Machine Learning Algorithms with Python

All 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. Assumptions of Machine Learning Algorithms
  2. Performance Evaluation Metrics
  3. Part of Speech Tagging
  4. Mini-Batch K-Means Clustering
  5. Multinomial Naive Bayes
  6. Bernoulli Naive Bayes
  7. Agglomerative Clustering
  8. VisualKeras for Visualizing a Neural Network
  9. Stochastic Gradient Descent
  10. Explained Variance
  11. F-Beta Score
  12. Classification Report
  13. Passive Aggressive Regression
  14. R2 Score
  15. Lazy Predict
  16. Missing Values Calculation
  17. t-SNE Algorithm
  18. AutoKeras Tutorial
  19. Bias and Variance
  20. Perceptron
  21. Class Balancing Techniques
  22. One vs All & One vs One
  23. Polynomial Regression
  24. BIRCH Clustering
  25. Independent Component Analysis
  26. Kernel PCA
  27. Sparse PCA
  28. Non Negative Matrix Factorization
  29. Neural Networks Tutorial
  30. PyCaret
  31. Scikit-learn Tutorial
  32. NLTK Tutorial
  33. TextBlob Tutorial
  34. Streamlit Tutorial
  35. DBSCAN Clustering
  36. Naive Bayes
  37. Passive Aggressive Classifier
  38. Gradient Boosting (Used in implementing the Instagram Algorithm)
  39. Logistic Regression
  40. Linear Regression
  41. K-Means Clustering
  42. Dimensionality Reduction
  43. Principal Component Analysis
  44. Automatic EDA
  45. Feature Scaling
  46. Apriori Algorithm
  47. K Nearest Neighbor
  48. CatBoost
  49. SMOTE
  50. Hypothesis Testing (Commonly used in Outlier Detection)
  51. Content-Based Filtering
  52. Collaborative Filtering
  53. Cosine Similarity
  54. Tf-Idf Vectorization
  55. Cross-Validation
  56. Confusion Matrix
  57. 4 Graph Algorithms (Connected Components, Shortest Path, Pagerank, Centrality Measures)
  58. Ridge and Lasso Regression
  59. StandardScaler
  60. SARIMA
  61. ARIMA
  62. Auc and ROC Curve
  63. XGBoost Algorithm
  64. Long Short Term Memory (LSTM)
  65. One Hot Encoding
  66. Bidirectional Encoder Representations from Transformers (BERT)
  67. Facebook Prophet
  68. NeuralProphet
  69. AdaBoost Algorithm
  70. Random Forest Algorithm
  71. H2O AutoML
  72. Polynomial Regression
  73. Gradient Descent Algorithm
  74. Grid Search Algorithm
  75. Manifold Learning
  76. Decision Trees
  77. Support Vector Machines
  78. Neural Networks
  79. FastAI
  80. LightGBM
  81. Pyforest Tutorial
  82. Machine Learning Models You Should Know

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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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1109

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