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3 changes: 3 additions & 0 deletions .github/dependabot.yml
Original file line number Diff line number Diff line change
Expand Up @@ -11,3 +11,6 @@ updates:
- "/docs"
schedule:
interval: "weekly"
ignore:
- dependency-name: "MLJ"

16 changes: 0 additions & 16 deletions .github/workflows/CompatHelper.yml

This file was deleted.

2 changes: 1 addition & 1 deletion docs/Project.toml
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Expand Up @@ -29,7 +29,7 @@ CategoricalDistributions = "0.2.2"
DataFrames = "1.8.2"
Distributions = "0.25.130"
Documenter = "1.17.0"
EvoTrees = "0.18.7"
EvoTrees = "0.18.7, 0.19"
InteractiveUtils = "1"
IterationControl = "0.5.4"
MLJBase = "1.14.0"
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2 changes: 1 addition & 1 deletion docs/src/learning_mlj.md
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Expand Up @@ -16,7 +16,7 @@ If you are an ML practitioner transitioning from another platform, we highly rec

- [Julia Data Science](https://github.com/JuliaDataScience/JuliaDataScience)

- [MLJTutorial](https://github.com/ablaom/MLJTutorial.jl): material for a 4 hour MLJ
- [MLJTutorial](https://github.com/JuliaAI/MLJTutorial.jl): material for a 4 hour MLJ
workshop

- [MLCourse](https://github.com/jbrea/MLCourse): Teaching material for an introductory
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4 changes: 2 additions & 2 deletions docs/src/working_with_categorical_data.md
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Expand Up @@ -246,8 +246,8 @@ d.(v_int)

In MLJ the objects with `OrderedFactor` or `Multiclass` scientific
type have machine type `CategoricalValue`, from the
[CategoricalArrays.jl]
(https://juliadata.github.io/CategoricalArrays.jl/stable/) package.
[CategoricalArrays.jl](https://juliadata.github.io/CategoricalArrays.jl/stable/)
package.
In some sense `CategoricalValue`s are an implementation detail users
can ignore for the most part, as shown above. However, you may want
some basic understanding of these types, and those implementing MLJ's
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56 changes: 1 addition & 55 deletions examples/using_mlj/README.md
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@@ -1,57 +1,3 @@
# Using MLJ

[Videos]() on using the MLJ software.

### Video timings

#### MLJ. Lesson 1

- 00:00 Introduction
- 01:05 Goals
- 01:48 Prerequisites and Getting Help
- 02:21 Supervised Learning Recap
- 05:31 Models and Machines
- 09:28 Live Coding: Regression
- 28:55 Scientific Types
- 32:29 Live Coding: Scitypes and Classification
- 55:45 End

#### Using MLJ. Lesson 2: Model Composition

- 00:00 Introduction
- 00:08 Goals
- 01:19 Prerequisites and Getting Help
- 02:00 Composite Models Defined
- 04:13 Model Pipelines
- 05:59 Data Leakage
- 08:37 Target Transformations
- 12:43 Live Coding: Pipelines
- 18:42 Live Coding: Target Transformations
- 20:35 Other Model Wrappers
- 25:02 End

#### Using MLJ. Lesson 3: Tuning Models

- 00:00 Introduction
- 00:06 Goals
- 01:12 Prerequisites and Getting Help
- 01:27 Live Coding: Learning Curves
- 13:27 Tuning as Model Wrapper
- 18:45 Live Coding: Grid Search
- 26:17 Live Coding: Random Search
- 30:48 Nested Resampling
- 33:11 Final Observations
- 36:59 End


## Resources

- All things MLJ: [juliaml.ai](https://juliaml.ai)

- [General MLJ learning resources](https://juliaai.github.io/MLJ.jl/dev/learning_mlj/)

- [slides](slides/): PDF slides used in the video presentations

- [code](code/): .jl files for the live coding parts of the presentations, including
package environement files Project.toml and Manifest.toml. **Keep these in the same folder
as the .jl files.**
Resources for the "Using MLJ" videos have moved to [here](https://juliaai.github.io/MLJTutorial.jl/dev/#*Using-MLJ*-(video-series).
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