# Top 9 Machine Learning Algorithms for Data Scientists

Posted on August 01, 2020

For most of the beginners, the machine learning algorithms seem to be uninteresting or boring to learn further about. Up to some extent, this stands true but in many cases, you might stumble upon a few-page description for each algorithm and it can turn out to be time-consuming in order to figure out each and every detail. If you have a strong desire to become a Machine Learning expert then you actually need to brush up your knowledge related to it as there is no other way around. In this article, we will be talking about the 8 most common algorithms in simple words including a brief overview along with some useful links. So, let's begin!

## #1 Conditional Random Fields (CRFs) #

This algorithm is used to simulate a sequence of an RNN which can be used in conjunction with an RNN algorithm. They can also be used in various tasks of structured prediction as in image segmentation. The algorithm models each element of the sequence so that the neighbors are affecting the component labels in the sequence and not all the labels are independent of each other. You can use CRF for the sequence of sequences in text, image, time series, and DNA.

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## #2 Convolutional Neural Networks #

In practical, all the modern achievements in the field of machine learning can be achieved by dint of convolutional neural networks that are used for image classification, image segmentation, and object detection. It was invented by Jan Lekun in the early 90s, networks have convolutional layers that act as hierarchical object extractors. You can use it to work with text and even for working with graphics.

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## #3 Principal Component Analysis (PCA)/SVD #

This is one of the most important machine learning algorithms which allows you to reduce the dimension of the data by losing the amount of information. The algorithms are used in multiple areas like object recognition, computer vision, data compression and much more. The computational of the principal components are reduced to calculating the eigenvalues and eigenvectors of the covariance matrix of the original data and to the singular decomposition of the data matrix. You can express several signs though one, merge, speak and work already with a simpler model. Of course, it is not possible to avoid information loss but the PCA method will help you to minimize that. SVD is the way to calculate the ordered components.

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## #4 Decision Trees #

This is one of the most common machine learning algorithms which is used in statistics and data analysis for predictive models. The structure represents the leaves and branches. Attributes of the objective function widely depend on the branches of the decision tree where the values of the objective function are stored in the leaves and the remaining nodes contain attributes for which the cases differ. In order to classify a new case, you need to go down the tree to the leaf and give the appropriate value. The ultimate goal is to create a model that predicts the value of the target variable based on several input variables.

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## #5 Feed-Forward Neural Networks (FFNN) #

In general, there are unit-level logistic regression classifiers. Multiple layers of scales are separated by non-linearities like sigmoid, tanh, cool new selu and relu + softmax. They are also known as multilayer perceptrons as FFNN can be used to classify and learn without a guide as autoencoders. The algorithm can be used to train a classifier or extract the functions as autoencoders.

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## #6 K-Means Clustering #

This is everyone's favorite uncontrolled clustering algorithm. The k-means algorithms are the simplest but inaccurate clustering method in its classical implementation. The method splits the set of elements of a center space into a previously known number of clusters k. This algorithm minimizes the standard deviation at the points of each cluster. Here, the basic idea is that at each iteration the center of mass is recalculated for each cluster that you get in the previous step. Then, the vectors are divided into the clusters again according to which the new centers were closed in the selected metric.

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### #7 Logistic Regression #

It is limited to linear regression with non-linearity after applying weights and hence the output limit is close to 1 and 0 in case of the sigmoid. Also, the cross-entropy loss functions are optimized using the gradient descent method. Logistic regression is used for classification and not regression. It is the same as a single-layer neural network and when learned using optimization techniques like gradient descent or L-BFGS, it is called 'maximum entropy classification method'.

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## #8 Support Vector Machines (SVM) #

It is a linear model similar to linear and logistic regression. The difference between them is that it has a margin-based loss function which you can optimize the loss function by using optimization methods i.e. SGD or L-BFGS. The most unique thing that SVMs can do is to study classifier classifiers as it can be used to train the classifiers and even regressors.

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## #9 Recurrent Neural Networks (RNNs) #

This model sequences by applying the same set of weights at time t and input at time t of the aggregator recursively. Pure recurrent neural networks are rarely used now but it is analogs. For instance - LSTM and GRU are the most up to date in most sequence modeling problems. LSTM is used instead of a simple dense layer in pure RNN. Use this network for any task of text classification, machine translation, and language modeling.

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