heemokyim's repositories
10-steps-to-become-a-data-scientist
๐ข Ready to learn! you will learn 10 skills as data scientist:๐ Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.
Credit-Card-Fraud
A Very Deep Neural Network that can classify Credit Card Fraudulent Transaction with 99.92% Accuracy.
Datascience_Portfolio
My journey with datascience
DeepNeuralNetwork_HousePrices
Deep neural network created with python + keras to estimate housing prices from Kaggle dataset
dsschool
dsschool jupyter notebook
ELO
Elo Merchant Category Recommendation 21th solution
elo-recommender
Kaggle Competition
eloMerchantComp_kaggle
https://www.kaggle.com/c/elo-merchant-category-recommendation
EloMerchantKaggle
This is a repository for elo merchant kaggle competition.
GooglePlay-Review-Crawler
Crawl the review of google play store 2018
heemokyim.github.com
github pages
heemokyim.github.io
Build a Jekyll blog in minutes, without touching the command line.
kaggle
Kaggle competitions
Learn-Keras-for-Deep-Neural-Networks
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. Youโll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, youโll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months" By Siraj Raval on Youtube
Learn_Deep_Learning_in_6_Weeks
This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube
Master-project
Project from 2017
seaborn
Statistical data visualization using matplotlib
Study_Python
Python ๊ณต๋ถ ๋ด์ฉ์ ์ ๋ฆฌํฉ๋๋ค.
wns-analytics-wizard-2018
Winners solutions for [WNS Analytics Wizard 2018](https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018/)