I have listed down 7 types of Machine Learning algorithms which you must know. You should have thorough knowledge of these algorithms and techniques. Why and where these algorithms are used, what is the mathematics behind these, how are these algorithms implemented in Python and R, how to measure performance of these algorithms etc?

Below is the list of basic types of Machine Learning algorithms:

1.

I will keep adding more algorithms and techniques to the list in future.

Below is the list of basic types of Machine Learning algorithms:

1.

**Classification Algorithms**- KNN (K-Nearest Neighbors)
- Naive Bayes
- Decision Trees and Random Forest
- SVM (Support Vector Machine)

**Regression Algorithms**- Linear Regression
- Logistic Regression

**Clustering and Association Algorithms**- K-Means Clustering

**Dimensionality Reduction Techniques**- Feature Selection and Feature Extraction
- PCA (Principal Component Analysis)
- SVD (Singular Value Decomposition)
- LDA (Linear Discriminant Analysis)
- MDS (Multi-Dimension Scaling)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
- ICA (Independent Component Analysis)

**. Regularization**- Ridge Regression (L1 Regularization)
- Lasso Regression (L2 Regularization)
- Elastic-Net Regression

6.

7. **Ensemble Learning Techniques and Algorithms**- Bagging and Boosting
- Random Forest
- AdaBoost
- Gradient Boosting Machine (GBM)
- XGBoost

**Time Series Analysis and Sentiment Analysis**I will keep adding more algorithms and techniques to the list in future.

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