- Relationship between SVD and PCA. How to use SVD to perform PCA?
- In Depth: Principal Component Analysis
- In-Depth: Manifold Learning
- Recursive Feature Elimination
- A tutorial on Principal Components Analysis - PDF
- Principal Component Analysis explained
- Step Forward Feature Selection: A Practical Example in Python
- Python Data Science Handbook
- Uma visão sintética e comentada do Data Management Body of Knowledge - PDF
- Minimally Sufficient Pandas
- Why and How to Use Pandas with Large Data
- Getting started with Data Analysis with Python Pandas
- Python Pandas: Tricks & Features You May Not Know
- Pandas - Getting started tutorials
- Pandas Tutorial: Essentials of Data Science in Pandas Library
- Python Pandas Tutorial: A Complete Introduction for Beginners
- Basic Time Series Manipulation with Pandas
- Tidy data
- Python For Data Science Cheat Sheet Pandas Basics - PDF
- Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning
- Understanding the Bias-Variance Tradeoff
- Introduction to Machine Learning Algorithms: Linear Regression
- 7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regression
- The Gauss-Markov Theorem and BLUE OLS Coefficient Estimates
- Tikhonov regularization
- Ridge Regression for Better Usage
- Lasso (statistics)
- Tutorial: Understanding Regression Error Metrics in Python
- Understand Regression Performance Metrics
- Tensorflow on Google - PDF
- Confusion matrix and other metrics in machine learning
- Let’s learn about AUC ROC Curve!
- Classification Algorithms Comparison
- Having an Imbalanced Dataset? Here Is How You Can Fix It.
- Foundations of Imbalanced Learning - PDF
- Data Mining for Imbalanced Datasets - An OverView - PDF
- An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain - PDF
- Explaining the Success of Nearest Neighbor Methods in Prediction - PDF
- Classification: Basic Concepts, Decision Trees, and Model Evaluation - PDF
- Exploratory Data Analysis
- Exploratory Data Analysis
- A Gentle Introduction to Exploratory Data Analysis
- A Simple Tutorial on Exploratory Data Analysis
- Introduction to Hypothesis Testing - PDF
- The Power of Visualization in Data Science
- 15 Stunning Data Visualizations
- 15 Insane Things That Correlate With Each Other
- Feature Engineering
- Feature Scaling with scikit-learn
- How to win a kaggle competition
- What are some best practices in Feature Engineering?
- Discover Feature Engineering, How to Engineer Features and How to Get Good at It
- Fundamental Techniques of Feature Engineering for Machine Learning
- Feature Engineering Cookbook for Machine Learning
- Outlier detection with Scikit Learn
- Working With Text Data
- WTF is TF-IDF?
- How to self-learn statistics of data science
- Statistics Done Wrong
- Probability Theory Review for Machine Learning - PDF
- Understanding Probability Distributions
- Probability distribution
- Statistical Modeling: The Two Culture - PDF
- TEORIA DAS PROBABILIDADES II - Variáveis Aleatórias Unidmensionais - PDF
- Probability and Information Theory
- A Gentle Introduction to Statistical Hypothesis Testing
- How to Correctly Interpret P Values
- A Dirty Dozen: Twelve P-Value Misconceptions - PDF
- An investigation of the false discovery rate and the misinterpretation of p-values - PDF
- Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
- Why Are P Values Misinterpreted So Frequently?
- Statistical Significance Explained
- Introduction to Probability Theory
- The Math Behind A/B Testing with Example Python Code
- Handy Functions for A/B Testing in Python
Esse documento foi originalmente compilado por Rodrigo Azevedo @razevedo1994