Video course · 12 chapters · 461 min · certificate
Machine Learning for Beginners: Regression, Classification, and Model Evaluation
A full beginner machine learning course: why ML goes beyond data analysis, applications, data science vs ML vs AI, the ML pipeline, supervised and unsupervised learning, classification vs regression, simple and multiple linear regression, model evaluation, bias, variance, regularisation and hyperparameter tuning.
What you'll learn
- Explain why and where ML is used
- Distinguish data science, ML and AI
- Describe the ML pipeline
- Differentiate supervised and unsupervised learning
- Build and evaluate linear regression models
- Manage bias, variance, regularisation and tuning
Chapters
12 chapters · 461:14-
2:49
01Intro Free preview
Introduction to machine learning
Course overview.
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9:15
02Motivation Members
Beyond data analysis
From describing to predicting.
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16:43
03Applications Members
Why ML: applications
Input → output action.
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7:16
04Concepts Members
Data science vs ML vs AI
Clearing confusion.
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17:54
05Pipeline Members
ML pipeline and supervised learning
Steps in industry.
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6:35
06Paradigms Members
Unsupervised and other paradigms
Beyond labels.
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13:58
07Supervised Members
Classification vs regression
Two supervised types.
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27:06
08Regression Members
Introduction to linear regression
Fitting a line.
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122:43
09Evaluation Members
Simple linear regression and evaluation
One predictor and metrics.
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112:48
10MLR Members
Multiple linear regression
Many predictors.
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71:08
11Generalisation Members
Bias, variance and regularisation
Over- and underfitting.
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52:59
12Tuning Members
Hyperparameter tuning and model selection
Choosing the best model.
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.