Index

A

• Alternating Least Squares (ALS)
  • about / Matrix factorization
  • limitations / Limitations of ALS
• Anaconda
  • download link / Setting up the environment

B

• backpropagation / Neural networks and deep learning
• BaseSimpleEstimator interface / The BaseSimpleEstimator interface
• bias-variance dilemma / Error terms
• bias-variance trade-off / Error terms
• bias trade-off
  • about / The bias/variance trade-off
  • error terms / Error terms
  • error, due to / Error due to bias

C

• CARTClassifier / An example of supervised learning in action
• Classification and Regression Trees (CART) / Decision trees – an introduction
• clustering
  • methods / Various clustering methods
  • about / What is clustering?
  • distance metrics / Distance metrics
• coefficient / Parametric models
• cold-start problem / Limitations of content-based systems
• collaborative filtering
  • about / Recommended systems and an introduction to collaborative filtering
  • item-to-item / Item-to-item collaborative filtering
• content-based filtering
  • about / Content-based filtering
  • limitations / Limitations of content-based systems

D

• data
  • splitting / Model evaluation and data splitting
  • splitting, scikit-learn library used / Splitting made easy
• decision trees
  • about / Introduction to non-parametric models and decision trees, Decision trees – an introduction, Decision trees
  • example / An intuitive example – decision tree
  • implementing / How do decision trees make decisions?
  • tree, splitting by hand / Splitting a tree by hand
  • splitting, on x1 / If we split on x1
  • splitting, on x2 / If we split on x2
  • implementing, from scratch / Implementing a decision tree from scratch
  • classification tree / Classification tree
  • regression tree / Regression tree
• deep learning / Neural networks and deep learning
• descent / Hill climbing and descent

E

• environment
  • setting up / Setting up the environment
• Euclidean distance / Distance metrics, Recommended systems and an introduction to collaborative filtering

F

• finite-dimensional models / Finite-dimensional models

H

• high bias
  • handling, strategies for / Strategies for handling high bias
  • strategies, for handling / Strategies for handling high bias
• high variance
  • handling, strategies for / Strategies for handling high variance
  • strategies, for handling / Strategies for handling high variance
• hill climbing / Hill climbing and loss functions, Hill climbing and descent
  • about / Hill climbing and descent
• homophily / Recommended systems and an introduction to collaborative filtering

I

• in-sample evaluation
  • versus out-of-sample evaluation / Out-of-sample versus in-sample evaluation

K

• KDTree / A classic KNN algorithm
• KNN
  • about / KNN – introduction
  • considerations / KNN – considerations
  • classic KNN algorithm / A classic KNN algorithm
  • implementing, from scratch / Implementing KNNs from scratch
• KNN clustering / KNN clustering

L

• learning curves / Learning curves
• linear regression
  • implementing, from scratch / Implementing linear regression from scratch
• logistic function / Hill climbing and descent
• logistic regression
  • about / Logistic regression
  • implementing, from scratch / Implementing logistic regression from scratch
  • example / Example of logistic regression
• logistic regression algorithm
  • about / The algorithm
  • example / Example of logistic regression
• logistic regression models
  • about / Logistic regression models
  • concept / The concept
  • math / The math
  • logistic (sigmoid) transformation / The logistic (sigmoid) transformation
  • algorithm / The algorithm
  • predictions, creating / Creating predictions
• loss functions
  • about / Hill climbing and loss functions, Loss functions
  • slope, measuring of curve / Measuring the slope of a curve
  • slope, measuring of Nd-curve / Measuring the slope of an Nd-curve
  • slope, measuring of multiple functions / Measuring the slope of multiple functions

M

• machine learning (ML) / Recommended systems and an introduction to collaborative filtering
• Mahalanobis / Distance metrics
• Manhattan / Distance metrics
• matrix factorization
  • about / Matrix factorization
  • in Python / Matrix factorization in Python
• model
  • evaluation / Model evaluation and data splitting
• model parameters / Finite-dimensional models

N

• neural network
  • about / Neural networks and deep learning, Neural networks
  • training, tips for / Tips and tricks for training a neural network
• non-parametric / Parametric models
• non-parametric learning algorithms
  • characteristics / Characteristics of non-parametric learning algorithms
• non-parametric models
  • about / Introduction to non-parametric models and decision trees
  • learning / Non-parametric learning
  • model parametric, considerations / Is a model parametric or not?
  • pros / Non-parametric models – pros/cons, Pros of non-parametric models
  • cons / Non-parametric models – pros/cons, Cons of non-parametric models
  • considerations / Which model to use?

O

• out-of-sample evaluation
  • versus in-sample evaluation / Out-of-sample versus in-sample evaluation

P

• parametric learning algorithms
  • characteristics / The characteristics of parametric learning algorithms
• parametric models
  • about / Parametric models
  • finite-dimensional models / Finite-dimensional models
  • example / Parametric model example
  • pros / The pros and cons of parametric models
  • cons / The pros and cons of parametric models
• Pearson correlation / Recommended systems and an introduction to collaborative filtering
• perceptron / Neural networks and deep learning
• Python
  • matrix factorization / Matrix factorization in Python

R

• recommended systems / Recommended systems and an introduction to collaborative filtering

S

• scikit-learn library
  • used, for splitting data / Splitting made easy
• Spam dataset / An example of supervised learning in action
• Sum of Squared Error (SSE) / Loss functions
• supervised learning in action
  • example / An example of supervised learning in action
• supervised machine learning
  • example / An example of supervised learning in action
  • about / Supervised learning

T

• Training score / An example of supervised learning in action, Learning curves
• transfer learning
  • using / Using transfer learning

U

• underfitting / The pros and cons of parametric models
• unsupervised machine learning / Supervised learning

V

• Validation score / An example of supervised learning in action, Learning curves
• variance trade-off
  • about / The bias/variance trade-off
  • error terms / Error terms
  • error, due to / Error due to variance