Few algorithms a Data science professional should know

1.Perceptron Algorithm
2.Hoeffding's Inequality
3. Data preparation:- Normalisation, Feature scaling, Binary scaling, standardisation
4.Machine Learning algorithms such as Linear , logistic regression, Principal Component analysis,
Artificial Neural Network, K- Nearest Neighbours, Naive bayes, K- means clustering,Support Vector Machine,Random Forerst
5. Validation algorithms

Perceptron Algorithm

1.     Initialize the weights and threshold to small random numbers.

2.     Present a vector x to the neuron inputs and calculate the output.

     3.      Update the weights 
     4.       Repeat steps 2 and 3 until:

o   the iteration error is less than a user-specified error threshold or

a predetermined number of iterations have been completed .

Hoeffding’s inequality
    A powerful technique—perhaps the most important inequality in learning theory—for bounding the probability that sums of bounded random variables are too large or too small

x