Add caveats about MLJBase versions needed for describe, etc

docs/src/notebooks/UsingMLJ/01_basics/notebook.jl

@@ -7,14 +7,26 @@
 # To run the code in this tutorial in a live Julia session, first follow the instructions
 # given [here](@ref instructions).
 
+# ### Video Timings
+
+# - 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
+
 # We start by inspecting the packages, and their exact versions, in the currently active
 # package environment:
 
 using Pkg
 Pkg.status()
 
 
-# # Part I. Regression
+# ## Part I. Regression
 
 using MLJ
 using UnicodePlots # for pretty display of labeled probability vectors
@@ -116,7 +128,7 @@ scitype(3.143f0)
 scitype(["cat", "mouse", "dog"])
 
 
-# # Part II. Classification
+# ## Part II. Classification
 
 # New data set for classification, the Adult Dataset (census data):
 using Downloads, CSV

docs/src/notebooks/UsingMLJ/02_model_composition/notebook.jl

@@ -7,6 +7,20 @@
 # To run the code in this tutorial in a live Julia session, first follow the instructions
 # given [here](@ref instructions).
 
+# ### Video Timings
+
+# - 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
 
 # Load some model code:
@@ -64,7 +78,10 @@ evaluations = evaluate(
     measure=mav,
 )
 
-# Here's a pretty view of these results:
+# (To provide multiple models, tagged with strings, to `evaluate` requires MLJBase 1.12.0
+# or higher.)
+
+# Here's a pretty view of these results (needs MLJBase 1.13.0 or higher):
 
 describe.(evaluations) |> pretty
 

docs/src/notebooks/UsingMLJ/03_model_tuning/notebook.jl

@@ -7,6 +7,20 @@
 # To run the code in this tutorial in a live Julia session, first follow the instructions
 # given [here](@ref instructions).
 
+# ### Video Timings
+
+# - 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
+
+
 # ## Part I. Learning Curves
 
 using MLJ, Plots
@@ -123,7 +137,7 @@ e1 = evaluate(tuned_pipe, X, y; options...)
 
 @show ebase e0 e1;
 
-# Or, we can do this:
+# Or, we can do this (needs MLJBase 1.13.0 or higher):
 
 describe.([e0, e1]) |> pretty