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

Default image
Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1207

Leave a Reply