AI Engineering For Beginners

What you'll learn
Development Fundamentals: GitHub for version control and CI practices tailored for data science projects.
AI Model Interaction: Prompt engineering techniques and Retrieval-Augmented Generation (RAG) to effectively communicate with and enhance AI models.
Model Customization: Fine-tuning pre-trained AI models for specific tasks and implementing AI automation workflows.
Practical Application: Utilizing the Hugging Face platform to access, deploy, and work with state-of-the-art AI models.
Requirements
Basic Python knowledge
Description
AI Engineering for Beginners: (Cover Langchain, Prompt Engineering ,Hugging Face, RAG , Fine Tunning to get you started in AI Engineering in less than 5 hours).
(Basic's + Demo Use case for all)
Why Take This Course?
Artificial Intelligence is revolutionizing industries across the globe, creating unprecedented opportunities for innovation and growth. By diving into AI engineering, you're positioning yourself at the forefront of this technological revolution. Here's why this course is crucial for your career
High Demand
: AI engineers are among the most sought-after professionals in tech, with demand far outpacing supply.
Lucrative Career
: AI engineering offers some of the highest salaries in the tech industry, reflecting the value and scarcity of these skills.
Innovation Driver
: Gain the skills to create cutting-edge AI solutions that can solve real-world problems and transform industries.
Future-Proof Skills
: AI is here to stay. These skills will remain relevant and valuable for decades to come.
Versatility
: The knowledge gained is applicable across various sectors, from healthcare to finance, retail to robotics.
This course provides a comprehensive introduction to AI engineering, equipping you with practical, in-demand skills that will set you apart in the job market and open doors to exciting career opportunities.
Course Content
In this AI Engineering for Beginners course, you will learn
GitHub for Data Science
: Version control and collaboration tools for managing data science projects.
CI for Data Science
: Continuous Integration practices tailored for data science workflows.
Prompt Engineering
: Techniques to effectively communicate with and guide AI models.
RAG (Retrieval-Augmented Generation)
: Methods to enhance AI responses with external knowledge.
Fine-tuning
: Customizing pre-trained AI models for specific tasks or domains.
AI Automation
: Streamlining AI processes and workflows for increased efficiency.
Hugging Face
: Utilizing this popular platform for accessing and deploying AI models.
This is a short course with a perfect blend of theory + code.
One can take this course understanding everything and then choose the use case they want and dive deep.
Who this course is for
Aspiring AI engineers: Individuals looking to start a career in AI or transition from other tech fields.
Data science enthusiasts: Those with basic Python skills who want to explore AI applications.
Software developers: Programmers interested in expanding their skillset to include AI engineering.
Tech-savvy professionals: People from various backgrounds who want to understand and leverage AI in their work.
Homepage

https://www.udemy.com/course/ai-engineering/

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