Academies/

Machine Learning Foundations

Machine Learning Foundations

Learn to build and evaluate supervised and unsupervised models: regression, clustering, anomaly detection, ensembles, and time series forecasting. Apply dimensionality reduction, model selection, validation, and interpretability techniques.

10 courses4 h 45 min totalEnglishfor Professionals learning Machine Learning Foundations
Learning Path

Machine Learning Fundamentals and Applications

beginner · 4 h 45 min

Learning route for Machine Learning Foundations

Earn the Machine Learning Fundamentals and Applications certificate

Enrol, then finish its 10 required courses for one certificate with a public verification link you can add to LinkedIn.

Enrol in this path
Start Here
Introduction to Linear & Logistic Regression
1

Introduction to Linear & Logistic Regression

15 min
Getting Started with Bias-Variance Tradeoff
2

Getting Started with Bias-Variance Tradeoff

15 min
Dimensionality Reduction Essentials
3

Dimensionality Reduction Essentials

15 min
Clustering Algorithms for Beginners
4

Clustering Algorithms for Beginners

45 min
Level 1
Introduction to Ensemble Methods
6

Introduction to Ensemble Methods

15 min
Time Series Forecasting Essentials
8

Time Series Forecasting Essentials

30 min
Model Selection & Validation Fundamentals
5

Model Selection & Validation Fundamentals

30 min
Getting Started with Anomaly Detection
7

Getting Started with Anomaly Detection

30 min
Level 2
Getting Started with Model Interpretability
9

Getting Started with Model Interpretability

45 min
Level 3
Model Interpretability for Beginners
10

Model Interpretability for Beginners

45 min