Everyone has the rights to technology started so that everyone can have access to computers and mobile decides. When code without barrier initiative started, it is to reduce the gender gap in AI before evolving to a more diverse involvement of the population in decision making in AI. If everyone should have the rights to AI, then AI should move towards like everyone can create their own website. Inspired by this, the solution is to focus on Azure ML with minimal code to show that everyone can use AI. The suitable components in the ML Designer for the task are chosen.

What it does

What the project requires is to search for documents that means the same thing contextually. The documents are clustered using the existing clustering algorithm in Azure ML.

How we built it

Study the news, is there anything in common or outstanding different.

  1. Set up Azure ML.
  2. Launch Studio.
  3. Upload the Dataset
  4. Use the Azure ML Designer, 4.1 Preprocess text 4.2 Filter Based Feature Selection 4.3 Train Clustering model 4.4 Clustering 4.5 Evaluate the classification

Challenges we ran into

I didn't get the free credit to try out. I tried paying on my own but Azure policy to make request and wait for request for Pay as you use, too late! Also I didn't get time per weekend to work on this. Technically, Clustering vs Classification, which is a better option.

Accomplishments that we're proud of

I just set and give it a try! And I reach here!

What we learned

Theory and Practical! Self learning through exploration

What's next for Accentura-AI Professor_TPP

Use Feature Hash to convert text to numeric for classification

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