10 Algorithms data scientist should know

There is no second thought that the subfields of statistics and neurology have gained huge popularity in past years. As Big data and AI are said to be next big thing in the tech industry, machine learning & NLP has powers to predict what will happen next and all this based on the past data collected. Some of the most common examples of statistics and NLP use by companies are the Facebook news feeds display algorithm and Amazon book recommendation engine.

However when the dataset becomes humongous then identifying right patterns is not a cake walk because each new data set correlates with one another in various tangents, the arduous task is to find the right pattern in the matrix of complicated information.

Hence to get the right data and patterns these are the few algorithms that are really essential for any data scientist and machine-learning engineer:

We can classify these Algorithms into three different subsets that are supervised, unsupervised and reinforcement leanings all of these subsets derive their operators and logics from statistics, neurology and mathematics.

So let’s start with fire:

A) Supervised Learning: Supervised learning is suitable for that dataset where the label is available for certain data, and from that label, the filtrations are done to achieve the predictive values.

1) Decision trees: One of the simplest ways to produce well defined predictive algorithms, though over concentrating and making unnecessary large trees might not help you in building appropriate predictive algorithms. Decision trees are built by answering yes/no questions on certain parameters.

2) Naïve Bayes classification: Too complicated right but it’s actually not , the classifier is built upon the high school math baby’s probability formula. The major use of this classification is in face recognition software and yes that trending Snapchat filters use the same thing to detect your face correctly.

3) Linear Regression: sometimes is really good to use the basic regression models like linear regression or least square regression. It’s just fitting the data set in a formula of the straight line and drives the predictive outcome while using the formula and the model.

4) Logistic regression: logistic regression is used when we want to get the binomial outcome of one of more explanatory variables, it consists of discrete series and measures relationship with a categorical variable with one or more independent variable. Practical uses range from gets credit risk score m measure the ROI of marketing campaigns etc.

5) SVM – Support Vector Machine is a binary classification algorithm where one needs to hardcore mathematics to determine that how two points on data set are different from each other and the degree of their similarities & differences can be visualized with it .

6) Ensemble – One of the best algorithms to use for getting right predictive model under supervised learning. The advantages of using it are as follows:

• Reduce the degree of biases by taking account various parameters strategically

• Reducing the variance and hence producing subtle, it’s just done with the handful of scoring techniques based on probabilities.

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.

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