Introduction to Generative AI for Software Development
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Introduction to Generative AI for Software Development
This course is part of Generative AI for Software Development Professional Certificate
Instructor: Laurence Moroney
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What you'll learn
Integrate generative AI in development. Learn to use generative AI tools from initial design to deployment, enhancing your efficiency and creativity.
Optimize your code quality. Improve your coding, if youβre just starting and need help fixing bugs or an experienced developer breaking new ground.
Experiment quickly. Using LLMs can speed up your ability to prototype and test new features, allowing you to quickly iterate and ship your code.
Learn how LLMs work. By knowing how machine learning systems work, youβll be able to use them more effectively to support your work as a developer.
Skills you'll gain
Tools you'll learn
Details to know
6 assignments
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There are 3 modules in this course
This course is designed to enhance your skills by integrating AI chatbots as pair programmers in your development process. Youβll learn about how large language models (LLMs) work and how this general-purpose technology can be applied to common software development tasks to help boost productivity, creativity, and support you in your tasks as a developer.
By the end of this course, you will be able to: - Understand the differences between machine learning and traditional software development - Describe how large language models generate text - Prompt an LLM to assist in the tasks that make up the software developer role - Guide an LLM to complete a task in a specific way by writing detailed prompts and iterating to improve output - Leverage the depth of software development knowledge encoded in an LLM by prompting it to assume specific job roles or personas - Write code quickly using an LLM as a pair-coding partner - Analyze code for efficiency, security, and performance using an LLM This course assumes you have a background in software development, but are new to using LLMs as part of your development process. By knowing how machine learning systems work, and having an understanding of how they can be applied in software development, youβll be able to use them more effectively to support your growth and work as a developer or engineer.
What's included
8 videos5 readings2 assignments1 app item
8 videosβ’Total 37 minutes
- Conversation between Laurence Moroney and Andrew Ngβ’12 minutes
- Course Overviewβ’1 minute
- What is Generative AI?β’3 minutes
- AI and machine learningβ’4 minutes
- Machine learning exampleβ’4 minutes
- Supervised learningβ’4 minutes
- Introduction to transformersβ’3 minutes
- Key transformer conceptsβ’6 minutes
5 readingsβ’Total 25 minutes
- In-video resourcesβ’2 minutes
- Setting up your Jupyter environmentβ’10 minutes
- Essential reading: Engage directly with our Jupyter and ChatGPT labsβ’2 minutes
- Getting the Most Out of this Courseβ’10 minutes
- Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!β’1 minute
2 assignmentsβ’Total 30 minutes
- Quiz 1β’15 minutes
- Quiz 2β’15 minutes
1 app itemβ’Total 1 minute
- Intake Surveyβ’1 minute
What's included
12 videos2 assignments2 ungraded labs
12 videosβ’Total 59 minutes
- Transformers and codeβ’3 minutes
- Generating code with chatbotsβ’9 minutes
- Iterative promptingβ’4 minutes
- Giving the LLM feedbackβ’5 minutes
- Assigning the LLM a roleβ’6 minutes
- Leveling up with multiple rolesβ’2 minutes
- Expert roles for specialized knowledgeβ’5 minutes
- LLM best practicesβ’4 minutes
- LLM-Powered Coding Tools: Chat, IDE-Integrated, Agenticβ’5 minutes
- LLM-Powered Coding Tools: Strengths and Weaknessesβ’5 minutes
- The Context Windowβ’6 minutes
- LLM-Powered Coding Tools: Best Practicesβ’5 minutes
2 assignmentsβ’Total 30 minutes
- Quiz 1β’15 minutes
- Quiz 2β’15 minutes
2 ungraded labsβ’Total 10 minutes
- ChatGPT environment - To be used alongside with the lecturesβ’5 minutes
- Python environment - To be used alongside with the lecturesβ’5 minutes
What's included
9 videos2 readings2 assignments1 programming assignment4 ungraded labs
9 videosβ’Total 46 minutes
- Data structures in productionβ’1 minute
- Arraysβ’4 minutes
- Linked listsβ’7 minutes
- Self-paced exercise: Doubly-linked Lists β’4 minutes
- Treesβ’10 minutes
- Graphsβ’6 minutes
- Graded lab: Graph algorithmsβ’2 minutes
- Hash tables and mapsβ’11 minutes
- Course conclusionβ’2 minutes
2 readingsβ’Total 15 minutes
- (Optional) Downloading your Notebook and Refreshing your Workspaceβ’10 minutes
- Acknowledgmentsβ’5 minutes
2 assignmentsβ’Total 30 minutes
- Quiz 1β’15 minutes
- Quiz 2β’15 minutes
1 programming assignmentβ’Total 180 minutes
- Building a Travel & Flight Optimization Appβ’180 minutes
4 ungraded labsβ’Total 75 minutes
- ChatGPT environment - To be used alongside with the lecturesβ’5 minutes
- Building a Doubly Linked List Class with an LLMβ’60 minutes
- GPT-4o Environment for Ungraded Lab (to use alongside the Ungraded Lab)β’5 minutes
- GPT-4o Environment for Assignment (to use alongside the programming assignment)β’5 minutes
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Reviewed on Apr 21, 2025
The course is good, but I just battled with understanding the Python code part, as I'm a beginner.
Reviewed on Oct 4, 2024
Lawrence's unique style provides tremendous insights on crafting prompts and techniques to get the most out of LLMs, producing modular coding and robust, reliable, and highly efficient software.
Reviewed on Oct 1, 2024
- context window not large enough to host long continuous chats, esp when code pasting is involved
Frequently asked questions
The course focuses on integrating generative AI tools into software development, covering machine learning techniques, transformer models, iterative code refinement, and advanced data structures.
Ideal for software developers, engineers, and aspiring professionals with intermediate knowledge of software development and a basic understanding of AI concepts.
You will learn to integrate generative AI tools, apply machine learning techniques, implement and optimize data structures, and use iterative prompting and role-based AI feedback to refine code.
More questions
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