Graduate Students

Curriculum Vitae

Course (Applied Machine Learning):

Tutorials:

Index:

Fascinating Blogs
Interesting Papers
Bias-Variance Trade-Off
Feature Engineering
Metric Learning
Machine Learning in R
Statistical Models for Machine Learning
Time Series
Interpretability
Cheat Sheets
Videos

Fascinating Blogs:

Towards Data Science: This is a platform for data scientists to propose up-to-date content, mainly focused on data science, machine learning, artificial intelligence, and …
Machine Learning Crash Course from Google: Google’s fast-paced, practical introduction to machine learning which covers building deep neural networks with TensorFlow.
Distill is an academic journal in machine learning and it was dedicated to clear explanations of machine learning.
Machine Learning Plus: Simple and straightforward tutorials on machine learning in R and Python.
The blog of Dawid Kopczyk: Fascinating tutorials about machine learning
The blog of Christopher Olah: Fascinating tutorials about neural networks
Machine Learning Recipe: Fascinating tutorials about machine learning
Off the Convex Path: Understanding non- convex optimization in algorithms, machine learning and nature at large
Data Vedas: This blog was created by Rai Kapil keeping in mind the difficulties faced by people who are new to the field of data science.
Need Help Getting Started with Applied Machine Learning? by Jason Brownlee
Awesome Data Science: An open source Data Science repository to learn and apply towards solving real world problems.
New to Data School? Start Here! by Data School
R2D3: An experiment in expressing statistical thinking with interactive design
Machine Learning Resources by Ritchie Ng
Top 10 Machine Learning Algorithms for Beginners

Interesting Papers:

A Few Useful Things to Know about Machine Learning by Pedro Domingos
The Unreasonable Effectiveness of Data by Alon Halevy, Peter Norvig, and Fernando Pereira
The End of Theory: The Data Deluge Makes The Scientific Method Obsolete by Chris Anderson

Bias-Variance Trade-Off:

Paper: The Bias-Variance Dilemma by Raul Rojas
Blog: Bias-Variance Tradeoff in Machine Learning by Satya Mallick
Blog: Understanding the Bias-Variance Tradeoff
Blog: Bias and Variance in Machine Learning by Renu Khandelwal
Blog: Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning
Blog: A Visual Introduction to Machine Learning: Model Tuning and the Bias-Variance Trade Off by Stephanie Yee and Tony Chu
Blog: The Bias-Variance Tradeoff in Statistical Machine Learning - The Regression Setting
NoteBook: Exploring the Bias-Variance Tradeoff by Kevin Markham

Feature Engineering:

:sparkles: Blog: Feature Engineering
:sparkles: Blog: Selecting Statistical-Based Features in Machine Learning Application by Pravin Dhandre (This article is an excerpt from a book Feature Engineering Made Easy co-authored by Sinan Ozdemir and Divya Susarla)
:sparkles: Blog: Feature Selection – Ten Effective Techniques with Examples (in R)
Blog: Fundamental Techniques of Feature Engineering for Machine Learning by Emre Rencberoglu
Blog: How to Create Useful Features for Machine Learning by Kevin Markham
Blog: Non-Mathematical Feature Engineering Techniques for Data Science by Sachin Joglekar
Blog: Feature Selection – Part I (Univariate Selection) by Ando Saabas
Blog: Feature Selection Using Genetic Algorithms in R by Pablo Casas
Book: Feature Engineering for Machine Learning and Data Analytics by Guozhu Dong and Huan Liu
Book: Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by By Alice Zheng and Amanda Casari
Book: Feature Engineering Made Easy: Identify Unique Features from Your Dataset in Order to Build Powerful Machine Learning Systems by by Sinan Ozdemir and Divya Susarla

Metric Learning:

Paper: Distance Metric Learning, with Application to Clustering with Side-Information
Survey: A Survey on Metric Learning for Feature Vectors and Structured Data by Aurelien Bellet, Amaury Habrard, and Marc Sebban
Install: Metric-Learn
Example: Metric Learning and Plotting

Machine Learning in R:

Blog: Caret Package by Max Kuhn
NoteBook: Principles of Machine Learning R
Blog: Caret Package – A Practical Guide to Machine Learning in R
Blog: An Introduction to Machine Learning with R Laurent Gatto
Blog: Practical Machine Learning Course Notes by Xing Su
Cheat Sheet: Caret Package by Max Kuhn

Statistical Models for Machine Learning:

Tutorial: Poisson Regression in R by Hafsa Jabeen
Blog: Using Linear Regression for Predictive Modeling in R by Rose Martin
Tutorial: Understanding Regression Error Metrics in Python by Christian Pascual
Lecture: Poisson Models for Count Data by Germán Rodríguez

Time Series:

Tutorial: Time Series Analysis with Pandas by Jennifer Walker

Interpretability:

Book: Interpretable Machine Learning by Christoph Molnar

Cheat Sheets:

Cheat Sheets by Kailash Ahirwar

Videos:

Machine Learning Video Library - Learning From Data by Yaser Abu-Mostafa