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Introduction to Vector Databases with Pinecone top-rated

with Elitsa Kaloyanova
4.8/5
(80)

Gaining insights from data is crucial for businesses. With emerging AI technologies, the importance of vectorization and vector databases is set to increase significantly. In this Vector Databases with Pinecone course, you’ll have the opportunity to explore the Pinecone database—a leading vector solution—and learn to implement a vector database for semantic search using real data.

2 hours of content 817 students
Start for free

What you get:

  • 2 hours of content
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Introduction to Vector Databases with Pinecone top-rated

A course by Elitsa Kaloyanova
Start for free

What you get:

  • 2 hours of content
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement
Start for free

What you get:

  • 2 hours of content
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Vector databases
  • Pinecone
  • Embedding algorithms
  • Custom vector databases
  • Semantic search
  • Vectorize AI

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

In this Introduction to Vector Databases with Pinecone course, you’ll explore cutting-edge vector databases, focusing on embeddings and their vectorization roles alongside various embedding algorithms. Utilize the Pinecone AI (a user-friendly managed vector database) and Python to build custom databases and develop a semantic search algorithm with our 365 data. Examine different data vectorization methods at various aggregation levels. Additionally, I’ll show you how to upsert sections of the Hugging Face FineWeb dataset, providing a practical Hugging Face tutorial on working with real-world data.

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Introduction to the course

1.1 Introduction to the course

3 min

Database comparison: SQL, NoSQL, and Vector

1.2 Database comparison: SQL, NoSQL, and Vector

5 min

Understanding vector databases

1.3 Understanding vector databases

4 min

Introduction to vector space

2.1 Introduction to vector space

5 min

Distance metrics in vector space

2.2 Distance metrics in vector space

6 min

Vector embeddings walkthrough

2.3 Vector embeddings walkthrough

4 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise
categorize exercise

Curriculum

  • 1. Introduction to Vector Databases
    3 Lessons 12 Min

    This section provides a foundational understanding of different types of databases, focusing on SQL, NoSQL, and Vector databases. You'll learn what is a vector database, what makes them unique and their advantages over traditional databases.

    Introduction to the course
    3 min
    Database comparison: SQL, NoSQL, and Vector
    5 min
    Understanding vector databases
    4 min
  • 2. Basics of Vector Space and High-Dimensional Data
    3 Lessons 15 Min

    Dive into the core concepts of vector spaces and high-dimensional data. This section introduces vector spaces, covers distance metrics in these spaces, and explains vector embeddings—setting the stage for working with vector databases.

    Introduction to vector space
    5 min
    Distance metrics in vector space
    6 min
    Vector embeddings walkthrough
    4 min
  • 3. Introduction to The Pinecone Vector Database
    8 Lessons 29 Min

    This section offers a comprehensive overview of vector database engines, focusing on Pinecone. You’ll learn about Pinecone’s features, register and create an index, and connect to Pinecone using Python. Practical lessons include creating and deleting indices, upserting data, and embedding algorithms with data from various sources, including CSV files.

    Vector databases, comparison
    7 min
    Pinecone registration, walkthrough and creating an Index
    4 min
    Connecting to Pinecone using Python
    3 min
    Creating a new index Read now
    1 min
    Creating and deleting a Pinecone index using Python
    3 min
    Upserting data to a pinecone vector database
    4 min
    Getting to know the fine web data set and loading it to Jupyter
    2 min
    Upserting data from a text file and using an embedding algorithm
    5 min
  • 4. Case Study Semantic Search with Pinecone and Custom Data
    18 Lessons 62 Min

    Through a detailed case study, this section of our Vector Databases with Pinecone course explores semantic search using Pinecone. You'll be introduced to the case study's dataset, learn about data loading and preprocessing, and compare different embedding algorithms. The practical application includes embedding data, upserting it to Pinecone, performing similarity searches, and updating the vector database. Advanced topics cover using different embedding algorithms and exploring vector databases for recommendation systems, semantic image search, and biomedical research.

    Introduction to semantic search
    4 min
    Introduction to the case study – smart search for data science courses
    5 min
    Getting to know the data for the case study
    2 min
    Data loading and preprocessing
    4 min
    Pinecone Python APIs and connecting to the Pinecone server
    4 min
    Embedding algorithms
    4 min
    Embedding the data and upserting the files to Pinecone
    3 min
    Similarity search and querying the data
    4 min
    How to update and change your vector database
    4 min
    Data preprocessing and embedding for courses with section data
    4 min
    Courses and Sections Together Assignment Read now
    1 min
    Upserting the new updated files to Pinecone
    2 min
    Similarity search and querying courses and sections data
    4 min
    Weighted semantic search assignment Read now
    1 min
    Using the BERT embedding algorithm
    4 min
    Vector database for recommendation engines
    4 min
    Vector database for semantic image search
    4 min
    Vector database for biomedical research
    4 min

Topics

AIPythonJupyterHuggingfaceVector DatabasesEmbedding algorithmsSemantic searchPinecone

Tools & Technologies

python

Course Requirements

  • Python - an intermediate level of Python programming is desired, as we'll expect you to have an environment running (we use Jupyter Notebook) and you should be familiar with reading csv files, list comprehensions and iterators.
  • Databases - an understanding of relational databases is desired, but not required for this course.

Who Should Take This Course?

Level of difficulty: Advanced

  • Data Engineers looking to upskill with vector solutions
  • Data engineers interested in new data solutions
  • Data Scientist interested in data storage solutions
  • ML engineers who want to upskill with AI

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Elitsa Kaloyanova

Elitsa Kaloyanova

Senior Data Scientist at

7 Courses

2931 Reviews

47749 Students

Elitsa Kaloyanova is a Computational Biologist, with significant expertise in the fields of algorithms, data structures, phylogenetics, and population genetics. She has a solid academic background in Bioinformatics with publications on constructing Phylogenetic Networks and Trees. In 2021, she led 365’s effort to create practice exams and course exams for each course included in the program. Elitsa was able to successfully coordinate with several types of stakeholders and performed superior Quality Assurance.

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What Our Learners Say

Abulgasim Abdulrahman
Abulgasim Abdulrahman
18.11.2024
awesome
Paitoon Prownets
Paitoon Prownets
18.11.2024
excelence

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