Video course · 15 chapters · 260 min · certificate
Machine Learning – Beginner's Course
A beginner's ML course: a 2024 roadmap, must-have skills and career paths, ML basics, the bias-variance trade-off, overfitting and regularisation, linear and logistic regression theory, and a full linear regression case study from data cleaning and outliers to causal and predictive analysis.
What you'll learn
- Follow an ML learning roadmap
- Explain bias-variance and overfitting
- Understand linear and logistic regression theory
- Clean data and detect outliers with IQR
- Run causal and predictive linear regression
- Check OLS assumptions
Chapters
15 chapters · 259:32-
10:39
01Roadmap Members
Introduction and 2024 roadmap
Where to start.
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28:15
02Skills Members
Must-have skills
Growing field.
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22:05
03Careers Members
Career paths and ML basics
Job titles.
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7:05
04Bias-variance Members
Bias-variance trade-off
Balance.
-
15:34
05Regularisation Members
Overfitting and regularisation
Control complexity.
-
13:18
06Stats Members
Linear regression: statistical view
Significance.
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23:24
07Theory Members
Linear regression theory
Causal analysis.
-
15:17
08Logistic Members
Logistic regression theory
Classification.
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18:07
09Case Members
Case study introduction
Practical start.
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12:15
10Variables Members
Loading data and defining variables
X and y.
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17:40
11Cleaning Members
Cleaning and descriptive stats
Missing values.
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10:21
12Outliers Members
IQR outlier detection
Interquartile range.
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20:31
13Split Members
Correlation and train/test split
sklearn.
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26:53
14Causal Members
Running regression: causal analysis
statsmodels.
-
18:08
15Assumptions Members
OLS assumptions and prediction
Check and predict.
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.