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This course on developing RAG Applications using Open This course covers all the basics aspects of LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will make your RAG Applications more efficient.
Read more
This course on developing RAG Applications using Open This course covers all the basics aspects of LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will make your RAG Applications more efficient.
Read more
This course on developing RAG Applications using Open This course covers all the basics aspects of LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will make your RAG Applications more efficient.
List of Projects Included:
SQL RAG: Convert Natural Language to SQL Statements and apply on your MySQL Database to extract desired Results.
RAG with Conversational Memory: Create a simple RAG Application with Conversational Memory.
CV Analysis: Load a CV document and extract JSON based key information from the document.
Conversational HR Chatbot: Create a comprehensive HR Chatbot that is able to respond with answers from a HR Policy and Procedure database loaded into a Vector DB, and retain conversational memory like ChatGPT. Build UI using Streamlit.
Structured Data Analysis: Load structured data into a Pandas Dataframe and use a Few-Shot ReAct Agent to perform complex analytics.
Invoice Data Extractor: Upload multiple Invoices and extract key information into a CSV format. Build UI using Streamlit.
For each project, you will learn:
- The Business Problem
- What LLM and LangChain Components are used
- Analyze outcomes
- What are other similar use cases you can solve with a similar approach.
Capstone Project:
You will also have the opportunity to complete a Capstone Project at the end of the course. This will help you strengthen your ability to independently develop a LangChain RAG Project from scratch.
Register for this course and see more details by visiting:
OpenCourser.com/course/kbrxwl/gen
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Develops complex applications using RAG (Retrieval-Augmented Generation) frameworks, including Agents, Chains, Retrievers, and Loaders
Strengthens understanding of Language Embeddings and Vector Databases, enabling efficient semantic search and similarity analyses
Provides hands-on coding experience with multiple Prompt Engineering techniques to enhance RAG application efficiency
Offers comprehensive study of core LLM and framework components, such as Agents, Tools, Chains, and Parsers
Features practical projects covering various use cases, including SQL RAG, Conversational Memory, CV Analysis, and Structured Data Analysis
Provides opportunities for learners to build comprehensive RAG applications, including UI using Streamlit, based on real-world business needs
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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 Gen AI - RAG Application Development using LangChain with these
activities:
Review your statistics and probability knowledge
Show steps
Ensure your foundational knowledge in statistics and probability is strong before delving into data analysis.
Browse courses on
Statistics
Show steps
-
Review your lecture notes from previous courses or textbooks.
-
Go through online resources or tutorials to refresh your memory.
-
Attempt practice problems to test your understanding.
Read "Deep Learning with Python" by Chollet, Franco
Show steps
Develop a strong foundation in deep learning concepts to enhance your understanding of RAG Applications.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read Chapter 1-5 to gain an overview of deep learning.
-
Complete the exercises in Chapter 2-4 to apply your knowledge.
Read 'Data Analytics Made Accessible'
Show steps
Gain a comprehensive understanding of data analytics concepts and techniques through a well-written and accessible book.
View
Big Data: Second Edition
on Amazon
Show steps
-
Purchase or borrow a copy of the book.
-
Set aside dedicated time for reading.
-
Take notes and highlight important concepts.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Create a comprehensive study guide
Show steps
Integrate your notes, assignments, and course materials into a structured and concise study guide for easy revision.
Show steps
-
Review all course materials and identify key concepts.
-
Summarize the main ideas in a concise and clear manner.
-
Organize the study guide into logical sections and topics.
Review the basic mathematical operations
Show steps
Ensure that you are adept in basic mathematical operations prior to entering the course.
Browse courses on
Mathematical Operations
Show steps
-
Solve addition problems without the use of a calculator.
-
Solve subtraction problems without the use of a calculator.
Complete online tutorials on data visualization
Show steps
Comprehend the basics of data visualization techniques to effectively convey information from data.
Show steps
-
Identify reputable online platforms offering data visualization tutorials.
-
Choose tutorials that align with your learning objectives.
-
Follow the instructions and complete the exercises.
Follow the Language Chain (LangChain) tutorial series
Show steps
Gain practical experience in building RAG Applications by following step-by-step tutorials.
Show steps
-
Set up the LangChain environment by following the installation guide.
-
Complete the introductory tutorial on building a simple RAG Application.
Solve practice problems on data analysis
Show steps
Reinforce your understanding of data analysis concepts through consistent practice and problem-solving.
Browse courses on
Data Analysis
Show steps
-
Find practice problems from textbooks, online resources, or instructors.
-
Allocate dedicated time for solving these problems.
Practice developing RAG Applications using the provided code snippets
Show steps
Practice developing RAG Applications using the provided code snippets will help you solidify your understanding of the concepts taught in the course and reinforce your ability to apply them in real-world scenarios.
Show steps
-
Find a code snippet that demonstrates a RAG Application.
-
Understand the purpose of the code snippet and the techniques it uses.
-
Modify the code snippet to customize it for your own use case.
-
Run the code snippet and observe the results.
-
Repeat steps 1-4 with different code snippets.
Take part in a workshop on statistical modeling
Show steps
Learn the fundamental principles and methods of statistical modeling in a structured and practical setting.
Browse courses on
Statistical Modeling
Show steps
-
Attend the workshop sessions regularly.
-
Participate in hands-on exercises and discussions.
-
Complete any assignments or projects related to the workshop.
Design a presentation on RAG Applications
Show steps
Develop your communication skills by creating a presentation that effectively showcases your understanding of RAG Applications.
Show steps
-
Plan the structure and key points of your presentation.
-
Gather relevant examples and case studies to illustrate your ideas.
-
Practice delivering your presentation with clear and engaging language.
Solve practice exercises on RAG Application development
Show steps
Reinforce your understanding of RAG principles and techniques through repetitive exercises.
Show steps
-
Attempt the LangChain practice exercises on natural language understanding.
-
Solve coding challenges on building RAG Applications for specific tasks.
Create a collection of data analysis tools and resources
Show steps
Organize and assemble valuable data analysis tools and resources for easy access and future reference.
Browse courses on
Data Analysis
Show steps
-
Identify and research different data analysis tools and resources.
-
Categorize and organize the resources based on their functionality.
-
Create a central repository or documentation to store the collection.
Develop a RAG Application for a real-world problem
Show steps
Apply your skills to address a specific use case and demonstrate your ability to develop practical RAG Applications.
Show steps
-
Identify a problem that can be solved using a RAG Application.
-
Design and implement a LangChain-based RAG solution.
-
Evaluate the performance of your application and make necessary improvements.
Offer assistance to fellow students in the course
Show steps
Enhance your understanding by explaining concepts to others and providing support within the learning community.
Show steps
-
Identify opportunities to assist classmates through online forums or study groups.
-
Prepare clear and concise explanations.
-
Provide constructive feedback and encouragement.
Develop a data analysis project and present your findings
Show steps
Apply your knowledge and skills to a practical project, demonstrating your ability to analyze data and communicate insights.
Browse courses on
Data Analysis
Show steps
-
Define the problem or question you want to address with the project.
-
Gather and prepare the necessary data.
-
Conduct the data analysis using appropriate techniques.
-
Interpret the results and draw meaningful conclusions.
-
Create a presentation to showcase your findings.
Review your statistics and probability knowledge
Show steps
Ensure your foundational knowledge in statistics and probability is strong before delving into data analysis.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your lecture notes from previous courses or textbooks.
- Go through online resources or tutorials to refresh your memory.
- Attempt practice problems to test your understanding.
Read "Deep Learning with Python" by Chollet, Franco
Show steps
Develop a strong foundation in deep learning concepts to enhance your understanding of RAG Applications.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1-5 to gain an overview of deep learning.
- Complete the exercises in Chapter 2-4 to apply your knowledge.
Read 'Data Analytics Made Accessible'
Show steps
Gain a comprehensive understanding of data analytics concepts and techniques through a well-written and accessible book.
View
Big Data: Second Edition
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Set aside dedicated time for reading.
- Take notes and highlight important concepts.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Create a comprehensive study guide
Show steps
Integrate your notes, assignments, and course materials into a structured and concise study guide for easy revision.
Show steps
Show steps
Show steps
- Review all course materials and identify key concepts.
- Summarize the main ideas in a concise and clear manner.
- Organize the study guide into logical sections and topics.
Review the basic mathematical operations
Show steps
Ensure that you are adept in basic mathematical operations prior to entering the course.
Browse courses on
Mathematical Operations
Show steps
Show steps
Show steps
- Solve addition problems without the use of a calculator.
- Solve subtraction problems without the use of a calculator.
Complete online tutorials on data visualization
Show steps
Comprehend the basics of data visualization techniques to effectively convey information from data.
Show steps
Show steps
Show steps
- Identify reputable online platforms offering data visualization tutorials.
- Choose tutorials that align with your learning objectives.
- Follow the instructions and complete the exercises.
Follow the Language Chain (LangChain) tutorial series
Show steps
Gain practical experience in building RAG Applications by following step-by-step tutorials.
Show steps
Show steps
Show steps
- Set up the LangChain environment by following the installation guide.
- Complete the introductory tutorial on building a simple RAG Application.
Solve practice problems on data analysis
Show steps
Reinforce your understanding of data analysis concepts through consistent practice and problem-solving.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find practice problems from textbooks, online resources, or instructors.
- Allocate dedicated time for solving these problems.
Practice developing RAG Applications using the provided code snippets
Show steps
Practice developing RAG Applications using the provided code snippets will help you solidify your understanding of the concepts taught in the course and reinforce your ability to apply them in real-world scenarios.
Show steps
Show steps
Show steps
- Find a code snippet that demonstrates a RAG Application.
- Understand the purpose of the code snippet and the techniques it uses.
- Modify the code snippet to customize it for your own use case.
- Run the code snippet and observe the results.
- Repeat steps 1-4 with different code snippets.
Take part in a workshop on statistical modeling
Show steps
Learn the fundamental principles and methods of statistical modeling in a structured and practical setting.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Attend the workshop sessions regularly.
- Participate in hands-on exercises and discussions.
- Complete any assignments or projects related to the workshop.
Design a presentation on RAG Applications
Show steps
Develop your communication skills by creating a presentation that effectively showcases your understanding of RAG Applications.
Show steps
Show steps
Show steps
- Plan the structure and key points of your presentation.
- Gather relevant examples and case studies to illustrate your ideas.
- Practice delivering your presentation with clear and engaging language.
Solve practice exercises on RAG Application development
Show steps
Reinforce your understanding of RAG principles and techniques through repetitive exercises.
Show steps
Show steps
Show steps
- Attempt the LangChain practice exercises on natural language understanding.
- Solve coding challenges on building RAG Applications for specific tasks.
Create a collection of data analysis tools and resources
Show steps
Organize and assemble valuable data analysis tools and resources for easy access and future reference.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify and research different data analysis tools and resources.
- Categorize and organize the resources based on their functionality.
- Create a central repository or documentation to store the collection.
Develop a RAG Application for a real-world problem
Show steps
Apply your skills to address a specific use case and demonstrate your ability to develop practical RAG Applications.
Show steps
Show steps
Show steps
- Identify a problem that can be solved using a RAG Application.
- Design and implement a LangChain-based RAG solution.
- Evaluate the performance of your application and make necessary improvements.
Offer assistance to fellow students in the course
Show steps
Enhance your understanding by explaining concepts to others and providing support within the learning community.
Show steps
Show steps
Show steps
- Identify opportunities to assist classmates through online forums or study groups.
- Prepare clear and concise explanations.
- Provide constructive feedback and encouragement.
Develop a data analysis project and present your findings
Show steps
Apply your knowledge and skills to a practical project, demonstrating your ability to analyze data and communicate insights.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the problem or question you want to address with the project.
- Gather and prepare the necessary data.
- Conduct the data analysis using appropriate techniques.
- Interpret the results and draw meaningful conclusions.
- Create a presentation to showcase your findings.
Career center
Learners who complete Gen AI - RAG Application Development using LangChain will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course on developing RAG Applications using LangChain could be useful for Data Analysts who want to learn how to use AI to automate data analysis tasks. The course covers a variety of topics that are relevant to Data Analysts, such as natural language processing, machine learning, and data visualization.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course on developing RAG Applications using LangChain could be useful for Machine Learning Engineers who want to learn how to use AI to build more efficient and accurate machine learning models. The course covers a variety of topics that are relevant to Machine Learning Engineers, such as natural language processing, machine learning algorithms, and model evaluation.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course on developing RAG Applications using LangChain could be useful for Software Engineers who want to learn how to use AI to build more intelligent and user-friendly software applications. The course covers a variety of topics that are relevant to Software Engineers, such as natural language processing, machine learning, and user interface design.
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve business problems. This course on developing RAG Applications using LangChain could be useful for Data Scientists who want to learn how to use AI to automate data science tasks. The course covers a variety of topics that are relevant to Data Scientists, such as natural language processing, machine learning, and data visualization.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and deploying natural language processing models. This course on developing RAG Applications using LangChain could be useful for Natural Language Processing Engineers who want to learn how to use AI to build more efficient and accurate natural language processing models. The course covers a variety of topics that are relevant to Natural Language Processing Engineers, such as natural language processing algorithms, machine learning, and model evaluation.
AI Researcher
AI Researcher
AI Researchers are responsible for conducting research in the field of artificial intelligence. This course on developing RAG Applications using LangChain could be useful for AI Researchers who want to learn how to use AI to solve complex problems. The course covers a variety of topics that are relevant to AI Researchers, such as natural language processing, machine learning, and computer vision.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course on developing RAG Applications using LangChain could be useful for Product Managers who want to learn how to use AI to build more innovative and user-friendly products. The course covers a variety of topics that are relevant to Product Managers, such as natural language processing, machine learning, and user research.
UX Designer
UX Designer
UX Designers are responsible for designing the user experience of software applications. This course on developing RAG Applications using LangChain could be useful for UX Designers who want to learn how to use AI to build more intuitive and user-friendly software applications. The course covers a variety of topics that are relevant to UX Designers, such as natural language processing, machine learning, and user research.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course on developing RAG Applications using LangChain could be useful for Business Analysts who want to learn how to use AI to automate business analysis tasks. The course covers a variety of topics that are relevant to Business Analysts, such as natural language processing, machine learning, and data visualization.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to businesses on a variety of topics. This course on developing RAG Applications using LangChain could be useful for Consultants who want to learn how to use AI to provide more innovative and effective solutions to their clients. The course covers a variety of topics that are relevant to Consultants, such as natural language processing, machine learning, and data visualization.
Technical Writer
Technical Writer
Technical Writers are responsible for creating and maintaining technical documentation. This course on developing RAG Applications using LangChain could be useful for Technical Writers who want to learn how to use AI to automate technical writing tasks. The course covers a variety of topics that are relevant to Technical Writers, such as natural language processing, machine learning, and data visualization.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course on developing RAG Applications using LangChain could be useful for Project Managers who want to learn how to use AI to automate project management tasks. The course covers a variety of topics that are relevant to Project Managers, such as natural language processing, machine learning, and data visualization.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for testing software applications and identifying defects. This course on developing RAG Applications using LangChain could be useful for Quality Assurance Analysts who want to learn how to use AI to automate quality assurance tasks. The course covers a variety of topics that are relevant to Quality Assurance Analysts, such as natural language processing, machine learning, and data visualization.
Salesforce Developer
Salesforce Developer
Salesforce Developers are responsible for developing and customizing Salesforce applications. This course on developing RAG Applications using LangChain could be useful for Salesforce Developers who want to learn how to use AI to build more efficient and effective Salesforce applications. The course covers a variety of topics that are relevant to Salesforce Developers, such as natural language processing, machine learning, and data visualization.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. This course on developing RAG Applications using LangChain may be useful for Data Engineers who want to learn how to use AI to automate data engineering tasks. The course covers a variety of topics that may be relevant to Data Engineers, such as natural language processing, machine learning, and data visualization.
For more career information including salaries, visit:
OpenCourser.com/course/kbrxwl/gen
Reading list
We've selected nine 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
Gen AI - RAG Application Development using LangChain.
This textbook provides a comprehensive introduction to language models and their applications in human language technology. It offers in-depth coverage of topics like neural language models, attention mechanisms, and language embeddings.
Provides practical guidance on implementing machine learning models using popular Python libraries. It covers topics like data preprocessing, model training, and model evaluation, which are essential skills for developing RAG applications.
This online book and accompanying video lectures provide a practical and accessible introduction to deep learning. It covers topics like convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive introduction to natural language processing using Python. It covers topics like tokenization, stemming, parsing, and machine learning for NLP tasks.
This classic textbook provides a comprehensive introduction to statistical learning methods and their applications. It covers topics like supervised and unsupervised learning, model selection, and regularization. While not directly focused on RAG applications, it offers essential background knowledge for understanding machine learning concepts.
This textbook provides a comprehensive overview of speech and language processing, including topics like speech recognition, speech synthesis, and natural language understanding. It offers a broader perspective on language processing technologies that can complement the focus on RAG applications.
Save
This widely-used textbook provides a comprehensive overview of deep learning models and their applications. While not specifically focused on RAG, it offers essential background knowledge on neural networks and deep learning concepts.
This classic textbook provides a comprehensive introduction to reinforcement learning, a type of machine learning that is often used in natural language processing tasks. It offers a theoretical foundation for understanding how RAG applications can learn from feedback.
Save
Compilation of Turing's seminal writings on computation, intelligence, and mind. It provides historical context and insights into the foundations of artificial intelligence, which is relevant to the development of RAG applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kbrxwl/gen
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Effort
9.5 total hours
Via
Udemy
Good to know
Develops complex applications using RAG (Retrieval-Augmented Generation) frameworks, including Agents, Chains, Retrievers, and Loaders
Strengthens understanding of Language Embeddings and Vector Databases, enabling efficient semantic search and similarity analyses
Provides hands-on coding experience with multiple Prompt Engineering techniques to enhance RAG application efficiency
Offers comprehensive study of core LLM and framework components, such as Agents, Tools, Chains, and Parsers
Features practical projects covering various use cases, including SQL RAG, Conversational Memory, CV Analysis, and Structured Data Analysis
Provides opportunities for learners to build comprehensive RAG applications, including UI using Streamlit, based on real-world business needs
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