This course focuses on utilizing LangChain, a framework designed for building and deploying AI-powered applications. Participants will learn how to leverage LangChain’s capabilities to create sophisticated applications that integrate natural language processing, machine learning, and AI-driven functionalities.
Prerequisites
Basic understanding of programming (preferably Python)
Familiarity with AI and machine learning concepts
Knowledge of natural language processing (NLP) is helpful but not required
What will you gain after this course
Understand the LangChain framework and its components
Develop AI-powered applications using LangChain’s tools and libraries
Integrate natural language processing and machine learning into applications
Create and deploy applications that leverage LangChain’s capabilities
Evaluate and optimize the performance of AI-powered applications
Jobs you can get with LangChain: Building AI-Powered Applications Training
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Who is this certification for?
Software developers and engineers
Data scientists interested in application development
AI and machine learning practitioners
Product managers and designers working with AI technologies
Students or professionals seeking to build skills in AI-powered application development
Small Groups
With small groups of students, our instructors can work closely with each student.
Flexible Class Schedules
Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.
Experienced Instructors
Our instructors follow a modified are personalized approach to engage students during class
Hi-Tech Lab Facilities
Our students can access our lab facilities anytime for practical experience during and after studies.
Syllabus
Introduction to LangChain
Overview of LangChain and its capabilities
Key concepts and components of the LangChain framework
Use cases and applications of LangChain
Setting Up LangChain
Installing and configuring LangChain
Overview of LangChain’s development environment and tools
Creating a simple LangChain project
Core Components of LangChain
Understanding LangChain’s architecture and modules
Working with LangChain’s libraries for NLP and AI
Building blocks for AI-powered applications
Developing AI-Powered Applications with LangChain
Integrating natural language processing (NLP) features
Implementing machine learning models within LangChain
Creating interactive and intelligent user interfaces
Advanced Features and Customization
Leveraging LangChain’s advanced functionalities and APIs
Customizing and extending LangChain for specific needs
Best practices for optimizing performance and scalability
Testing and Deploying LangChain Applications
Testing strategies for AI-powered applications
Deploying LangChain applications to various platforms
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