Generative AI & NLP Development

Overview

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Course

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Generative AI & NLP Development

Master generative AI and NLP with cutting-edge techniques! Supercharge your skills by fine-tuning LLMs, leverage RLHF and RAG, master prompt engineering, and automate AI-driven content generation to create impactful solutions.

Blended

Mentored

Intermediate

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This course is part of a program:

It is not possible to enroll for individual courses on this program. If you wish to take this course, please enroll for the full program.

Generative AI is transforming industries with its ability to automate workflows, enhance task execution, and create intelligent conversational systems. This course equips you with the skills to build, fine-tune, and deploy next-gen AI solutions that drive real impact.

In this course, you will learn to optimize large language models (LLMs) using parameter-efficient fine-tuning and reinforcement learning with human feedback (RLHF) for improved AI performance. You will also explore Retrieval-Augmented Generation (RAG), AI assistants, and prompt engineering to design dynamic conversational AI systems. Plus, you will develop AI agents to automate workflows and enhance operational efficiency.

In addition, you will work with text-to-image and text-to-video models, gaining practical experience in AI-driven image, video, and content generation. The course also covers AI-powered content writing, ethical considerations, and legal aspects of AI media to ensure responsible AI development.

By the end of the course, you will have the expertise to develop, deploy, and optimize generative AI applications, preparing you for advanced roles in AI engineering, data science, and content automation.

This course comprises 5 purposely designed modules that take you on a carefully defined learning journey.

Our proven learning methodology blends the best that instructor-led training and self-paced learning have to offer. Leveraging the power of instructor feedback, mentor-supported hands-on practice, and additional home-based studying, you will build the deep technical and practical understanding todays employers are looking for.

Additionally, you will enjoy learning via an interactive online classroom environment where you will be able to participate and actively engage with your peers, instructors, and mentors. Plus, you will get the opportunity to earn recognized certifications which will help your resume and LinkedIn profile stand out.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include reading material, hands-on labs, and online exam questions.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues.

Once you have successfully completed the course, you will earn your Certificate of Completion.

You will be able to:

  • Fine-tune large language models (LLMs).
  • Implement retrieval-augmented generation (RAG).
  • Develop AI-powered assistants with LangChain.
  • Generate AI-based content (text, images).
  • Understand AI agents and task automation.
  • Use AutoGPT and CrewAI for AI orchestration.
  • Develop AI workflows for research and business applications.

  • Machine learning engineers Deploying & scaling AI models in the cloud.
  • Data scientists Automating ML workflows & optimizing model deployment.
  • AI engineers Integrating MLOps best practices for production-ready AI.
  • Cloud & DevOps engineers Expanding AI model deployment on AWS & GCP.
  • Software engineers Building & managing scalable AI applications in the cloud.
  • IT & system administrators Exploring cloud AI infrastructure & automation.
  • AI product managers Understanding MLOps for AI model lifecycle management.

  • Basic Python programming Familiarity with Python syntax, data structures, and basic scripting.
  • Fundamental machine learning & NLP knowledge Understanding of supervised learning, text processing, and embeddings is helpful.
  • Experience with deep learning frameworks (optional) Exposure to TensorFlow, PyTorch, or Hugging Face is beneficial but not required.
  • Familiarity with APIs & cloud services (optional) Basic knowledge of cloud-based AI services and APIs can enhance the learning experience.