I have listed down 7 types of Machine Learning algorithms which you must know. You should have thorough knowledge of these algorithms and techniques.

1.

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

- Why and where these algorithms are used?
- What is the mathematics behind these algorithms?
- How are these algorithms implemented in Python and R?
- How to measure the performance of these algorithms?

1.

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

**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 (L2 Regularization)
- Lasso Regression (L1 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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