ReadingNoteIdx.md

Reading Notes Index for Data Science

Recommended books, articles and courses

• D. Smith, Lionbridge, 10 Free Top Notch Machine Learning Courses
• R. Verma, 24 Best (and Free) Books To Understand Machine Learning
• V. Granville, 19 Great Articles About Natural Language Processing (NLP)

Visualization

• K. He & G. Meeden, Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach

Probability & Statistics

Basics

• Thomas Maydon, The 4 Types of Data Analytics, 2017
• R. Joseph, Which hypotheses test to perform?, 2018

Study Design

• JW. Song & KC. Chung, Observational studies: cohort and case-control studies. Plast Reconstr Surg. 2010;126(6):2234–2242

Bayesian Approaches

• D. Spiegelhalter, K. Abrams, J. Myles, An Overview of the Bayesian Approach, Chapter 3 in Bayesian Approaches to Clinical Trials and Health-Care Evaluation, 2004+ S. Ghosh, Basics of Bayesian Methods, in "Methods in molecular biology" (Clifton, N.J.) 620:155-78, 2010
• J. Martin Bland and Douglas G. Altman, Bayesians and frequentists
• Magdalena Szumilas, Explaining Odds Ratios
• H. Liu and L. Wasserman, "Bayesian Inference" Part A, Part B, and Part C

Time Series Analysis

• K. Gray, Time Series Analysis: A Primer, 2016

Linear Algebra

General

• V. K. Yadav, A Comprehensive Beginners Guide to Linear Algebra for Data Scientist

Operations & Properties

• Wikipedia, Determinant
• Wikipedia, Invertible Matrix

Eigenvalues and Eigenvector

• Wikipedia, Eigenvalues and Eigenvector
• Wikipedia, Eigenvalue algorithms

Artificial Intelligence

Machine Learning

General Topics for ML

• P. Domingos, A Few Useful Things to Know about Machine Learning
• R. Kohavi, Randal M. Henne, and Dan Sommerfield, Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO
• S. Kaufman, S. Rosset, & C. Perlich, Leakage in Data Mining: Formulation, Detection, and Avoidance
• M. Zinkevich, Rules of Machine Learning: Best Practices for ML Engineering
• M. Saar-Tsechansky and Foster Provost, Handling Missing Values when Applying Classification Models
• Graphs
• K. Gray, Making Sense of Machine Learning
• M. Mayo, Decision Tree Classifiers: A Concise Technical Overview
• M. Mayo, Comparing Clustering Techniques: A Concise Technical Overview
• P. Domingos, A Few Useful Things to Know about Machine Learning, Univ. of Washington, 2012
• L. Smith, A Disciplined Approach to Neural Network Hyper-Parameters: Part-I - Learning Rate, Batch Size, Momentum, and Weight Decay, 2018
• ML Glossary, Logistic Regression

Feature Engineering

• Pier Paolo Ippolito, Feature Selection Techniques
• A. Singh, 6 Powerful Feature Engineering Techniques For Time Series Data (using Python)
• Y. Charfaoui, Hands-on with Feature Engineering Techniques, 2018
  • Broad Introduction
  • Variable Types
  • Common Issues in Datasets
  • Imputing Missing Values
  • Categorical Variables Encoding
  • Transforming Variables
  • Variables Discretization
  • Handling Outliers
  • Feature Scaling
  • Handling Time-Date and Mixed Variables
  • Advanced Feature Engineering Methods
• W. McGinnis, Beyond One-Hot: An Explanation of Categorical Variables
• S. Mazaanto, Beyond One-Hot. 17 Ways of Transforming Categorical Features Into Numeric Features
• R. Holbrook, Feature Engineering, Kaggle
  • What is Feature Engineering
  • Mutual Information
  • Creating Feature
  • Clustering with K-Means
  • Principal Components Analysis
  • Target Encoding
  • Example: House Prices

Feature Selection

• Y. Charfaoui, Hands-on with Feature Selection Techniques, 2018
  • An Introduction
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  • Hybrid Methods
  • More Advanced Methods

Adaptive Learning

• P. Rojas, Fast Learning Algorithms, 1996

Machine Learning Models

• M. Mayo, Decision Tree Classifiers: A Concise Technical Overview
• M. Mayo, Comparing Clustering Techniques: A Concise Technical Overview

Support Vector Machine (SVM)

• Matthew Mayo, Support Vector Machines: A Concise Technical Overview, 2016
• Noel Bambrick, Support Vector Machines: A Simple Explanation, 2016
• GregL, Why Use SVM?
• Eric Kim, Everything You Wanted to Know about the Kernel Trick, 2015

Application - Social Networks

• D. Easley and J. Kleinberg, Power Laws and Rich-Get-Richer Phenomena, Chapter 18 in Networks, Crowds, and Markets: Reasoning About a Highly Connected World
• D. Easley and J. Kleinberg, The Small-World Phenomenon, Chapter 20 in Networks, Crowds, and Markets: Reasoning About a Highly Connected World

Neural Networks

General Topics for Neural Networks

• M. Stewart, Introduction to Neural Networks
• M. Stewart, Intermediate Topics in Neural networks
• M. Stewart, Neural Network Optimization
• M. Stewart, Simple Guide to Hyperparameter Tuning in Neural Networks
• M. Stewart, Neural Style Transfer and Visualization of Convolutional Networks
• M. Stewart, Advanced Topics in Neural Networks
• R. Nerd, Delta Learning Rule & Gradient Descent | Neural Networks
• D. Rollins, Delta Function
• S. Ray, Understanding and coding Neural Networks From Scratch in Python and R

Activation Functions

• C. McCormick, Deep Learning Tutorial - Softmax Regression
• Softmax Classifier in CS231n Convolutional Neural Networks for Visual Recognition, Stanford University

Convolution Neural Networks

• A. Deshpande, A Beginner's Guide To Understanding Convolutional Neural Networks, 2016

Deep Learning

• A. Deshpande, The 9 Deep Learning Papers You Need to Know About

DataBase for Data Science

Python Implementation

• G. Seif, 23 great Pandas codes for Data Scientists