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

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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