Psychiatric Nurse: Duties, Pay, and How to Become One
December 16, 2024
Article · 6 min read
Cultivate your career with expert-led programs, job-ready certificates, and 10,000 ways to grow. All for $25/month, billed annually. Save now
Recommended experience
Intermediate level
Participants should have Python skills and basic knowledge of Large Language Models (LLMs) for full engagement with the course.
Recommended experience
Intermediate level
Participants should have Python skills and basic knowledge of Large Language Models (LLMs) for full engagement with the course.
Navigate through the Hugging Face Ecosystem
Comparing Models using various Factors and Practical Considerations
Using a Model from Hugging Face
Determine the most suitable model for a given task by scoring the results from each candidate model on a variety of parameters.
Add to your LinkedIn profile
September 2024
1 assignment
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are literally thousands of Large Language Models or LLMs available out there that can be used for a plethora of purposes. Hugging Face is the de-facto hub for language models, offering a huge collection where you can find and use almost any model you need. Choosing the right model can be an arduous task given models come in various shapes, sizes and configurations and each model is specialized at something different. So, when you approach Hugging Face in search of the right Model for your requirement, you have to know the art of this matchmaking.
In this course, we will learn how to navigate through the Hugging Face Hub for Models, matching their configurations to your needs. We will understand key characteristics of Models (LLMs), such as Size, Computational Requirements, Specializations, Licensing and so on. We will look into various families of Models and their specializations, performance and variants. We will also learn how to use various models from Hugging Face and Evaluate them based on your requirements. This course is designed for professionals deeply involved in the field of AI and machine learning, including Data Scientists, Machine Learning Engineers, AI Engineers, LLM RAG Application Developers, Software Developers, and IT Engineers. It targets individuals who are actively building or plan to build applications leveraging Large Language Models (LLMs) and seek to enhance their ability to select and utilize the most appropriate models for their specific needs. Participants should have a strong foundation in Python programming and a basic understanding of Large Language Models (LLMs) and their programmatic use, as the course will build on these concepts with practical coding exercises and advanced topics like model selection, comparison, and evaluation. By the end of this course, learners will have achieved four key objectives. They will master navigating the Hugging Face ecosystem, gaining proficiency in finding and understanding various models. They will also learn to effectively use these models, comparing them based on multiple factors and practical considerations. Additionally, the course will guide participants in testing and evaluating different models, enabling them to score and assess the results based on specific parameters. Ultimately, learners will be equipped to select the most suitable model for a given task, ensuring optimal performance in their applications.
This course develops advanced skills in selecting and evaluating Large Language Models (LLMs) from the Hugging Face Hub. Learners will master navigating the model repository, analyzing key characteristics like computational needs and specializations, and strategically matching models to project requirements for optimal application performance.
11 videos4 readings1 assignment1 peer review3 discussion prompts
The Coursera Instructor Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their courses.
DeepLearning.AI
Course
DeepLearning.AI
Course
Coursera Instructor Network
Course
Coursera Instructor Network
Course
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Financial aid available,