This specialization is a quick start guide to help people use and launch LLMs like GPT, Llama, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts, and covers topics such as constructing agents, fine-tuning a Llama 3 model with RLHF, building recommendation engines with Siamese BERT … [Read more...] about Quick Start Guide to Large Language Models (LLMs)
Data Science
Statistical Learning for Engineering Part 2
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss … [Read more...] about Statistical Learning for Engineering Part 2
Engineering Probability and Statistics Part 1
Welcome to Engineering Probability and Statistics Part 1. Throughout your time in this course, you will be given opportunities to check your understanding of course material, as well as engage in quizzes to reflect on all the concepts you have explored within each module. By the end of this part 1 course on engineering probability and statistics, you will have a foundational … [Read more...] about Engineering Probability and Statistics Part 1
Machine Learning and Data Analytics Part 1
This course delves into both the theoretical aspects and practical applications of data mining within the field of engineering. It provides a comprehensive review of the essential fundamentals and central concepts underpinning data mining. Additionally, it introduces pivotal data mining methodologies and offers a guide to executing these techniques through various algorithms. … [Read more...] about Machine Learning and Data Analytics Part 1
Foundations for Data Analytics Part 2
This course offers students an opportunity to learn fundamentals of computation required to understand and analyze real world data. The course helps students to work with modern data structures, apply data cleaning and data wrangling operations. The course covers conceptual and practical applications of probability and distribution, cluster analysis, text analysis and time … [Read more...] about Foundations for Data Analytics Part 2
