Resources

Topics

Activation Functions

Adaboost

Adaptive Experimental Design

Akaike Information Criterion (AIC)

Analyses for Controlled Randomized Design (CRD)

Artificial Neural Network Optimization

Artificial Neural Networks (ANN)

Back Propagation

Bayes Theorem

BeautifulSoup

Bias–Variance Tradeoff

Boosting

Bootstrap

Bootstrap-Aggregating (Bagging)

Categorical Predictors

Causal Effects

Classification

Classification Boundaries

Classification Criteria

Classification Non-Separable Data

Collinearity

Common Bosting Techniques

Communication

Comparison of Classification Methods

Comparison of Methods

Confidence Intervals

Controlling for Confounding (Advantages/Disadvantages)

Convolutional Neural Networks (CNN)

Cross-Validation (CV)

Data Analysis

Data Augmentation

Data Cleaning

Data Collection

Data Engineering

Data Exploration

Data Formats: JSON and CSV

DataFrame

Decision Tree

Decision Trees

Demo

Descriptive Statistics

Dictionaries

Dimensionality Reduction

Discriminant Analysis

Dropout

Effective Communication

Effective Visualization

Eigenvectors and Eigenvalues

Ensemble Methods

Ensemble Models

Entropy

Ethics for Data Science

Experiments and AB-testing

Exploratory Data Analysis (EDA)

Exponential Family

Fairness Transparency and Accountability (FAT)

Feature Scaling

Feature Selection

Feed Forward Neural Network

Functions

Generalized Linear Models

Gini Index

Gradient Boosting

Gradient Descent

Gram Matrix

Greedy Backward Elimination

Greedy Forward Selection

High Dimensionality Reduction

Hyperparameter Optimization

Hypothesis Testing

Importance of Predictors

Imputation Methods

Information Criteria (AIC/BIC)

Information Theory

Interaction Terms

K-Nearest Neighbors (KNN) Regression

Keras

Kernel Trick

Kullback–Leibler (KL) Divergence

Lasso

Likelihood Function

Linear Algebra

Linear Discriminant Analysis (LDA)

Linear Regression

Link Function

Lists

Logistic Regression

Loss Function

Matplotlib

Maximum Likelihood Estimation (MLE)

Mean Squared Error (MSE)

Mis-classification Rates

Model Fitness

Model Selection

Multi Layer Perceptron (MLP)

Multilayer Perceptron (MLP)

Multinomial Logistic Regression

Multiple Linear Regression

Multiple Logistic Regression

Multivariate Normal distribution

NN

Norm Penalties

NumPy

One Versus Rest (OVR)

Out of Bag Error (OOB)

Output Units

Overfitting

P-values

Pandas

Parse HTML

Perceptron

Polynomial Regression

Principal Component Analysis (PCA)

Principle Components Analysis (PCA)

Probabilistic Linear Regression

Probit

Pruning

Quadratic Discriminant Analysis (QDA)

R-squared (R2)

Radio Operator Characteristics (ROC)

Random Forest

Read Data

Regression Trees

Regularization

Ridge

Root Mean Squared Error (RMSE)

Scikit-learn

SciPy

Scraping

Seaborn

Softmax

Sparse Representation

Stacking

Standard Errors

Statistical Parity

Support Vector Classifier (SVC)

Support Vector Machine (SVM)

Support Vector Machines (SVM)

Tensor

The Data Science Process

The Grammar of Data

Universal Approximitation Theorem

Variable Importance

Visualization

XGBoost