# Top 10 Algorithms & Models Every Data Scientist Should Know- continued (Unsupervised and reinforcement learning)

Hello everybody, so I would like to thank everyone who liked my previous article, which was all about, supervised learning algorithms and the uses of the same. All those who didn’t read the former article must read it and get the basic understanding of supervised learning algorithms, here’s the link :https://www.linkedin.com/pulse/10-algorithms-data-scientist-should-know-vyom-bhardwaj?trk=prof-post

For now let’s start with unsupervised learning and reinforcement learning, although both of them can be done with multiple algorithms but here we will discuss only a few popular ones. However, this does not mean you should try the other cause every problem in data science needs a special solution with the right hypothesis in the veracity and uncertainty.

Unsupervised learning:

I would like to define unsupervised learning as where there are no output datasets and the datasets are clustered under different classes. Thus you don’t have any trained dataset.

So the popular algorithms to solve it, are as follows:

7) Clustering Algorithms: As the name suggests clustering algorithms are used to group or regroup those elements that have similar traits, there are few clustering algorithms here below:

· Centroid-based algorithms

· Connectivity-based algorithms

· Density-based algorithms

· Probabilistic

· Dimensionality Reduction

· Neural networks / Deep Learning (Please don’t get into this cause the Wikipedia page will make you go insane, hence we will cover this in the next article)

8) PCA — Principal Component Analysis — the algorithms are used to convert possibly correlated variables into linear uncorrelated variables known as components, where the procedure is used is known as orthogonal transformation (little mathematical but if didn’t get it we have an example too)

In layman terms PCA help[s to streamline the 3D graphs into 2d by making the variables as linear as possible. However PCS doesn’t work with too noisy data and while dealing with computer visions , but still it’s one of the best we have.

9) Singular Value decomposition –

PCA is said to be a simple application of SVD but the algorithms are mainly used is computer visions, although autoencoders are one of the best approaches to deal with computer vision because it is based on neural networks.

10) ICA independent component analysis: ICA is more powerful algorithm than PCA, the underlying logic of these algorithms remains the same as in the case of PCA but here the variable is treated mutually independent and non-gaussian.

The technique is used to identify the speech signals and most of the voice recognition system like Google Assist and SIRI use this algorithm.

I would like to make a separate article on reinforcement learning make sure that the readers of these articles are well prepared to move forward with more intermediate things.

Make sure to get your trial for muoro.io if you want hassle free analytics with the above algorithms.