@@ -238,16 +238,7 @@ const navItems = [
238
238
"Understand the different types and methods introduced by MLJ" ,
239
239
]
240
240
} ,
241
- {
242
- name : "KNN & Ridge Regression Learning Network on AMES Pricing Data" ,
243
- href : "/end-to-end/AMES/" ,
244
- tags : [ "Regression" , "Learning Networks" , "Hyperparameter Tuning" ] ,
245
- ilos : [
246
- "Get familiar with building baselines models for your machine learning task" ,
247
- "Learn how to build simple learning networks (advanced model composition) in MLJ" ,
248
- "Learn how to tune and analyze the evaluation results from learning networks"
249
- ]
250
- } ,
241
+
251
242
{
252
243
name : "KNN, Logistic Regression and PCA on Wine Dataset" ,
253
244
href : "/end-to-end/wine/" ,
@@ -328,7 +319,7 @@ const navItems = [
328
319
]
329
320
} ,
330
321
{
331
- name : "Linear Regression on Temporal Power Generation Data" ,
322
+ name : "Linear Regression on Temporal Power Data" ,
332
323
href : "/end-to-end/powergen/" ,
333
324
tags : [ "Data Processing" , "Regression" ] ,
334
325
ilos : [
@@ -352,23 +343,7 @@ const navItems = [
352
343
]
353
344
} ,
354
345
{
355
- name : "Benchmarking Classification Models on Breast Cancer Data" ,
356
- href : "/end-to-end/breastcancer" ,
357
- tags : [
358
- "Encoders" ,
359
- "Classification" ,
360
- "Iterative Models" ,
361
- "Distribution Fitter" ,
362
- "Bayesian Models" ,
363
- "Neural Networks" ,
364
- ] ,
365
- ilos : [
366
- "Familiarize yourself with common data preprocessing and visualization workflows" ,
367
- "Learn how MLJ can be used to benchmark a large set of models against some dataset"
368
- ]
369
- } ,
370
- {
371
- name : "Credit Fraud Detection with Logistic Regression, SVM and Neural Networks" ,
346
+ name : "Credit Fraud Detection with Classical and Deep Models" ,
372
347
href : "/end-to-end/creditfraud" ,
373
348
tags : [
374
349
"Classification" ,
@@ -393,6 +368,32 @@ const navItems = [
393
368
id : "advanced" ,
394
369
href : "#!" ,
395
370
sections : [
371
+ {
372
+ name : "Benchmarking Classification Models on Breast Cancer Data" ,
373
+ href : "/advanced/breastcancer" ,
374
+ tags : [
375
+ "Encoders" ,
376
+ "Classification" ,
377
+ "Iterative Models" ,
378
+ "Distribution Fitter" ,
379
+ "Bayesian Models" ,
380
+ "Neural Networks" ,
381
+ ] ,
382
+ ilos : [
383
+ "Familiarize yourself with common data preprocessing and visualization workflows" ,
384
+ "Learn how MLJ can be used to benchmark a large set of models against some dataset"
385
+ ]
386
+ } ,
387
+ {
388
+ name : "KNN & Ridge Regression Learning Network on AMES Pricing Data" ,
389
+ href : "/advanced/AMES/" ,
390
+ tags : [ "Regression" , "Learning Networks" , "Hyperparameter Tuning" ] ,
391
+ ilos : [
392
+ "Get familiar with building baselines models for your machine learning task" ,
393
+ "Learn how to build simple learning networks (advanced model composition) in MLJ" ,
394
+ "Learn how to tune and analyze the evaluation results from learning networks"
395
+ ]
396
+ } ,
396
397
{
397
398
name : "Build Basic Learning Networks with MLJ" ,
398
399
href : "/advanced/ensembles-3" ,
0 commit comments