Sri Yogesh K.

Data Hacker | Deep Learning Enthusiast | Filmmaker

Student @ INSOFE

This website lists a few of my projects and includes blog posts detailing tips, tricks and insights into the fields of Data Science and Deep Learning.

To download my resume, please CLICK HERE

Interests

Creating Visualisations, Reports and Dashboards in R
Building Models and Architecting Deep Learning solutions in Python & R
Solving Big Data problems using the Apache Ecosystem

Education

Deep Learning Foundations Nanodegree, 2017

Udacity
Big Data Analytics & Optimization, 2017

International School of Engineering
BS in Biology, 2016

IISER Bhopal

Courses

A list of Courses completed/pursuing related to Data Science, Deep Learning & Big Data

CPEE (Certificate Program in Engineering Excellence) in Big Data Analytics and Optimization at Internationa School of Engineering, 2017. [Course Certified for the quality of Content, Pedagogy and Assessment by the School of Computer Science at Carnegie Mellon University, USA]
Deep Learning Foundations Nanodegree at Udacity (Currently Pursuing)
The R Programming Environment by Johns Hopkins University on Coursera. Click here for the verifiable certificate
The Data Scientist’s Toolbox by Johns Hopkins University on Coursera. Click here for the verifiable certificate
Using Python to Access Web Data by University of Michigan on Coursera. Click here for the verifiable certificate
Python Data Structures by University of Michigan on Coursera. Click here for the verifiable certificate
R Programming by Johns Hopkins University on Coursera. Click here for the verifiable certificate
Programming for Everybody (Getting Started with Python) by University of Michigan on Coursera. Click here for the verifiable certificate
Data Analyst Nanodegree at Udacity (Currently Pursuing)

Projects

A selection of Data Science/Deep Learning Projects

EXPLORE AND SUMMARIZE DATA IN R

Investigated a dataset using R and exploratory data analysis techniques, exploring both single variables and relationships between variables. Then, presented the findings in a neatly formatted html file.

INVESTIGATE A DATASET IN PYTHON

Posed a question about a dataset, then used NumPy and Pandas to answer that question based on the data and created a report to share the results.

PREDICTING THE DOMAIN OF TEXT DATA IN PYTHON

Investigated a dataset in a pandas dataframe, tokenized the text and used a Naïve Bayes Classifier to predict the domain of a given text.

Preliminary Report on the Retrospective analysis of Patient Adherence

This is a preliminary report I created to measure and predict patient adherence on a retrospective dataset

TEST A PERCEPTUAL PHENOMENON

Analyzed the Stroop effect using descriptive statistics to provide an intuition about the data, and inferential statistics to draw a conclusion based on the results.

Recent Posts

Getting started with the Academic framework for Hugo

Wed, Apr 20, 2016 academic, hugo

Create a beautifully simple personal or academic website in under 10 minutes.

Customizing the homepage with widgets

Wed, Apr 20, 2016 academic, hugo

Enable/disable and configure widgets to customize your homepage.

Managing content

Wed, Apr 20, 2016 academic, hugo

This is a brief guide to managing content with the Academic framework. Content can include publications, projects, talks, and news/blog articles. After you have read this guide about creating and managing content, you may also be interested to learn about writing content with Markdown, LaTeX, and Shortcodes.

Writing content with Markdown, LaTeX, and Shortcodes

Wed, Apr 20, 2016

Content can be written using Markdown, LaTeX math, and Hugo Shortcodes. Additionally, HTML may be used for advanced formatting. This article gives an overview of the most common formatting options.

Hello R Markdown

Thu, Jul 23, 2015 R R Markdown, plot, regression

R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. You can embed an R code chunk like this: summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.

Contact

Open to Collaborate on Interesting Projects!!

sriyogesh94@gmail.com