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.