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Guil Hernandez
This course offers an advanced journey into the realm of AI engineering, focusing on the creation, utilization, and management of embeddings in vector databases. Learners will begin by grasping the concept of embeddings and their pivotal role in AI's interpretative processes. The course progresses through practical exercises on setting up environment variables, creating embeddings, and integrating these into vector databases with tools like Supabase.
Read more
This course offers an advanced journey into the realm of AI engineering, focusing on the creation, utilization, and management of embeddings in vector databases. Learners will begin by grasping the concept of embeddings and their pivotal role in AI's interpretative processes. The course progresses through practical exercises on setting up environment variables, creating embeddings, and integrating these into vector databases with tools like Supabase.
Read more
This course offers an advanced journey into the realm of AI engineering, focusing on the creation, utilization, and management of embeddings in vector databases. Learners will begin by grasping the concept of embeddings and their pivotal role in AI's interpretative processes. The course progresses through practical exercises on setting up environment variables, creating embeddings, and integrating these into vector databases with tools like Supabase.
Participants will engage in challenges that test their ability to pair text with corresponding embeddings, manage semantic searches, and use similarity searches to query embeddings. They will also learn to create conversational responses with OpenAI and handle complex tasks like chunking text from documents.
What makes this course unique is its comprehensive coverage of both the theoretical aspects of AI embeddings and the practical skills needed to implement these concepts in real-world applications. By the end of the course, learners will not only have mastered the technical knowledge but will also have developed a proof of concept for an AI chatbot, ready to tackle advanced AI engineering challenges.
Register for this course and see more details by visiting:
OpenCourser.com/course/p5i2tv/learn
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What's inside
Syllabus
Learn Embeddings and Vector Databases
This course provides an in-depth look at AI engineering with a focus on creating and using embeddings in vector databases. Starting with the basics of embeddings, learners will advance through practical tasks involving environment setup, embedding creation, and database integration using tools like Supabase.
Challenges will test skills in text pairing, semantic and similarity searches, and crafting AI conversational responses, leading to a final project that solidifies their understanding. This course stands out for its mix of theory and hands-on practice, preparing participants to develop an AI chatbot by the end.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops an understanding of embeddings and their role in AI interpretation
Covers practical skills, such as setting up environment variables, creating embeddings, and integrating them into vector databases
Provides hands-on exercises and challenges to test learners' abilities
Includes a project that allows learners to apply their knowledge to create an AI chatbot
Teaches skills that are relevant to industry and academia
Requires students to come in with some background knowledge in AI and programming
Save this course
Save Learn Embeddings and Vector Databases to your list so you can find it easily later:
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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 Learn Embeddings and Vector Databases with these
activities:
Review Linear Algebra Fundamentals
Show steps
Strengthens foundational knowledge in linear algebra, ensuring a solid base for understanding vector spaces and other advanced AI engineering concepts.
Browse courses on
Linear Algebra
Show steps
-
Go through online resources or textbooks to refresh basic linear algebra topics.
-
Practice solving problems related to vector operations, matrix transformations, and linear equations.
Course Materials Review
Show steps
Enhances retention and understanding by reinforcing course concepts through the review and organization of learning materials.
Show steps
-
Organize lecture notes, readings, assignments, and other course materials.
-
Create summaries, mind maps, or concept trees to reinforce key ideas.
-
Identify areas where further clarification or review is needed.
Review Vector Database Concepts
Show steps
Refreshes foundational skills in vector database fundamentals, ensuring a strong conceptual understanding for advanced topics in AI Engineering.
Browse courses on
Vectors
Show steps
-
Revisit vector space models, vector representations, and cosine similarity.
-
Practice converting text and numeric data into vector representations.
-
Explore database structures and query languages optimized for vector data.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Peer Mentoring Program
Show steps
Facilitates knowledge exchange and support among learners, fostering a collaborative learning environment and strengthening understanding of course concepts.
Show steps
-
Join or form a peer mentoring group with other students in the course.
-
Take turns leading discussions, sharing knowledge, and providing feedback.
-
Collaborate on assignments or projects to enhance learning outcomes.
Embedding Creation and Integration Drills
Show steps
Reinforces practical skills in embedding creation and integration, building proficiency in the hands-on implementation of AI engineering concepts.
Browse courses on
Embeddings
Show steps
-
Complete coding exercises on different techniques for creating embeddings.
-
Integrate embeddings into Supabase and perform vector operations.
-
Write queries to retrieve and manipulate embeddings.
OpenAI Conversational AI Tutorial
Show steps
Provides structured guidance on building conversational AI systems with OpenAI, enhancing skills in this rapidly growing area of AI engineering.
Browse courses on
Conversational AI
Show steps
-
Follow the official OpenAI tutorial on building conversational AI models.
-
Experiment with different training data and model parameters.
-
Evaluate the performance of your models and identify areas for improvement.
AI Engineering Workshop
Show steps
Offers an immersive learning experience through hands-on projects and expert guidance, fostering a deeper understanding of the latest AI engineering techniques.
Browse courses on
Industry Trends
Show steps
-
Attend a workshop organized by industry leaders or academic institutions.
-
Participate in group projects and discussions.
-
Network with experts and professionals in the field.
Semantic and Similarity Search Case Study
Show steps
Demonstrates the practical application of semantic and similarity searches in AI engineering projects, fostering a deeper understanding of real-world use cases.
Browse courses on
Semantic Search
Show steps
-
Identify a domain or industry relevant to your interests.
-
Create a dataset of text or numerical data.
-
Build a vector database and generate embeddings for the data.
-
Design and implement semantic and similarity search workflows.
-
Analyze the results and discuss the potential applications.
AI Conversational Chatbot Project
Show steps
Provides a culminating learning experience by applying course concepts to the development of a real-world AI application, fostering creativity, problem-solving, and comprehensive understanding.
Show steps
-
Gather requirements and design the chatbot's functionality.
-
Collect and preprocess a training dataset.
-
Train and evaluate a machine learning model for natural language understanding.
-
Integrate the model with a chatbot platform and create a user interface.
-
Test and iterate on the chatbot's performance.
Review Linear Algebra Fundamentals
Show steps
Strengthens foundational knowledge in linear algebra, ensuring a solid base for understanding vector spaces and other advanced AI engineering concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go through online resources or textbooks to refresh basic linear algebra topics.
- Practice solving problems related to vector operations, matrix transformations, and linear equations.
Course Materials Review
Show steps
Enhances retention and understanding by reinforcing course concepts through the review and organization of learning materials.
Show steps
Show steps
Show steps
- Organize lecture notes, readings, assignments, and other course materials.
- Create summaries, mind maps, or concept trees to reinforce key ideas.
- Identify areas where further clarification or review is needed.
Review Vector Database Concepts
Show steps
Refreshes foundational skills in vector database fundamentals, ensuring a strong conceptual understanding for advanced topics in AI Engineering.
Browse courses on
Vectors
Show steps
Show steps
Show steps
- Revisit vector space models, vector representations, and cosine similarity.
- Practice converting text and numeric data into vector representations.
- Explore database structures and query languages optimized for vector data.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Peer Mentoring Program
Show steps
Facilitates knowledge exchange and support among learners, fostering a collaborative learning environment and strengthening understanding of course concepts.
Show steps
Show steps
Show steps
- Join or form a peer mentoring group with other students in the course.
- Take turns leading discussions, sharing knowledge, and providing feedback.
- Collaborate on assignments or projects to enhance learning outcomes.
Embedding Creation and Integration Drills
Show steps
Reinforces practical skills in embedding creation and integration, building proficiency in the hands-on implementation of AI engineering concepts.
Browse courses on
Embeddings
Show steps
Show steps
Show steps
- Complete coding exercises on different techniques for creating embeddings.
- Integrate embeddings into Supabase and perform vector operations.
- Write queries to retrieve and manipulate embeddings.
OpenAI Conversational AI Tutorial
Show steps
Provides structured guidance on building conversational AI systems with OpenAI, enhancing skills in this rapidly growing area of AI engineering.
Browse courses on
Conversational AI
Show steps
Show steps
Show steps
- Follow the official OpenAI tutorial on building conversational AI models.
- Experiment with different training data and model parameters.
- Evaluate the performance of your models and identify areas for improvement.
AI Engineering Workshop
Show steps
Offers an immersive learning experience through hands-on projects and expert guidance, fostering a deeper understanding of the latest AI engineering techniques.
Browse courses on
Industry Trends
Show steps
Show steps
Show steps
- Attend a workshop organized by industry leaders or academic institutions.
- Participate in group projects and discussions.
- Network with experts and professionals in the field.
Semantic and Similarity Search Case Study
Show steps
Demonstrates the practical application of semantic and similarity searches in AI engineering projects, fostering a deeper understanding of real-world use cases.
Browse courses on
Semantic Search
Show steps
Show steps
Show steps
- Identify a domain or industry relevant to your interests.
- Create a dataset of text or numerical data.
- Build a vector database and generate embeddings for the data.
- Design and implement semantic and similarity search workflows.
- Analyze the results and discuss the potential applications.
AI Conversational Chatbot Project
Show steps
Provides a culminating learning experience by applying course concepts to the development of a real-world AI application, fostering creativity, problem-solving, and comprehensive understanding.
Show steps
Show steps
Show steps
- Gather requirements and design the chatbot's functionality.
- Collect and preprocess a training dataset.
- Train and evaluate a machine learning model for natural language understanding.
- Integrate the model with a chatbot platform and create a user interface.
- Test and iterate on the chatbot's performance.
Career center
Learners who complete Learn Embeddings and Vector Databases will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers are at the forefront of AI engineering, bringing artificial intelligence concepts and techniques to life. This course provides a foundation in creating and using embeddings, key components of AI models used for tasks like semantic search, text classification, and similarity search. Whether you're interested in building AI chatbots, developing computer vision applications, or exploring other applications of AI, this course can help you build the skills needed in these roles.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and maintaining machine learning models. This course provides a solid understanding of embeddings, which are foundational components of many machine learning algorithms. By learning how to create and manage embeddings in vector databases, you'll gain skills that are highly sought after in the field of machine learning engineering.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and derive insights. This course provides a practical understanding of embeddings, which can be used for tasks such as natural language processing, image recognition, and anomaly detection. Whether you're interested in building models for fraud detection, customer segmentation, or predictive analytics, this course will equip you with valuable skills.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides knowledge of embeddings and vector databases that can be applied in a variety of software engineering roles, particularly those involving natural language processing, machine learning, or data science. Whether you're working on search engines, chatbots, or recommendation systems, this course can enhance your ability to build robust and efficient software solutions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course provides a foundation in embeddings, which can be used for tasks such as clustering, anomaly detection, and time series analysis. Whether you're working with large datasets in finance, healthcare, or retail, this course will equip you with techniques to extract valuable insights and make informed decisions.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course provides a deep dive into vector databases, a specialized type of database designed for storing and querying embeddings. By learning how to set up, configure, and optimize vector databases, you'll gain skills that are in high demand in various industries, including technology, healthcare, and finance.
Chatbot Developer
Chatbot Developer
Chatbot Developers design, build, and maintain chatbots. This course provides practical experience in creating AI chatbots using embeddings and vector databases. You'll learn techniques for training chatbots, handling natural language input, and generating personalized responses. Whether you're working on customer service chatbots, virtual assistants, or conversational agents, this course will help you build engaging and intelligent chatbot applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers specialize in developing and applying techniques for processing and understanding human language. This course provides a foundation in embeddings, which are essential for tasks such as text classification, sentiment analysis, and machine translation. Whether you're working on natural language search engines, chatbots, or other language-based applications, this course will equip you with the skills needed to build sophisticated and effective NLP solutions.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop algorithms for analyzing and interpreting visual data. This course provides an understanding of embeddings, which can be used for tasks such as object recognition, image classification, and facial recognition. Whether you're working on self-driving cars, medical imaging systems, or surveillance applications, this course will enhance your ability to build computer vision systems that can extract meaningful insights from images and videos.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields, including AI and machine learning. This course provides a solid foundation in embeddings, which are increasingly used in research projects involving natural language processing, computer vision, and knowledge graphs. Whether you're interested in exploring new applications of embeddings or contributing to the advancement of AI, this course will equip you with the theoretical and practical knowledge necessary for success.
Product Manager
Product Manager
Product Managers are responsible for defining, developing, and launching new products. This course provides insights into the use of embeddings in product development, particularly for products involving AI, search, or personalization. Whether you're working on building chatbots, recommendation engines, or other AI-powered products, this course can help you understand how to leverage embeddings to enhance product functionality and user experience.
Business Analyst
Business Analyst
Business Analysts use data and analytics to solve business problems and improve decision-making. This course provides a practical understanding of embeddings, which can be used for tasks such as customer segmentation, market analysis, and fraud detection. Whether you're working on optimizing marketing campaigns, improving customer service, or identifying new business opportunities, this course will equip you with techniques to leverage embeddings for data-driven insights and decision support.
Technical Writer
Technical Writer
Technical Writers create documentation and other written materials to explain technical concepts and products. This course provides knowledge of embeddings and vector databases, enabling you to effectively communicate these concepts to technical and non-technical audiences. Whether you're writing documentation for AI systems, software applications, or user manuals, this course will enhance your ability to convey complex technical information in a clear and engaging manner.
UX Designer
UX Designer
UX Designers focus on improving the user experience of products and services. This course provides insights into the use of embeddings in UX design, particularly for applications involving natural language processing, search, and personalization. Whether you're working on chatbot interfaces, e-commerce websites, or other user-facing applications, this course can help you understand how to leverage embeddings to enhance user interactions and create more intuitive and engaging experiences.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze and predict financial data. This course provides a foundation in embeddings, which can be used for tasks such as stock price prediction, risk assessment, and fraud detection. Whether you're working on building trading algorithms, developing risk management systems, or performing financial analysis, this course will equip you with techniques to leverage embeddings for data-driven decision-making in the financial industry.
For more career information including salaries, visit:
OpenCourser.com/course/p5i2tv/learn
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Learn Embeddings and Vector Databases.
Provides a comprehensive overview of machine learning concepts and techniques, including a detailed exploration of embedding techniques. It offers practical guidance on using popular machine learning libraries like Scikit-Learn, Keras, and TensorFlow.
Focuses on the practical aspects of deep learning and provides a hands-on approach to understanding and implementing embedding models using Python. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a good overview of deep learning techniques for natural language processing and speech recognition, serving as a useful reference for those interested in these topics
This textbook on deep learning good resource for those who want to explore the topic further, providing good context for those less familiar with this area
Provides a comprehensive introduction to natural language processing techniques, including embedding methods for text data. It covers topics such as text classification, text summarization, and machine translation.
Save
Provides a collection of recipes for using TensorFlow 2.0, serving as a useful reference for those working with this framework
Offers a gentle introduction to machine learning concepts and techniques, providing a foundation for understanding embedding techniques. It uses plain language and intuitive explanations to make machine learning accessible to beginners.
Provides a theoretical foundation for machine learning and predictive analytics, including an overview of embedding techniques. It covers the mathematical principles behind machine learning algorithms and their applications in various domains.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/p5i2tv/learn
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Level
Intermediate
Via
Coursera
Institution
Scrimba
Instructor
Guil Hernandez
Language
English
This course belongs to a
collection
called
AI Engineering. It's comprised of six courses:
- Learn Embeddings and Vector Databases (viewing)
- Open-source AI Models
- Intro to AI Engineering
- Learn OpenAI's Assistant API
- Build AI Apps with LangChain.js
- Learn AI Agents
Good to know
Develops an understanding of embeddings and their role in AI interpretation
Covers practical skills, such as setting up environment variables, creating embeddings, and integrating them into vector databases
Provides hands-on exercises and challenges to test learners' abilities
Includes a project that allows learners to apply their knowledge to create an AI chatbot
Teaches skills that are relevant to industry and academia
Requires students to come in with some background knowledge in AI and programming
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