chore: bump version number

[mhamilton723]

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Briefly describe the changes included in this Pull Request.

How is this patch tested?

• I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

• No. You can skip this section.
• Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

• No. You can skip this section.
• Yes. Make sure you have added samples following below steps.

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[acrolinxatmsft1]

Acrolinx Scorecards

A minimum Acrolinx score of 80 is required.

Click the scorecard links for each article to review the Acrolinx feedback on grammar, spelling, punctuation, writing style, and terminology.

Article	Acrolinx score	Word and Phrases Score	Correctness Score	Scorecard	Processed
docs/Explore Algorithms/Deep Learning/Getting Started.md	100	100	100	link	✅
docs/Explore Algorithms/Other Algorithms/Cyber ML.md	92	100	88	link	✅
docs/Get Started/Install SynapseML.md	81	100	60	link	✅
docs/Reference/Docker Setup.md	87	100	88	link	✅
docs/Reference/Dotnet Setup.md	93	100	67	link	✅
docs/Reference/Quickstart - LightGBM in Dotnet.md	90	100	54	link	✅
docs/Reference/R Setup.md	78	100	53	link	✅

More information about Acrolinx

• Install Acrolinx locally for VSCode
• False positives or issues
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[github-actions]

Hey @mhamilton723 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
This helps us to create release messages and credit you for your hard work!

Examples of commit messages with semantic prefixes:

• fix: Fix LightGBM crashes with empty partitions
• feat: Make HTTP on Spark back-offs configurable
• docs: Update Spark Serving usage
• build: Add codecov support
• perf: improve LightGBM memory usage
• refactor: make python code generation rely on classes
• style: Remove nulls from CNTKModel
• test: Add test coverage for CNTKModel

To test your commit locally, please follow our guild on building from source.
Check out the developer guide for additional guidance on testing your change.

[acrolinxatmsft1]

Acrolinx Scorecards

A minimum Acrolinx score of 80 is required.

Click the scorecard links for each article to review the Acrolinx feedback on grammar, spelling, punctuation, writing style, and terminology.

Article	Acrolinx score	Word and Phrases Score	Correctness Score	Scorecard	Processed
docs/Explore Algorithms/Deep Learning/Getting Started.md	100	100	100	link	✅
docs/Explore Algorithms/Other Algorithms/Cyber ML.md	92	100	88	link	✅
docs/Get Started/Install SynapseML.md	81	100	60	link	✅
docs/Reference/Docker Setup.md	87	100	88	link	✅
docs/Reference/Dotnet Setup.md	93	100	67	link	✅
docs/Reference/Quickstart - LightGBM in Dotnet.md	90	100	54	link	✅
docs/Reference/R Setup.md	78	100	53	link	✅
website/versioned_docs/version-0.11.3/Deploy Models/Overview.md	78	100	68	link	✅
website/versioned_docs/version-0.11.3/Deploy Models/Quickstart - Deploying a Classifier.md	68	50	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Advanced Usage - Async, Batching, and Multi-Key.md	79	100	44	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Geospatial Services.md	72	57	73	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Multivariate Anomaly Detection.md	98	100	91	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Overview.md	91	100	95	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Analyze Celebrity Quotes.md	100	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Analyze Text.md	100	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Creare a Visual Search Engine.md	77	100	68	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Create Audiobooks.md	100	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Document Question and Answering with PDFs.md	75	100	87	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Flooding Risk.md	77	74	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/AI Services/Quickstart - Predictive Maintenance.md	79	67	86	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Anomaly Detection/Quickstart - Isolation Forests.md	72	100	83	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Causal Inference/Overview.md	89	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Causal Inference/Quickstart - Measure Causal Effects.md	86	100	74	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Causal Inference/Quickstart - Measure Heterogeneous Effects.md	87	100	75	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Classification/Quickstart - SparkML vs SynapseML.md	96	100	82	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Classification/Quickstart - Train Classifier.md	92	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Classification/Quickstart - Vowpal Wabbit on Tabular Data.md	82	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Classification/Quickstart - Vowpal Wabbit on Text Data.md	73	72	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Deep Learning/Distributed Training.md	95	100	100	link	✅
website/versioned_docs/version-0.11.3/Explore Algorithms/Deep Learning/Getting Started.md	100	100	100	link	✅

More information about Acrolinx

• Install Acrolinx locally for VSCode
• False positives or issues
• Troubleshooting issues with Acrolinx