# Tutor profile: Kabir M.

## Questions

### Subject: Python Programming

Programming is one of the fundamentals today in just about any work. Knowing how to code fluently can help you perform things a lot faster. There are many languages you can express programming in. One of the is Python and it has become a default in many industry. Why is it though?

In the early days, the way to communicate with a computer to perform tasks was using machine dependent or assembly languages. Although, those codes used to run quite efficiently, programming in them was a turmoil. I programmed in 8085 microprocessor and the instruction set were very complicated. However, we have slowly moved to high level languages which are very easy to for us humans to write in. Python is as high level a language as available. It is easy to understand for a beginner and still can is very efficient. I can help you understand the basics of programming in python, functional and object oriented programming, conditional programming, recursions and data structures. I will also explain about some key concepts of time complexity, parallel programming, thread programming, etc. I will also work together to solve problems you might be finding complicated. I will also draw parallels of python programming language with other programming languages such as Scala, Java, C, C++ that I have worked with so that it may help you to move to a different language efficiently.

### Subject: Machine Learning

Machine Learning is the flavor of computer science right now. There are lot of people interested in it. But what is it though? What can we do with it?

Machine Learning heavily relies on pattern recognition and inferences. We can break it down into supervised (using training data for knowledge extraction), unsupervised (understanding patterns for inferences) and reinforcement learning(maximizing a method based on rewards). I can help you understand the dynamics of supervised learning techniques such as regression models (linear and logistic), neural networks and unsupervised learning such as anomaly detection. I can demonstrate and explain natural language processing techniques which can help you extract structured information from the random texts. I can explain how recommendations systems work and how it can give you precise recommendations. I can also help you understand the essence of cost functions, talk about what over fitting and under fitting actually are and loads of other stuffs. Although understanding the theory of it all is important, understanding the application is even more so. I will be predominantly working on machine learning in my masters degree and can talk about things I have done both as an academic and in my past workplace. I will be delighted to have detailed conversations of projects you are working on, or just an idea you may be interested in and not sure how ML can help you solve it.

### Subject: Statistics

The midterm and final exam scores of 10 students in a statistics course are tabulated as shown. (a) Calculate the least squares regression line for these data. X Y 70 87 74 79 80 88 84 98 80 96 67 73 70 83 64 79 74 91 82 94

Let $$ y= a +bx $$ be the line that best estimates the points with the least error. We can calculate the values of a and b, we have $$ a= \frac{\Sigma Y. \Sigma X^{2} -\Sigma X . \Sigma XY}{n. \Sigma X^{2} - (\Sigma X)^{2}} $$ $$ b=\frac{n.\Sigma XY - \Sigma X . \Sigma Y}{n. \Sigma X^{2} - (\Sigma X)^{2}} $$ From the above give data we can calculate $$ \Sigma X= 745 , \Sigma Y= 868, \Sigma XY = 65087, \Sigma X^{2}= 55917 $$ Placing the above values in the equations, we can estimate the value of a and b to be $$ a=11.132 $$ $$ b= 1.016 $$ And the regression line is : $$y = 11.132 + 1.016x $$ We can then use this line to make future predictions such as when x is 58 for a student in a mid-term what score might he/she get in his final exam. This will be $$ 11.132 + 1.016*58 = 70.6 $$ I can help you to understand how to create such equations, how to get those estimates along with explaining about various other probability and statistical inference topics.

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