32 Machine Learning Algorithms Explained with Python

In this article, I will take you through an explanation and implementation of all Machine Learning algorithms with Python programming language.

Machine learning algorithms are a set of instructions for a computer on how to interact with, manipulate, and transform data. There are so many types of machine learning algorithms. Selecting the right algorithm is both science and art.

All Machine Learning Algorithms with Python

  1. Logistic Regression
  2. Linear Regression
  3. Apriori Algorithm
  4. Hypothesis Testing (Commonly used in Outlier Detection)
  5. DBSCAN Algorithm
  6. Tf-Idf Vectorization
  7. Cross-Validation
  8. 4 Graph Algorithms (Connected Components, Shortest Path, Pagerank, Centrality Measures)
  9. Ridge and Lasso Regression
  10. StandardScaler
  11. SARIMA
  12. XGBoost Algorithm
  13. Long Short Term Memory (LSTM)
  14. One Hot Encoding
  15. Bidirectional Encoder Representations from Transformers (BERT)
  16. Facebook Prophet
  17. AdaBoost Algorithm
  18. ARIMA
  19. Random Forest Algorithm
  20. H2O AutoML
  21. Polynomial Regression
  22. Gradient Descent Algorithm
  23. Grid Search Algorithm
  24. K-Means Algorithm
  25. Manifold Learning
  26. Principal Component Analysis
  27. Decision Trees
  28. Support Vector Machines
  29. Neural Networks

All the above algorithms are explained properly by using the python programming language. These were the common and most used machine learning algorithms. We will update this article with more algorithms soon. I hope you liked this article on all machine learning algorithms with Python programming language. Feel free to ask your valuable questions in the comments section below.

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