Cigdem Ozen's repositories
2015
Public material for CS109
ADSBDE
Advanced Data Science & Big Data Engineering
azure-quickstart-templates
Azure Quickstart Templates
challenges-kubernetes
:cloud: Challenges Your Kubernetes Skills And Knowledge
cheatsheet-aws-A4
:cloud: AWS CheatSheets In A4
cheatsheet-docker-A4
:book: Docker CheatSheets In A4
cheatsheet-python-A4
:book: Advanced Python Syntax In A4
Cheatsheets
Essential Cheat Sheets for deep learning and machine learning researchers
Data-Science-Cheat-Sheet
Cheat Sheets
dslp-repo-template
Template repository for data science lifecycle project
FES
Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson
git-book
Sync documentation with GitBook
Imputation-Techniques
Fast, efficient code to pull non-null categorical data out, encode it and impute nulls with KNN Impute from fancyimpute library
matplotlib_tutorial
Matplotlib Tutorial Library
MDI
Missing Data Imputation Python Library
mlcourse.ai
Open Machine Learning Course
notebooks
:notebook: A growing collection of Jupyter Notebooks written in Python, OCaml and Julia for science examples, algorithms, visualizations etc
Pandas-Data-Science-Tasks
Set of real world data science tasks completed using the Python Pandas library
Python-Notebooks
Jupyter Notebooks for How to Think Like a Computer Scientist - Learning with Python 3 (RLE) Textbook
python-notlarim
Python notes in Turkish.
python_course-1
Official website for of LEIXIR-IIB training
stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
theMLbook
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
VeriDefteri
Veri Defteri Üzerine Kısa Notlar (www.veridefteri.com)
week1-python
Learn the basics of Python 3 programming
week2-data-analysis
Learn the basics of NumPy, Pandas and Matplotlib
week3-supervised-learning
Learn the basics of Supervised Learning
week4-unsupervised-learning
Learn the basics of Unsupervised Learning
week5-preprocessing-and-tunning
Learn how to prepare the data for machine learning algorithms and how to fine tune the hyper-parameters of a model