hotaki-lab,H. Hotaki,github

Developer Portfolio Landing Page Template

Introduction

Use this template if you need a quick developer / data science portfolio! Based on a Minimal Jekyll theme for GitHub Pages.

Installation

See full step by step tutorial on Medium.

You can use the editor on GitHub to maintain and preview the content for your website in Markdown files.

Whenever you commit to this repository, GitHub Pages will run Jekyll to rebuild the pages in your site, from the content in your Markdown files.

Markdown

Markdown is a lightweight and easy-to-use syntax for styling your writing. It includes conventions for

Syntax highlighted code block

# Header 1
## Header 2
### Header 3

- Bulleted
- List

1. Numbered
2. List

**Bold** and _Italic_ and `Code` text

[Link](url) and ![Image](src)

For more details see GitHub Flavored Markdown.

Roadmap

See the open issues for a list of proposed features (and known issues).

References

[1] Jekyll theme "Minimal" for GitHub Pages: https://github.com/pages-themes/minimal (CC0 1.0 Universal License)
[2] Dummy photo via: https://pixabay.com/photos/man-male-adult-person-caucasian-1209494/ (Pixabay License)
[3] Dummy thumbnail image created by rawpixel.com: https://www.freepik.com/free-vector/set-elements-infographic_2807573.htm (Standard Freepik License)

H. Hotaki's Projects

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

This game is a simple yet meaningful representation of a classic dice rolling game in a digital WebApp format. This game was designed via HTML, CSS, JavaScript, and DOM. The code works better if you download all files/folders locally, then run it via Atom, Azure, etc.

hotaki

Welcome to my simple digital portfolio. This website briefly showcases the breadth and depth level of knowledge and experience in the DS field. The projects consist of academic research and industry standard style projects related to DS field. Please check it out and your feedback is welcome. Thanks.

mitx_6.86x

Notes of MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning

ml-tutorial

Introduction to ML packages for the 6.86x course

product-review-sentiment-analysis

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

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