10 Machine Learning Algorithms Which we Should Know.

  1. Linear regression
  2. Logistic regression
  3. Decision tree
  4. SVM algorithm
  5. Naive Bayes algorithm
  6. KNN algorithm
  7. K-means
  8. Random forest algorithm
  9. Dimensionality reduction algorithms
  10. Gradient boosting algorithm and AdaBoosting algorithm

Machine Learning Algorithms

1. Linear Regression

  • Y — Dependent Variable
  • a — Slope
  • X — Independent variable
  • b — Intercept

2. Logistic Regression

  • include interaction terms
  • eliminate features
  • regularize techniques
  • use a non-linear model

3. Decision Tree

4. SVM (Support Vector Machine) Algorithm

5. Naive Bayes Algorithm

6. KNN (K- Nearest Neighbors) Algorithm

  • KNN is computationally expensive
  • Variables should be normalized, or else higher range variables can bias the algorithm
  • Data still needs to be pre-processed.

7. K-Means

  • The K-means algorithm picks k number of points, called centroids, for each cluster.
  • Each data point forms a cluster with the closest centroids, i.e., K clusters.
  • It now creates new centroids based on the existing cluster members.
  • With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.

8. Random Forest Algorithm

  • If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
  • If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during this process.
  • Each tree is grown to the most substantial extent possible. There is no pruning.

9. Dimensionality Reduction Algorithms

10. Gradient Boosting Algorithm and AdaBoosting Algorithm

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Sunilkumar Prajapati

Sunilkumar Prajapati

Data Science enthusiast | Machine Learning | Deep Learning | Data Analyst | Software Support Engineer