorithms
  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)
6. Clustering Algorithms
  • k-Means
  • k-Medians
  • Expectation Maximisation (EM)
  • Hierarchical Clustering
7. Association Rule Learning Algorithms
  • Apriori algorithm
  • Eclat algorithm
8. Artificial Neural Network Algorithms
  • Perceptron
  • Back-Propagation
  • Hopfield Network
  • Radial Basis Function Network (RBFN)
9. Deep Learning Algorithms
  • Deep Boltzmann Machine (DBM)
  • Deep Belief Networks (DBN)
  • Convolutional Neural Network (CNN)
  • Stacked Auto-Encoders
10. Dimensionality Reduction Algorithms
  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Linear Discriminant Analysis (LDA)
  • Mixture Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)
11. Ensemble Algorithms
  • Boosting
  • Bootstrapped Aggregation (Bagging)
  • AdaBoost
  • Stacked Generalization (blending)
  • Gradient Boosting Machines (GBM)
  • Gradient Boosted Regression Trees (GBRT)
  • Random Forest
12. Other Algorithms
  • Computational intelligence (evolutionary algorithms, etc.)
  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Recommender Systems
  • Reinforcement Learning
  • Graphical Models.
  • Tuna-train jinsi ya kutumia machine learning algorithms kutengeneza machine learning models
 
Regression Algorithms - Hii inatumika ku predict output kutokana na input husika mfano ukiangalia AlphaGo ya Google inauzezo wa kutabiri next move ya player kuingana na data zilizopo kwenye database yake watu huwa wana fana nisha hii na ML , mimi hapa najua ReA kwenye neural network regression based in Python


Instance-based Algorithms - Hii inatumika ku training data, kw lugha nyepesi ni yaleyale mabo ya neural network

Regularization Algorithms - hii ina tumiwa na watu wa data scientists, ewanatumia data ku minimize risk

Decision Tree Algorithms - inatumika kwenye decision analysis

Mkuu kama ubebobe hivi kwenye haya mambo naomba uni PM cv yako, kuna kazi huawei wanatafuta ma engineer
 
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