50 Machine Learning Algorithms Explained using Python
50 Machine Learning algorithms and models explained using Python.
Published in
2 min readMar 9, 2021
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 with Python
- PyCaret
- DBSCAN Clustering
- Naive Bayes
- Passive Aggressive Classifier
- Gradient Boosting (Used in implementing the Instagram Algorithm)
- Logistic Regression
- Linear Regression
- K-Means Clustering
- Dimensionality Reduction
- Principal Component Analysis
- Automatic EDA
- Feature Scaling
- Apriori Algorithm
- K Nearest Neighbor
- CatBoost
- SMOTE
- Hypothesis Testing (Commonly used in Outlier Detection)
- Content-Based Filtering
- Collaborative Filtering
- Cosine Similarity
- Tf-Idf Vectorization
- Cross-Validation
- Confusion Matrix
- 4 Graph Algorithms (Connected Components, Shortest Path, Pagerank, Centrality Measures)
- Ridge and Lasso Regression
- StandardScaler
- SARIMA
- ARIMA
- XGBoost Algorithm
- Long Short Term Memory (LSTM)
- One Hot Encoding
- Bidirectional Encoder Representations from Transformers (BERT)
- Facebook Prophet
- NeuralProphet
- AdaBoost Algorithm
- Random Forest Algorithm
- H2O AutoML
- Polynomial Regression
- Gradient Descent Algorithm
- Grid Search Algorithm
- Manifold Learning
- Decision Trees
- Support Vector Machines
- Neural Networks
- FastAI
- LightGBM
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.
