86 Machine Learning Algorithms & Models Explained with Python
All Machine Learning Algorithms and Models Explained with Python programming language.
Aman Kharwal
Follow
Apr 5 · 2 min read
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
- Assumptions of Machine Learning Algorithms
- LeNet-5 Architecture
- Introduction and Approaches to build Recommendation Systems
- Mean-Shift Clustering
- Mini-Batch K-Means Clustering
- Part of Speech Tagging
- Performance evaluation Metrics
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
- Agglomerative Clustering
- VisualKeras for Visualizing a Neural Network
- Stochastic Gradient Descent
- Explained Variance
- F-Beta Score
- Classification Report
- Passive Aggressive Regression
- R2 Score
- Lazy Predict
- FLAML
- Missing Values Calculation
- t-SNE Algorithm
- AutoKeras Tutorial
- Bias and Variance
- Perceptron
- Class Balancing Techniques
- One vs All & One vs One
- Polynomial Regression
- BIRCH Clustering
- Independent Component Analysis
- Kernel PCA
- Sparse PCA
- Non Negative Matrix Factorization
- Neural Networks Tutorial
- PyCaret
- Scikit-learn Tutorial
- NLTK Tutorial
- TextBlob Tutorial
- Streamlit Tutorial
- 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
- Auc and ROC Curve
- 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
- Pyforest Tutorial
- 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.
