Introduction to Vector Databases with Pinecone top-rated
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.
$99.00
Lifetime access
What you get:
- 2 hours of content
- 16 Downloadable resources
- Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Introduction to Vector Databases with Pinecone top-rated
$99.00
Lifetime access
What you get:
- 2 hours of content
- 16 Downloadable resources
- Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
What you get:
- 2 hours of content
- 16 Downloadable resources
- Interactive exercises
- 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.
Learn for Free
1.1 Introduction to the course
3 min
1.2 Database comparison: SQL, NoSQL, and Vector
5 min
1.3 Understanding vector databases
4 min
2.1 Introduction to vector space
5 min
2.2 Distance metrics in vector space
6 min
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 1. Introduction to Vector Databases3 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 course3 minDatabase comparison: SQL, NoSQL, and Vector5 minUnderstanding vector databases4 min - 2. Basics of Vector Space and High-Dimensional Data3 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 space5 minDistance metrics in vector space6 minVector embeddings walkthrough4 min - 3. Introduction to The Pinecone Vector Database8 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, comparison7 minPinecone registration, walkthrough and creating an Index4 minConnecting to Pinecone using Python3 minCreating a new index Read now1 minCreating and deleting a Pinecone index using Python3 minUpserting data to a pinecone vector database4 minGetting to know the fine web data set and loading it to Jupyter2 minUpserting data from a text file and using an embedding algorithm5 min - 4. Case Study Semantic Search with Pinecone and Custom Data18 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 search4 minIntroduction to the case study – smart search for data science courses5 minGetting to know the data for the case study2 minData loading and preprocessing4 minPinecone Python APIs and connecting to the Pinecone server4 minEmbedding algorithms4 minEmbedding the data and upserting the files to Pinecone3 minSimilarity search and querying the data4 minHow to update and change your vector database4 minData preprocessing and embedding for courses with section data4 minCourses and Sections Together Assignment Read now1 minUpserting the new updated files to Pinecone2 minSimilarity search and querying courses and sections data4 minWeighted semantic search assignment Read now1 minUsing the BERT embedding algorithm4 minVector database for recommendation engines4 minVector database for semantic image search4 minVector database for biomedical research4 min
Topics
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.
Meet Your Instructor
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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