Video course · 12 chapters · 1081 min · certificate
Machine Learning with Python and Scikit-Learn – Full Course
A hands-on freeCodeCamp course on machine learning with Python and scikit-learn: problem framing, EDA, linear and multiple regression, logistic regression, decision trees and random forests, regularisation and tuning, a project framework, XGBoost, clustering and an end-to-end project.
What you'll learn
- Frame ML problems and explore data
- Build linear and logistic regression models
- Train decision trees and random forests
- Tune hyperparameters and regularise
- Use gradient boosting with XGBoost
- Apply clustering and run an end-to-end project
Chapters
12 chapters · 1080:34-
3:32
01Intro Members
Introduction
Hands-on ML.
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3:55
02Problem Members
Problem statement and data
Replace manual decisions.
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42:11
03EDA Members
EDA and visualisation
Understand the data.
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47:02
04Linear Members
Linear regression with one feature
Age vs charges.
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47:18
05MLR Members
Multiple regression and categorical features
More features.
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180:10
06Classification Members
Logistic regression
Discrete categories.
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100:13
07Trees Members
Decision trees and random forests
Real-world decisions.
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21:08
08Tuning Members
Regularisation and tuning
Bootstrapping.
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161:02
09Process Members
Approaching ML projects
Step by step.
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137:00
10XGBoost Members
Gradient boosting with XGBoost
K-fold CV.
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90:18
11Unsupervised Members
Clustering
No labels.
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246:45
12Project Members
End-to-end ML project
From scratch.
Jobs this course suits
Our AI checked this course against 500 jobs; these get the most out of it. Each job links to its learning path.