Fixed in Readme.md
@@ -14,20 +14,20 @@ Although the template provides standard implementation to different functionalit
### Repository Structure
1. **[template](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/cloudml-template/template)**: The directory includes:
- 1) trainer template with all the python modules to adapt to your data,
- 2) setup.py,
- 3) confg.yaml file for hyper-parameter tuning and specifying the Cloude MLE scale-tier, and
+ 1) trainer template with all the python modules to adapt to your data.
+ 2) setup.py.
+ 3) config.yaml file for hyper-parameter tuning and specifying the Cloude MLE scale-tier.
4) inference.py python (sample) script to perform prediction using a deployed model's API on Cloud ML Engine.
2. **[scripts](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/cloudml-template/scripts)**: The directory includes command-line scripts to:
- 1) train the model locally,
- 2) train the model on Cloud ML Engine, and
+ 1) train the model locally.
+ 2) train the model on Cloud ML Engine.
3) deploy the model on GCP as well as to make prediction (inference) using the deployed model.
3. **[examples](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/cloudml-template/examples)**: Currently four different examples are included:
- 1. classification, using the [Census Income](https://archive.ics.uci.edu/ml/datasets/Census+Income) (UCI Machine Learning Repository) dataset,
- 2. regression, using the [House Pricing](https://www.kaggle.com/apratim87/housingdata/data) (Kaggle) dataset,
- 3. regression, with a custom estimator, using [Natality Baby Weight](https://catalog.data.gov/dataset?tags=birth-weight) (data.gov) dataset, and
+ 1. classification, using the [Census Income](https://archive.ics.uci.edu/ml/datasets/Census+Income) (UCI Machine Learning Repository) dataset.
+ 2. regression, using the [House Pricing](https://www.kaggle.com/apratim87/housingdata/data) (Kaggle) dataset.
+ 3. regression, with a custom estimator, using [Natality Baby Weight](https://catalog.data.gov/dataset?tags=birth-weight) (data.gov) dataset.
4. classification, with a custom estimator, using [Statlog (German Credit Data)](https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) (UCI Machine Learning Repository) dataset.
The examples show how the template is adapted given a dataset. The datasets are found in the examples' folders (under "data" sub-directory).
