Order of learning Algorithms for datascience. Please help me



I tried to list out all algorithms related to Data Science… Can some one help the order of learning…….

Multiclass Classification Algorithm (Classification Algorithms)

  • Decision Forest
  • Decision Jungle
  • Logistic Regression
  • Neural Network
    One vs All Multiclass

Two-class classification algorithms (Classification Algorithms)

  • Averaged Perceptron
  • Bayes Point Machine
  • Boosted Decision Tree
  • Decision Forest
  • Decision Jungle
  • Locally deep support vector machine
  • Logistic Regression
  • Neural Network
  • Support vector machine

Regression Algorithm

  • Linear Regression
  • Bayesian Linear Regression
  • Boosted Decision Tree Regression
  • Decision Forest Regression
  • Fast Forest Quantile Regression
  • Neural Network Regression
  • Ordinal Regresssion
  • Poisson Regression

Clustering Algorithm

  • K-Means Clustering
  • Hierarchical Clustering
  • Agglomerative clustering
  • Divisive clustering
  • K medoids clustering

Ridge Regression
Lasso Regression
Random Forest
K-Nearest Neighbore(KNN)
Naïve Bayes
Principal Component Analysis
Dimensionality Reduction Algorithms
Gradient Boosting algorithms


The order is simple.

Start with Regression and statistics around regression and finally interpreting the results of regression.

Then move to trees and tree based algorithms.

Then try sone NN or SVM and try boosting algorithms.

FInally try the unsupervised learning.


Thank you very much vivek