Monday 11 February 2019

7 Basic Types of Machine Learning Algorithms You Must Know

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 algorithms?
  • How are these algorithms implemented in Python and R? 
  • How to measure the performance of these algorithms? 
Below is the list of basic types of Machine Learning algorithms:

1. Classification Algorithms
  • KNN (K-Nearest Neighbors)
  • SVM (Support Vector Machine)
  • Naive Bayes
  • Decision Trees and Random Forest
2. Regression Algorithms
  • Linear Regression
  • Logistic Regression
3. Clustering and Association Algorithms
  • K-Means Clustering
4. 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)
5. Regularization 
  • Ridge Regression (L2 Regularization)
  • Lasso Regression (L1 Regularization)
  • Elastic-Net Regression
6. Ensemble Learning Techniques and Algorithms 
  • Bagging and Boosting
  • Random Forest
  • AdaBoost
  • Gradient Boosting Machine (GBM)
  • XGBoost
7. Time Series Analysis and Sentiment Analysis

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

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I am currently messing up with neural networks in deep learning. I am learning Python, TensorFlow and Keras.

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