What will you learn in this Generative AI Engineering with LLMs Specialization Course
Develop in-demand, job-ready skills in generative AI, natural language processing (NLP) applications, and large language models (LLMs) within three months.
Tokenize and load text data to train LLMs, deploying models such as Skip-Gram, CBOW, Seq2Seq, RNN-based, and Transformer-based architectures using PyTorch.
Employ frameworks and pre-trained models like LangChain and Llama for training, developing, fine-tuning, and deploying LLM applications.
Implement question-answering NLP systems by preparing, developing, and deploying NLP applications using Retrieval-Augmented Generation (RAG).
Program Overview
Generative AI and LLMs: Architecture and Data Preparation
⏳ 20 hours
- Introduction to generative AI concepts, LLM architectures, and data preparation techniques.
Generative AI with Large Language Models
⏳ 29 hours
- Exploration of transformer architectures, model training, and fine-tuning methods.
Generative AI Advanced Fine-Tuning for LLMs
⏳ 22 hours
- Advanced techniques for fine-tuning LLMs, including instruction-tuning and reinforcement learning.
Building Generative AI Applications with LLMs
⏳ 20 hours
- Hands-on projects for developing and deploying generative AI applications.
Generative AI Capstone Project
⏳ 29 hours
- A comprehensive project to apply learned skills in a real-world scenario.
Ethics and Responsible AI
⏳ 22 hours
- Understanding ethical considerations and responsible AI practices.
Career Planning and Job Search Strategies
⏳ 29 hours
- Guidance on career development and job search strategies in the AI field.
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Job Outlook
Equips learners with practical skills for roles such as AI Engineer, NLP Engineer, Machine Learning Engineer, Deep Learning Engineer, and Data Scientist.
Provides hands-on experience with LLMs, beneficial for professionals aiming to work with generative AI technologies.
Enhances qualifications for positions requiring expertise in AI model development, fine-tuning, and deployment.
Specification: Generative AI Engineering with LLMs Specialization
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FAQs
- Basic Python and machine learning knowledge is recommended but not mandatory.
- Suitable for beginners with programming experience.
- Step-by-step labs guide learners through LLM implementation.
- Focuses on hands-on learning with PyTorch and AI frameworks.
- Encourages experimentation with generative AI applications.
- Prepares learners for roles such as AI Engineer, NLP Engineer, and Data Scientist.
- Provides hands-on experience with LLMs and generative AI frameworks.
- Teaches deployment and fine-tuning techniques for real-world applications.
- Builds a portfolio of practical projects to showcase expertise.
- Enhances employability in AI-focused organizations.
- Access to Python and PyTorch for hands-on exercises.
- Familiarity with frameworks like LangChain and LLaMA.
- Optional cloud platforms for deploying LLM applications.
- Course provides step-by-step guidance on tool setup.
- No expensive or proprietary tools are required.
- Regular hands-on exercises with model training and fine-tuning.
- Work on small LLM projects before tackling advanced applications.
- Review lab outcomes to improve accuracy and deployment skills.
- Experiment with different architectures like RNNs and Transformers.
- Continuous practice helps integrate generative AI techniques into real-world scenarios.
- Explore advanced topics like reinforcement learning and instruction-tuning.
- Learn about production-level deployment and optimization strategies.
- Join AI research communities for collaboration and mentorship.
- Experiment with multi-modal and large-scale AI models.
- Build a comprehensive portfolio to enhance professional opportunities in AI engineering.
