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Minerva Singh

Unlock the potential of large language models (LLM) with my comprehensive course: "Introduction to Large Language Models (LLMs) In Python." With a focus on LLM frameworks such as OpenAI, LangChain, and LLMA-Index, this course empowers you to build your own Document-Reading Virtual Assistant. Whether you're new to LLM implementation or seeking to advance your AI skills, this course offers an invaluable opportunity to explore the cutting-edge field of AI.

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Unlock the potential of large language models (LLM) with my comprehensive course: "Introduction to Large Language Models (LLMs) In Python." With a focus on LLM frameworks such as OpenAI, LangChain, and LLMA-Index, this course empowers you to build your own Document-Reading Virtual Assistant. Whether you're new to LLM implementation or seeking to advance your AI skills, this course offers an invaluable opportunity to explore the cutting-edge field of AI.

Course Highlights:

- Cloud-Based Python Environment: Harness the power of Saturn Cloud, a cloud-based Python environment, to implement robust LLM implementations.

- Practical Text Analysis: Learn to implement essential Natural Language Processing (NLP) techniques, including entity recognition and keyword extraction, to deconstruct the text documents

- Leveraging LLM Frameworks: Discover standard techniques for LLM frameworks, including LangChain, OpenAI and LLAMA-Index, for abstract summarization and querying.

Why Enroll in This Course?

By enrolling in this course, you're embarking on a journey to become an expert in harnessing the potential of text data with Large Language Models (LLMs). Driven by the vision of our experienced instructor, who holds an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, you'll receive the guidance needed to navigate the complexities of LLM implementation.

Beyond the course content, you'll benefit from continuous support, ensuring you extract the maximum value from your investment. Join our community of learners, immerse yourself in LLM analysis, and advance your expertise in AI and data science.

Enroll Now to Unlock the Power of Text Data With LLMs.

Enroll now

What's inside

Learning objectives

  • Learn to work with jupyter notebooks in a brand new cloud ecosystem-saturn cloud
  • Read in multiple pdfs into python
  • Implement common natural language processing (nlp) techniques including entity recognition and keyword extraction
  • Get acquainted with common large language model (llm) frameworks including langchain
  • Implement llm frameworks for abstract summarisation and answering questions

Syllabus

Introduction To The Course
Welcome To the Course
Data and Code
Python Installation
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Start With Google Colaboratory Environment
Google Colabs and GPU
Installing Packages In Google Colab
Another Cloud To Work In: Saturn Cloud
Say Hello To The Saturn Interface
Brain Fail: Dealing With Memory Problems
Get Started With The LLMs and Their Infrastructure
What Is a Document Reading Virtual Assistant?
Get Access To the OpenAI API
Introduction to LangChain
Start Reading in and Exploring Data
Read in a Single PDF
Read In Multiple PDFs
A More Straightforward Way To Read in Multiple PDFs
Learn More About Your Documents: Why We Need A Preliminary NLP Analysis
Entity Matching
Keyword Extraction
What Is TF-IDF?
Text Similarity
Use LLMs To Learn From Your Text
Overview-The Summarisation Process
Abstract Summarizer
Answer Questions Based On Given Text-LangChain
Theoretical Undepinnings
Answer Questions With Llama-Index
Preliminary Prompt Engineering
What Is Prompt Engineering?
Prompt Engineering With Langchain
Basic Python Primer
Introduction to Numpy
What Is Pandas?
Basic Data Cleaning With Pandas
Basic Principles of Data Visualisation

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops professional skills and deep expertise in a particular topic or set of topics
Teaches skills, knowledge, and/or tools that are highly relevant to industry
Builds a strong foundation for beginners
Taught by industry experts
Offers a comprehensive study of science, math, and technology
Offers multi-modal learning experience

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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 Introduction to Large Language Models (LLMs) In Python with these activities:
Review: Natural Language Processing with Python
Deepen your understanding of natural language processing techniques with this classic textbook.
Show steps
  • Read through the first three chapters to establish a foundation in NLP concepts.
  • Work through the exercises in the book to practice applying NLP.
LLM Framework Tutorials
Expand your knowledge of specific LLM frameworks by following guided tutorials and walkthroughs.
Browse courses on Large Language Models
Show steps
  • Identify LLM frameworks that you want to explore further.
  • Find online tutorials or documentation for these frameworks.
  • Follow the tutorials step-by-step to build a basic understanding of the frameworks.
NLP Practice Problems
Sharpen your NLP skills through focused practice on targeted problems.
Show steps
  • Identify a set of NLP practice problems online or in textbooks.
  • Solve the problems using the techniques covered in the course.
  • Review your solutions and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
NLP Study Group
Engage in collaborative discussions with peers to reinforce key NLP concepts and techniques.
Show steps
  • Form a study group with other students in the course.
  • Meet regularly to discuss course material, work through problems, and share insights.
LLM-Powered Virtual Assistant
Test your understanding by building a functional LLM-powered virtual assistant that performs various text-related tasks.
Browse courses on Large Language Models
Show steps
  • Define the scope and functionality of your virtual assistant.
  • Select an LLM framework and API to integrate into your assistant.
  • Develop a user interface for interacting with your assistant.
  • Deploy your virtual assistant and gather feedback from users.
Text Summarization Project
Apply your knowledge of NLP to develop a text summarization tool that extracts key information from documents.
Browse courses on Text Summarization
Show steps
  • Design the architecture of your text summarization tool.
  • Implement the tool using Python and the LLM frameworks covered in the course.
  • Test your tool on a variety of text documents.
  • Write a report summarizing your findings and the performance of your tool.
Contribute to Open-Source NLP Projects
Gain practical experience and contribute to the NLP community by participating in open-source projects.
Show steps
  • Identify open-source NLP projects that align with your interests.
  • Review the project documentation and codebase.
  • Identify areas where you can contribute your skills.
  • Submit a pull request with your proposed changes or contributions.
NLP Hackathon
Challenge yourself and showcase your NLP skills by participating in a hackathon focused on text-related tasks.
Show steps
  • Find an NLP hackathon that aligns with your interests.
  • Form a team or work individually to develop a solution to the hackathon challenge.
  • Submit your solution and present it to a panel of judges.

Career center

Learners who complete Introduction to Large Language Models (LLMs) In Python will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineers specialize in developing and implementing NLP models to extract insights from text data. This course will provide Natural Language Processing Engineers with a comprehensive understanding of LLM frameworks. They will learn how to use these frameworks for advanced NLP tasks such as text classification, named entity recognition, and machine translation, which are crucial for building sophisticated NLP systems.
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions and obtain valuable insights. This course will help Data Analysts in building a foundation in working with large text-based datasets using Python and various LLM frameworks. By harnessing the power of LLMs, Data Analysts can automate data analysis tasks, extract meaningful insights from unstructured text data, and derive actionable intelligence from various sources.
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. This course will help Machine Learning Engineers in gaining expertise in using LLMs for natural language processing tasks. They will learn how to implement LLM frameworks to automate tasks such as text summarization, question answering, and sentiment analysis, which are essential for developing robust machine learning models.
Data Scientist
Data Scientists use data to solve business problems and uncover valuable insights. This course will help Data Scientists in expanding their skillset by introducing them to LLMs and their applications in data science. They will learn how to use LLMs for data exploration, feature engineering, and predictive modeling, which are essential for building effective data science solutions.
Research Scientist
Research Scientists conduct research in various scientific fields, including computer science, natural language processing, and artificial intelligence. This course will help Research Scientists in exploring the cutting-edge advancements in LLM technology. They will learn how to leverage LLMs for their research, enabling them to develop innovative solutions and make significant contributions to their field of study.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will help Software Engineers in building a foundation in LLM integration and implementation. They will learn how to leverage LLMs to enhance the functionality of their applications, such as adding natural language processing capabilities, improving user experience, and automating tasks.
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. This course will help Technical Writers in enhancing their ability to communicate complex technical information effectively. They will learn how to use LLMs to generate clear and concise documentation, improve readability, and adapt to different audiences.
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course will help Product Managers in understanding the potential of LLMs and their impact on product development. They will learn how to incorporate LLMs into their product strategy, identify use cases, and gather user feedback to ensure successful product launches.
Content Strategist
Content Strategists develop and execute content strategies to achieve business objectives. This course will help Content Strategists in creating more engaging and effective content. They will learn how to use LLMs to generate ideas, optimize content for search engines, and analyze user engagement, enabling them to develop successful content strategies.
Information Architect
Information Architects design and organize information systems to ensure usability and accessibility. This course will help Information Architects in enhancing the user experience of information systems. They will learn how to use LLMs to improve information retrieval, create intuitive navigation structures, and optimize content for different platforms.
Business Analyst
Business Analysts identify and analyze business needs to improve processes and systems. This course may be useful for Business Analysts who want to explore the potential of LLMs in process optimization. They will learn how to use LLMs to analyze data, identify inefficiencies, and develop recommendations for improvement.
UX Designer
UX Designers create user interfaces and experiences for websites and applications. This course may be useful for UX Designers who want to learn how to incorporate LLMs into their designs. They will learn how to use LLMs to improve user interactions, personalize content, and conduct user testing.
Marketing Manager
Marketing Managers develop and execute marketing strategies to promote products and services. This course may be useful for Marketing Managers who want to leverage LLMs for marketing campaigns. They will learn how to use LLMs to generate marketing content, analyze customer data, and optimize targeting for better results.
Customer Success Manager
Customer Success Managers ensure customer satisfaction and retention. This course may be beneficial for Customer Success Managers who want to use LLMs to enhance customer interactions. They will learn how to use LLMs to provide personalized support, resolve customer issues, and identify opportunities for upselling and cross-selling.
Sales Manager
Sales Managers lead sales teams and develop sales strategies. This course may be helpful for Sales Managers who want to use LLMs to improve sales performance. They will learn how to use LLMs to generate leads, qualify prospects, and close deals more efficiently.

Reading list

We've selected 12 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 Introduction to Large Language Models (LLMs) In Python.
Provides a comprehensive overview of machine learning for text. It valuable resource for anyone interested in using machine learning for NLP.
Covers various keyword extraction and summarization techniques, providing a strong foundation for implementing these techniques in a Document-Reading Virtual Assistant.
Provides a comprehensive overview of statistical language learning. It valuable resource for anyone interested in learning more about statistical language learning.
Provides a comprehensive overview of speech and language processing. It valuable resource for anyone interested in learning more about speech and language processing.
Is an excellent resource for learning various aspects of NLP with transformers, but it does not go into detail on developing a Document-Reading Virtual Assistant.
Provides a comprehensive overview of deep learning techniques used in NLP. While it covers relevant concepts, it may not be as directly applicable as other resources for developing a Document-Reading Virtual Assistant.
Provides a comprehensive overview of machine learning techniques using Python libraries such as Scikit-Learn, Keras, and TensorFlow. While it does not focus on LLMs or NLP, it offers a strong foundation for understanding the underlying principles of machine learning.
Provides a comprehensive overview of natural language understanding and machine learning for NLP. It valuable resource for anyone interested in learning more about natural language understanding and machine learning for NLP.
Provides a comprehensive overview of the foundations of statistical natural language processing. It valuable resource for anyone interested in learning more about the foundations of statistical natural language processing.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable resource for anyone interested in learning more about information theory, inference, and learning algorithms.
Provides a comprehensive overview of probabilistic graphical models. It valuable resource for anyone interested in learning more about probabilistic graphical models.

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