The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases.
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The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases.
The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases.
Businesses carry out vector searches in NoSQL databases to improve an AI model's search accuracy and efficiency. During this micro course, you'll learn how to store and index vectors in MongoDB, perform vector searches, and apply the techniques in text similarity analysis and building image classification systems. Plus, you'll look at Cassandra, its features for storing and querying vectors, and how to carry out vector searches.
You'll also examine how to apply these concepts to building applications for movie recommendation, inventory management, and personalization. Plus, you'll get valuable practice applying your knowledge through hands-on labs and a real-world final project.
Note that this micro course is part of the Vector Database Fundamentals specialization, which is ideal for professionals who work with vector databases, relational databases, and NoSQL databases for AI. It requires a basic knowledge of MongoDB, Cassandra, and Node.js.
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Activities
Organize and review course materials
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Organizing and reviewing the course materials will help you stay on track and improve your understanding of the concepts covered.
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Organization
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Gather all course materials
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Create a filing system for the materials
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Regularly review the materials
Review MongoDB basics
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Reviewing the basics of MongoDB will strengthen your understanding of the fundamentals of document-oriented databases, which is essential for this course.
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MongoDB
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Review MongoDB documentation
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Create a MongoDB database and collection
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Insert, query, and update documents
Follow tutorials on vector search in MongoDB and Cassandra
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Following tutorials will provide you with practical guidance on how to store and query vectors in these databases.
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Vector Search
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Find online tutorials on vector search in MongoDB
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Follow the tutorials and complete the exercises
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Repeat the process for Cassandra
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Develop a Python script for vector search in MongoDB
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Building a Python script will allow you to apply your knowledge and reinforce your understanding of vector search in MongoDB.
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Vector Search
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Design the script's functionality
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Write the code for connecting to MongoDB
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Implement vector search queries
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Test and refine the script
Build an application for movie recommendation using vector search
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Developing an application will allow you to apply the concepts of vector search in a practical and real-world setting.
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Vector Search
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-
Gather data on movies and their features
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Preprocess and vectorize the data
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Create a vector search model
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Build a recommendation engine
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Deploy and test the application
Contribute to an open-source project related to vector databases
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By contributing to open source, you will gain valuable hands-on experience and deepen your understanding of the field.
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Open Source
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-
Find a suitable open-source project
-
Review the project's documentation and codebase
-
Identify an area where you can contribute
-
Make your contributions
-
Collaborate with other contributors
Organize and review course materials
Show steps
Organizing and reviewing the course materials will help you stay on track and improve your understanding of the concepts covered.
Browse courses on
Organization
Show steps
- Gather all course materials
- Create a filing system for the materials
- Regularly review the materials
Review MongoDB basics
Show steps
Reviewing the basics of MongoDB will strengthen your understanding of the fundamentals of document-oriented databases, which is essential for this course.
Browse courses on
MongoDB
Show steps
- Review MongoDB documentation
- Create a MongoDB database and collection
- Insert, query, and update documents
Follow tutorials on vector search in MongoDB and Cassandra
Show steps
Following tutorials will provide you with practical guidance on how to store and query vectors in these databases.
Browse courses on
Vector Search
Show steps
- Find online tutorials on vector search in MongoDB
- Follow the tutorials and complete the exercises
- Repeat the process for Cassandra
Develop a Python script for vector search in MongoDB
Show steps
Building a Python script will allow you to apply your knowledge and reinforce your understanding of vector search in MongoDB.
Browse courses on
Vector Search
Show steps
- Design the script's functionality
- Write the code for connecting to MongoDB
- Implement vector search queries
- Test and refine the script
Build an application for movie recommendation using vector search
Show steps
Developing an application will allow you to apply the concepts of vector search in a practical and real-world setting.
Browse courses on
Vector Search
Show steps
- Gather data on movies and their features
- Preprocess and vectorize the data
- Create a vector search model
- Build a recommendation engine
- Deploy and test the application
Contribute to an open-source project related to vector databases
Show steps
By contributing to open source, you will gain valuable hands-on experience and deepen your understanding of the field.
Browse courses on
Open Source
Show steps
- Find a suitable open-source project
- Review the project's documentation and codebase
- Identify an area where you can contribute
- Make your contributions
- Collaborate with other contributors
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