About

Prathamesh Nile

A dedicated individual with a strong background in Computer Science. My expertise lies in leveraging data to extract valuable insights and drive informed decision-making. With meticulous attention to detail and a collaborative mindset, I excel in a team environment. I have a proven track record of academic excellence, research achievements, and practical experience in the field of computer science. Eager to make a positive impact, I am poised to contribute my skills and expertise to the next phase of my career. With a solid foundation in computer science and a passion for data-driven solutions, I am driven to continue my journey of growth and success in this field.

Education

MASTER'S DEGREE OF COMPUTER SCIENCE

MTech Computer Science and Engineering
National Institute of Technology, Karnataka

BACHELOR'S DEGREE IN COMPUTER SCIENCE

B.E. Computer Engineering
Savitribai Phule Pune University

HIGH SCHOOL SECONDARY EDUCATION

Completed HSC from Dhanaji Nana College ,Jalgaon with 78% in March 2017

Completed SSC from Vidya English Medium High School ,Jalgaon with 93% in March 2015

Skills and Projects

SKILLS

1. C++
2. Python
3. Tableau
4. Rapid Miner
5. MySQL
6. Android Studio

ADDITIONAL SKILLS

1. Problem Solving
2. Mathematics
3. Aptitude
4. Leadership
5. Machine Learning
6. Data Science

Tableau Certified Professional


Mathematics for Machine Learning : Linear Algebra


Python for Data Science And AI


Cross Platform Mobile App Developement

PROJECTS

1. Handwritten Hindi Digit Recognition
2. Sentiment Analysis and Tweet Visualisation of Twitter
3. Principal Component Analysis using Autoencoders
4. Android App for RK Earthmovers And Land Developers

RESEARCH AND PUBLICATIONS

1. Handwritten Hindi Digit Recognition
(High Technology Letters ( HTL ) ยท Jun 5, 2021)


Handwritten digit recognition is practically one of the main applications in the fields in the areas of machine learning and in pattern recognition applications. The need to recognize the handwritten digits is a challenging task in ten class classification of the pattern recognition applications. Automatic recognition of handwritten digits is a difficult task, because of the variability in the writing styles, pen used for writing digits as well as the color of the handwritten digits which is in contrast to the printed characters through a computer. Furthermore, the Hindi Digits can be drawn or written in many different sizes. Hence, a robust offline Hindi handwritten digit recognition system has to account for all of such factors. Hence there is a need to take into account all the factors we need to choose global as well local features. The global and the local features include the endpoints of the digits, cross points of these digits, centroids of any loops of the digits, u shaped structures as well as C and inverted C structures which are to be taken care of. Variability in the writing styles of the digits is taken care of by size normalization and the normalizing them to constant thickness so that pre-processing of these digits becomes a lot easier. The method used for classifying these Digits is Artificial Neural network (ANN). The results obtained are also displayed in various forms such as graphical, histogram, etc. The results obtained are encouraged by taking into account the handwritten numerical samples of different people, different ages, different pen types, also different sizes which are successfully tested and results are excellent.

Experience

1. Tableau Desktop Professional Trainee

at IT Training Institute, Mumbai
March 2022

2. Data Science Intern

at Cloud Counselage Pvt.Ltd Mumbai
March 2020

3. Contributor

at DevIncept
September 2020

4. ISP

at Internshala
2020-21

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