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Machine Learning Algorithms: A Decision Tree Guide, Cheat Sheet of Machine Learning

machine learning is the most demanding skill, but people have problems understanding it, so I have prepared a cheat sheet for better and easy understanding and help in cracking the job interview.

Typology: Cheat Sheet

2020/2021

Available from 06/05/2022

kalash-bhadoriya
kalash-bhadoriya 🇮🇳

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Machine learning (ML) is a subfield of artificial intelligence (AI) and computer science that focuses
on using data and algorithms to mimic the way people learn, progressively improving its accuracy.
Use this tree graph as a guide to which ML algorithm to use to solve your AI problems.
(Principal
Component Analysis)
www.latinxinai.org
Dimension Reduction
Combining
Variables?
PCA
Probabilistic?
SVD
(Singular Value
Descomposition)
LDA
(Linear Discriminant
Analysis)
Working with
Labeled Data?
Hierarchical?
Hierarchical
Clustering
Needs to
Specify K?
DBSCAN
(Density-based Spatial
Clustering of
Applications with Noise)
Categorical
Variables?
K-Modes
Prefer
Probability?
K-Means GMM
(Gaussian Mixture
Models)
Predicting Numbers?
Speed or
Accuracy?
Speed or
Accuracy?
Accuracy
Neural Network
Gradient Boosting Tree
Random Forest
Speed
Decision Tree
Linear Regression
Speed
Explainable?
Decision
Tree
Logistic
Regression
Accuracy
Kernel Support-
Vector Machine
Neural Network
Gradient
Boosting Tree
Random Forest
Is the Data
Too Large?
Linear Support-
Vector Machine
Naive Bayes
Naive Bayes
Feature
Selection
Feature
Extraction
Unsupervised Learning:
Dimension Reduction
Supervised Learning:
Regression
Supervised Learning:
Classification
Unsupervised Learning:
Clustering

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Machine learning (ML) is a subfield of artificial intelligence (AI) and computer science that focuses

on using data and algorithms to mimic the way people learn, progressively improving its accuracy.

Use this tree graph as a guide to which ML algorithm to use to solve your AI problems.

(Principal Component Analysis) www.latinxinai.org

Dimension Reduction

Combining

Variables?

PCA

Probabilistic?

SVD

(Singular Value Descomposition)

LDA

(Linear Discriminant Analysis) Working with Labeled Data?

Hierarchical?

Hierarchical

Clustering

Needs to

Specify K?

DBSCAN

(Density-based Spatial Clustering of Applications with Noise) Categorical Variables?

K-Modes

Prefer Probability?

K-Means

GMM

(Gaussian Mixture Models)

Predicting Numbers?

Speed or

Accuracy?

Speed or Accuracy? Accuracy Neural Network Gradient Boosting Tree Random Forest

Speed

Decision Tree Linear Regression Speed Explainable? Decision Tree Logistic Regression Accuracy

Kernel Support-
Vector Machine
Neural Network
Gradient
Boosting Tree
Random Forest

Is the Data Too Large? Linear Support- Vector Machine Naive Bayes Naive Bayes

Feature

Selection

Feature

Extraction

Unsupervised Learning: Dimension Reduction Supervised Learning: Regression Supervised Learning: Classification Unsupervised Learning: Clustering