25% Off Introduction to Large Language Models (LLMs) In Python | Udemy Review & Coupon

Learn how to create a document-reading virtual assistant using LLMs.

This course covers:

This course introduces learners to the topic of large language models (LLMs) in Python. Through a series of 2.5 hours of on-demand video, one article and access on mobile and TV, learners will be able to gain a full lifetime access to this comprehensive course.

The instructor will start by introducing the concepts of LLMs in Python, covering topics such as language modeling with neural networks, natural language processing, and deep learning. Learners will then move on to discovering how to build a document-reading virtual assistant using LLMs. The course will also cover the use of tools such as TensorFlow, Keras, and GPT-2 for developinng powerful language models.

By the end of the course, learners will have a solid grasp on large language models, natural language processing, and deep learning, as well as hands-on experience creating a document-reading virtual assistant. A certificate of completion will be awarded upon successful completion.

What you'll learn

You can learn to work with Jupyter notebooks in the Saturn Cloud, a new cloud ecosystem.

Read multiple PDF files into Python.

Utilize standard natural language processing (NLP) techniques, including entity recognition and keyword extraction.

Familiarize yourself with popular Large Language Model (LLM) frameworks, including LangChain.

The implementation of LLM frameworks allows for abstract summarization and question answering.

About Instructor

Minerva Singh obtained a PhD from the University of Cambridge in 2017, where her research focused on using data science techniques to measure the effects of deforestation on tropical ecosystems. She holds an MPhil from the School of Geography and Environment and an MSc from the Department of Engineering at Oxford University. She has over 10 years of experience conducting academic research and advising stakeholders in data science, deep learning, and earth observation (EO) related topics. Her work has been published in high-level peer-reviewed international scientific journals such as PLOS One.

She has experience in implementing various tasks related to machine learning, data visualization, spatial data analysis, deep learning, and natural language processing using both R and Python. She has acquired her education from prestigious universities and has developed her statistical and data analysis skills through various online courses, including The Analytics Edge, Statistical Learning, and the IBM Data Science Professional certificate Track. She specializes in a variety of topics including deep learning (Tensorflow, Keras), machine learning, spatial data analysis (including EO data processing), data visualizations, natural language processing, and financial analysis, among others. She has served as a peer reviewer for well-regarded academic journals, including Remote Sensing, and has given guest lectures at prestigious events such as the Open Data Science Conference (ODSC).

Description

Learn how to utilize large language models (LLMs) with my comprehensive course titled "Introduction to Large Language Models (LLMs) In Python."

This course provides you with the tools and knowledge to create a Document-Reading Virtual Assistant using LLM frameworks like OpenAI, LangChain, and LLMA-Index. Whether you have experience with LLM implementation or are looking to enhance your AI skills, this course provides a valuable opportunity to delve into the field of AI.

Course Highlights:

Utilize the Saturn Cloud, a cloud-based Python environment, to implement robust LLM implementations.

The course "Practical Text Analysis" teaches essential Natural Language Processing (NLP) techniques, such as entity recognition and keyword extraction, for deconstructing text documents.

Explore various techniques for LLM frameworks, such as LangChain, OpenAI, and LLAMA-Index, that can be used for abstract summarization and querying.

What are the benefits of enrolling in this course?

By enrolling in this course, you will gain expertise in utilizing the potential of text data with Large Language Models (LLMs). Our experienced instructor, who has an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, will provide you with the guidance necessary for navigating the complexities of LLM implementation.

In addition to the course content, you will receive ongoing support to ensure that you get the most out of your investment. Become a part of our community of learners, engage in LLM analysis, and enhance your knowledge in AI and data science.

Enroll now to access the benefits of text data with LLMs.

This course is intended for:

Students who have previous experience with NLP analysis.

Individuals who are interested in using LLM frameworks may find it beneficial for learning more about your texts.

Individuals who study and work in the field of Artificial Intelligence (AI)