Name: Yohannes Didana
Type: User
Company: Banking and Finance
Bio: I am a versatile, self-motivated professional who is passionate about machine learning, deep learning and getting insight from data using my analytical skills.
Twitter: dr_didana
Location: Australia
Blog: https://github.com/yohannes-didana
Yohannes Didana's Projects
A python library for easy manipulation and forecasting of time series.
General Assembly's 2015 Data Science course in Washington, DC
This is a LinkedIn Learning repo for Data Cleaning in Python Essential Training.
Free Data Engineering course!
Machine learning datasets used in tutorials on MachineLearningMastery.com
The Leek group guide to data sharing
Deep Learning with TensorFlow, Keras, and PyTorch
Efficient Python Tricks and Tools for Data Scientists
The data represents credit card transactions that occurred over two days in September 2013 by European cardholders.
In this project, a regression model will be used to predict the chance of admission into a particular university based on the student's profile.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
This Repo contain Head First series eBook.
A set of useful tools for competitive data science.
Code & supporting files for chapters in book
Deep Learning
Learn the language basics in this 10-part course.
ipyvizzu is the Jupyter Notebook integration of Vizzu.
Code for the "Burn CPU, burn" competition at Kaggle. Uses Extreme Learning Machines and hyperopt.
A compiled list of kaggle competitions and their winning solutions for classification problems.
A compiled list of kaggle competitions and their winning solutions for image problems.
A compiled list of kaggle competitions and their winning solutions for miscellaneous problems.
A compiled list of kaggle competitions and their winning solutions for regression problems.
A compiled list of kaggle competitions and their winning solutions for sequence problems.
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.
Companion to Learning Hadoop and Learning Spark courses on Linked In Learning
Automatically visualize your pandas dataframe via a single print! 📊 💡
Code Repository for Machine Learning with PyTorch and Scikit-Learn