This blogpost will demonstrate how to set up Azure Functions with some Python code. More precisely, it will show how to call an Azure Function, add a parameter that specifies the name of the file that we want to read from and store that information in a database. Between reading and storing we have the chance to execute any python code we want. As long as it stays below the 30 min threshold :)
The following gives an overview of the most basic concepts found in python. It serves as a quick reminder when not having coded in python for a while.
Source: Download text file or Fork me on GitHub
Main if __name__ == '__main__': main() List <list> = <list>[from_inclusive : to_exclusive : step_size] <list>.append(<el>) <list>.extend(<collection>) <list> += [<el>] <list> += <collection> <list>.sort() <list>.reverse() <list> = sorted(<collection>) <iter> = reversed(<list>) sum_of_elements = sum(<collection>) elementwise_sum = [sum(pair) for pair in zip(list_a, list_b)] sorted_by_second = sorted(<collection>, key=lambda el: el[1]) sorted_by_both = sorted(<collection>, key=lambda el: (el[1], el[0])) flatter_list = list(itertools.
Conda How to set up a virtual environments using conda for the Anaconda Python distribution
A virtual environment is a named, isolated, working copy of Python that that maintains its own files, directories, and paths so that you can work with specific versions of libraries or Python itself without affecting other Python projects. Virtual environmets make it easy to cleanly separate different projects and avoid problems with different dependencies and version requiremetns across components.
Source https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation
$$Y= a *X + b$$
The best way to understand linear regression is to relive this experience of childhood.
Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values (binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected).
