Master Vector Database with Python for AI & LLM Use Cases

Description

In this comprehensive course on Vector Databases, you will delve into the exciting world of cutting-edge technologies that are transforming the field of artificial intelligence (AI), particularly in generative AI. With a focus on Future-Proofing Generative AI, this course will equip you with the knowledge and skills to harness the power of Vector Databases for advanced applications, including Language Model Models (LLM), Generative Pretrained Transformers (GPT) like ChatGPT, and Artificial General Intelligence (AGI) development.

Starting from the foundations, you will learn the fundamentals of Vector Databases and their role in revolutionizing AI workflows. Through practical examples and hands-on coding exercises, you will explore techniques such as vector data indexing, storage, retrieval, and conditionality reduction. You will also gain proficiency in integrating Pinecone Vector Data Base with other tools like LangChain, OpenAI API using Python to implement real-world use cases and unleash the full potential of Vector Databases.

Throughout the course, we will uncover the limitless possibilities of Vector Databases in generative AI. You will discover how these databases enable content generation, recommendation systems, language translation, and more. Additionally, we will discuss performance optimization, scalability considerations, and best practices for efficient implementation.

Led by an expert instructor with a PhD in computational nano science and extensive experience as a data scientist at leading companies, you will benefit from their deep knowledge, practical insights, and passion for teaching AI and Machine Learning (ML). Join us now to embark on this transformative learning journey and position yourself at the forefront of Future-Proofing Generative AI with Vector Databases. Enroll today and unlock a world of AI innovation!

What You Will Learn!

• Pinecone Vector Database, LangChain, Transformer Models for vector embedding, Generative AI, Open AI API Usage, Hugging Face Models
• Master the essential techniques for vector data embedding, indexing, and retrieval.
• A Practical Code Along with Semantic Search Use Case in Detail with Named Entity Recognition
• Developing an AI Chat Bot for Cognitive Search on Private Data Using LangChain
• Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).
• Explore various vector database technologies, including Pinecone, and learn how to set up and configure a vector database environment.
• Learn how vector databases enhance AI workflows by enabling efficient similarity search and nearest neighbor retrieval.
• Gain practical knowledge on integrating vector databases with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn.
• Implement code along exercises to build and optimize vector indexing systems for real-world applications.
• Explore practical use cases of vector databases in AI, generative AI, and LLM, such as recommendation systems, content generation, and language translation.
• Understand how vector databases can handle large-scale datasets and support real-time inference.
• Gain insights into performance optimization techniques, scalability considerations, and best practices for vector database implementation.

Who Should Attend!

• Data engineers, database administrators and data professionals curious about the emerging field of vector databases.
• Data scientists and analysts interested in exploring advanced AI techniques.
• Machine learning engineers seeking to enhance their knowledge of vector databases and their applications.
• AI researchers and practitioners looking to leverage vector databases for generative AI models.
• Software developers interested in integrating vector databases into their applications.
• Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area.
• Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML.

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