Data Scientists have different personalities; Some may be a jack of all trades, others may be a master of one. There is no denying that most of the data scientists aim to have a basic knowledge of common machine learning algorithms which would help them resolve new-domain problems.

The following article lists out the common machine learning algorithms that can help you get started on data science.

#### Principal Component Analysis(PCA)

This is one of the primary algorithms to learn when you are in the field of Data Science. PCA helps shrink the dimension of the data keeping most of the information intact. It is used in many areas such as object recognition, computer vision, data compression, etc.

#### K-means clustering

This is one of the favourite uncontrolled clustering algorithms. This algorithm is the simplest, however, it is also a little inaccurate clustering method in classical implementation.

#### Logistic Regression

Logistic Regression is constrained Linear Regression with a nonlinearity application after weights are applied, hence restricting the outputs close to +/- classes. Logistic Regression is used for classification and not regression.

#### SVM (Support Vector Machines)

This is a linear model similar to Linear/Logistic Regression. It differs from Linear/Logistic Regression because it has a margin-based loss function.

#### Feed-Forward Neural Networks

These are essentially multi-layered Logistic Regression classifiers. Many layers of weights separated by non-linearities. FFNNs can be used for classification and unsupervised feature learning as auto-encoders.

#### Convolutional Neural Networks

Almost any state-of-the-art vision-based machine learning in the world today has been achieved using Convolutional Neural Networks. They can be used for Image Classification, Object Detection, segmentation of images, text classification, object detection or image segmentation.

#### Decision Trees

This is one of the most used machine learning algorithms. It is used for predictive models in data analysis and statistics. Decision trees create models that predict the value of the target variable based on multiple input variables.

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