Vector Search with NoSQL Databases using MongoDB & Cassandra from Coursera

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.

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches essential skills for professionals in data science, machine learning, generative AI, and software development

Covers vector searches in NoSQL databases, which are in high demand in industry

Provides strong foundations for using Apache Cassandra and MongoDB for vector searches

Includes hands-on labs and a practical final project for real-world application

Offered as part of a specialization on vector database fundamentals, allowing for further exploration of the topic

Reviews summary

Practical vector search with mongodb & cassandra

According to students, this course offers highly relevant skills in vector search using MongoDB and Cassandra, crucial for AI and GenAI applications. The curriculum emphasizes a hands-on approach with practical labs and a real-world project, making it ideal for data scientists, ML engineers, and software developers. It’s noted for its focus on text similarity analysis and recommendation systems, but learners should have basic knowledge of MongoDB, Cassandra, and Node.js.

Part of a larger specialization for deeper learning.

"I understand this micro course is part of the broader Vector Database Fundamentals specialization."

"It feels like a good stepping stone for those interested in pursuing the full vector database specialization."

"I appreciate that this course can lead to deeper learning within an overall specialization track."

Geared towards specific technical professionals.

"This course is clearly designed for data scientists, ML engineers, and software developers like me."

"I find the content perfectly suited for professionals working with vector and NoSQL databases in the AI space."

"It’s excellent for enhancing my skills as a GenAI engineer, addressing direct industry needs."

Strong emphasis on real-world applications and labs.

"The hands-on labs are crucial for applying what I learn, providing valuable practical experience."

"I'm excited about tackling a real-world final project; it solidifies my understanding."

"It's great to see how these concepts apply to actual applications like movie recommendation and inventory management systems."

Acquire skills for a rapidly growing market.

"I'm learning skills directly applicable to the vector database market, which is projected to grow significantly."

"This course provides the sought-after skills for performing vector searches, which is incredibly valuable right now."

"I appreciate learning how to improve AI model search accuracy and efficiency, a key benefit for my work."

Assumes basic understanding of specific technologies.

"I need a basic knowledge of MongoDB and Cassandra before starting this course, which is important to know upfront."

"Be sure to brush up on Node.js fundamentals if you're planning to take this, as it's a stated prerequisite."

"This isn't a beginner course for database concepts; I found it helpful to come prepared with prior knowledge."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Search with NoSQL Databases using MongoDB & Cassandra with these activities:

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

Three other activities

Expand to see all activities and additional details

Show all six activities

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

Career center

Learners who complete Vector Search with NoSQL Databases using MongoDB & Cassandra will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Vector Search with NoSQL Databases using MongoDB & Cassandra

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical vector search with mongodb & cassandra

Activities

Career center

Reading list

Share

Similar courses