Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.
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Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.
For Course Creators
For Students
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AI for Software Engineers
Home Skillsoft AI for Software Engineers

Course

AI For Software Engineers

31 hours 49 minutes

Credits: Optional Learning

Description

This course teaches software engineers how to understand and apply generative AI, large language models, and deep learning in practical development contexts. Starting from AI and prompt engineering fundamentals, learners progress to working with transformer architectures and PyTorch, then advance to finetuning LLMs, building NLP applications, and implementing Retrieval-Augmented Generation (RAG) systems using industry tools such as Hugging Face, OpenAI, and Streamlit, before exploring AI-assisted development workflows using Claude Code and Codex CLI.

What Students Will Learn

  • Generative AI, Prompting and Ethics Awareness
  • An Introduction to Generative AI
  • An Introduction to GPT Models
  • Artificial Intelligence and Machine Learning
  • Deep Learning and Neural Networks
  • Getting Started with Prompt Engineering
  • Exploring Prompt Engineering Techniques
  • Case Studies in Prompt Engineering
  • Considerations for Using AI Responsibly

  • NLP and LLMs Proficiency (Advanced Level)
  • Introduction to Large Language Models (LLMs)
  • Transformers: The "T" in GPT
  • Exploring Transformers with Hugging Face
  • Exploring Transformers Lab: Exploring Their Carbon Footprint
  • Introduction to PyTorch and Neural Networks
  • Introduction to PyTorch and Neural Networks Lab: Predicting Electric Vehicle Charging Loads

  • Introduction to Hugging Face
  • Introduction to Finetuning
  • Finetuning Transformers with Hugging Face
  • Finetuning Language Models: Practice Lab
  • Portfolio Project: Analyze Texts With NLP
  • RAG Foundations
  • Build a RAG App
  • RAG Techniques

  • AI Development Tools
  • Introduction to Claude Code and AI-Assisted Development
  • Claude Code and AI-Assisted Development: Tic-Tac-Toe

  • Introduction to Codex CLI and AI-Assisted Development
  • Codex CLI and AI-Assisted Development Lab: Tic-Tac-Toe

  • Generative AI: Navigating the Course to the Artificial General Intelligence Future

Overall Learning Outcomes

  • Explain generative AI, GPT models, machine learning, and deep learning fundamentals
  • Apply prompt engineering techniques for code generation, debugging, summarization, and task automation
  • Use AI responsibly in accordance with organizational policies and ethical guidelines
  • Explain transformer architectures and how LLMs generate and process text
  • Work with Hugging Face transformers to perform language tasks including text generation, sentiment analysis, and summarization
  • Implement and train neural networks using PyTorch in hands-on lab environments
  • Fine-tune transformer models using PEFT and QLoRA techniques for classification and generative language tasks
  • Analyze text data using NLP techniques through portfolio-level projects
  • Build and extend RAG applications using Streamlit, Chroma, and OpenAI
  • Implement AI-assisted development workflows using Claude Code and Codex CLI
  • Demonstrate advanced proficiency in NLP and LLM concepts through skill benchmarks
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