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100+ Machine Learning Algorithms & Models Explained with Python

All Machine Learning Algorithms and Models Explained with Python programming language.

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

All Machine Learning Algorithms & Models with Python

  1. ARIMA for Time Series Forecasting
  2. Understand How Neural Network Works
  3. Assumptions of Machine Learning Algorithms
  4. Multiclass Classification Algorithms
  5. Binary Classification Algorithms
  6. Most Important Python Libraries for Data Science
  7. Best Approaches for Time Series Analysis
  8. Best Approaches for Sentiment Analysis
  9. Giving Inputs to a Machine Learning Model
  10. Adding Labels to a Dataset for Sentiment Analysis
  11. Process of Natural Language Processing
  12. Sentence and Word Tokenization
  13. Clustering Algorithms
  14. AlexNet Architecture
  15. Activation Functions
  16. LeNet-5 Architecture
  17. Visualizing a Machine Learning Algorithm
  18. Introduction and Approaches to build Recommendation Systems
  19. Mean-Shift Clustering
  20. Performance Evaluation Metrics
  21. Part of Speech Tagging
  22. Mini-Batch K-Means Clustering
  23. Multinomial Naive Bayes
  24. Bernoulli Naive Bayes
  25. Agglomerative Clustering
  26. VisualKeras for Visualizing a Neural Network
  27. Stochastic Gradient Descent
  28. Explained Variance
  29. F-Beta Score
  30. Classification Report
  31. Passive Aggressive Regression
  32. R2 Score
  33. Lazy Predict
  34. FLAML
  35. Missing Values Calculation
  36. t-SNE Algorithm
  37. AutoKeras Tutorial
  38. Bias and Variance
  39. Perceptron
  40. Class Balancing Techniques
  41. One vs All & One vs One
  42. Polynomial Regression
  43. BIRCH Clustering
  44. Independent Component Analysis
  45. Kernel PCA
  46. Sparse PCA
  47. Non Negative Matrix Factorization
  48. Neural Networks Tutorial
  49. PyCaret
  50. Scikit-learn Tutorial
  51. NLTK Tutorial
  52. TextBlob Tutorial
  53. Streamlit Tutorial
  54. DBSCAN Clustering
  55. Naive Bayes
  56. Passive Aggressive Classifier
  57. Gradient Boosting (Used in implementing the Instagram Algorithm)
  58. Logistic Regression
  59. Linear Regression
  60. K-Means Clustering
  61. Dimensionality Reduction
  62. Principal Component Analysis
  63. Automatic EDA
  64. Feature Scaling
  65. Apriori Algorithm
  66. K Nearest Neighbor
  67. CatBoost
  68. SMOTE
  69. Hypothesis Testing (Commonly used in Outlier Detection)
  70. Content-Based Filtering
  71. Collaborative Filtering
  72. Cosine Similarity
  73. Tf-Idf Vectorization
  74. Cross-Validation
  75. Confusion Matrix
  76. 4 Graph Algorithms (Connected Components, Shortest Path, Pagerank, Centrality Measures)
  77. Ridge and Lasso Regression
  78. StandardScaler
  79. SARIMA
  80. ARIMA
  81. Auc and ROC Curve
  82. XGBoost Algorithm
  83. Long Short Term Memory (LSTM)
  84. One Hot Encoding
  85. Bidirectional Encoder Representations from Transformers (BERT)
  86. Facebook Prophet
  87. NeuralProphet
  88. AdaBoost Algorithm
  89. Random Forest Algorithm
  90. H2O AutoML
  91. Polynomial Regression
  92. Gradient Descent Algorithm
  93. Grid Search Algorithm
  94. Manifold Learning
  95. Decision Trees
  96. Support Vector Machines
  97. Neural Networks
  98. FastAI
  99. LightGBM
  100. Pyforest Tutorial
  101. 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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