PRACTICAL AI FOR DEVS
ACCELERATE YOUR PROGRAMMING AND CAREER
with a seven week foundation in AI
- Next course starts 26 February 2024
- $1,680 ex GST — Partial scholarships available
- 12 hours per week, designed to fit around your life
AI has been heralded as a new era of computing, as significant as the PC, internet, mobile or cloud computing. This course is designed to help programmers get up to speed with this technology and help them apply it to their day to day work.
At the end of this seven week, online part-time course you will be:
- Applying AI assistants and tooling to accelerate your coding
- Able to build a range of different AI powered apps
- Up to speed with the state of AI, current trends and what they mean for you
Applied AI for Developers will provide you with a foundation of knowledge and experience that will transform your understanding of AI. Led by the Dev Academy co-founder Joshua Vial, this program is designed to elevate your knowledge, skills, and career growth in just seven weeks.
Joshua brings a wealth of knowledge, real-world experience, and an innovative mindset to the program. His engaging teaching style, coupled with a deep passion for AI, will keep you motivated and inspired throughout your learning journey.
Throughout my nearly 25 years programming and over a decade teaching I have never seen anything impact my work as significantly or swiftly as AI tools.
— Joshua Vial, co-founder
Practical individual and group projects
At Dev Academy, we focus on hands-on learning through individual and group projects. These tasks let you apply your AI knowledge in real-world situations, encouraging creative thinking, problem-solving, and teamwork. Completing these projects can bolster your professional portfolio and career prospects.
Short, sweet, and fits around your life
Seven weeks. With estimated workload of around 12 hours per week it suits your schedule. Attend online lectures or watch them afterward. Study and work on projects during the day, evenings, weekends... whatever works for you!
Curriculum and structure
This course offers a personalized learning experience, featuring weekly lectures, office hours, and a mix of core exercises and projects to help you meet your AI goals.
- Personalized learning plan based on prior experience and goals
- Combination of core and optional exercises each week
- One live lecture per week, repeated twice and recorded
- Drop-in office hours with JV each week, at least one in the evening
- Mix of prepared curriculum, individual projects, and group projects
- Course taught in Python, no prior Python knowledge required
Pre-Course: Foundations for Success
In this preparatory phase, you'll get acquainted with various AI coding assistants and learn how to run Local Language Models (LLMs) on your own machine.
- AI coding assistants
- GitHub Copilot, Codeium, Tabnine, Ghostwriter, CodeWhisperer, Cursor, Aider, Open Interpreter
- Evaluation and comparisons
- AI-powered learning
- Running local LLMs
Week 1: Foundations and Interfaces
This week provides an introduction to the ethics, key players in AI infrastructure and models, and hands-on experience with APIs like OpenAI.
- Ethics, pitfalls, and possibilities
- Overview of some providers
- Models: OpenAI, Anthropic, Cohere, Google, Huggingface
- Infrastructure: Azure, AWS, Google Cloud, HF Spaces, Runpod
- OpenAI API
- Function calling
- Building plugins
- Compatible APIs
- Developing against a local LLM
- OpenRouter to easily access many models
- Google Colab and Jupyter Notebooks
Week 2: Advanced Prompting and Frameworks
This week focuses on mastering advanced prompting techniques and introduces you to academic research in the field of AI.
- Langchain framework overview
- Advanced prompting techniques
- Zero shot, one shot, few shot
- Step-by-step instruction
- Chain of thought, tree of thought, graph of thought
- Persona adoption
- Mix of experts
- Huggingface Transformers introduction
- Strategies for engaging with academic research
- Researching and evaluating models
Week 3: Images, Voice, Video, and Translation
This week is dedicated to exploring the wide range of AI applications in multimedia, from generating images and transcribing voice to creating avatars.
- Text to image generation
- Transcription with Whisper
- Text to speech
- Voice cloning
- Avatar generation
Week 4: Retrieval Augmented Generation
This week lets you dive into the mechanics of knowledge retrieval, utilizing vector databases and augmented generation, while learning how to monitor and analyze these processes.
- Retrieval augmented generation
- Vector databases
- Embedding functions
- Open vs closed providers
- Knowledge graphs
- Observability and analytics (Phoenix and Langfuse)
- Exploring GptCache for caching
Week 5: Fine-Tuning and Deployment
In this week, you'll delve into the intricacies of fine-tuning models, managing training data, and deploying AI models effectively.
- Fine-tuning models
- Open (e.g., Llama 2) vs closed (OpenAI)
- Managing training data
- Synthetic training data
- Evaluation and metrics
- Deploying models
Week 6: Agent-Based AI Systems
This week guides you through the design and functioning of AI-powered agents, offering a comparative study of different frameworks and discussions on safety and alignment.
- Overview of some popular implementations
- Auto-GPT, AgentGPT, SuperAGI, Botpress, MetaGPT, DevOpsGPT, GPT-Researcher
- Comparison of agent frameworks
- Langchain, Haystack, Llama index
- Tools and monitoring
- Human supervision
- Recursive self-improvement, replication, and autonomous sourcing of compute **
** (Not really; we will focus extensively on alignment and safety)
Week 7: Culminating Projects
The course wraps up with this final week, where you'll apply all the knowledge and skills you've gained in individual or group projects.
Note: AI is moving fast, so our curriculum does too. Expect changes as we go.
- 12 hours of study time per week over 7 weeks
- Skills Needed
- You should know at least one programming language.
- You should be comfortable using Git.
- You should be able to set up your own development tools like Docker, Conda, and WSL (Windows users only).
- You should be comfortable using a Linux terminal
- Hardware and Software Requirements
- Operating System:
- Windows: 10 Pro recommended, Home Edition also works.
- macOS: High Sierra 10.13 or newer.
- Linux: Ubuntu 18.04 LTS or newer.
- Memory: 8GB RAM minimum.
- Disk Space: At least 20GB free.
- CPU: A modern quad-core processor.
- Internet: Stable connection required.
- Note: A graphics card is optional.
- Operating System:
$1,680.00 excluding GST ($1,932.00 including GST).
These fees can not be covered by student loans or allowances.
We're also keen on ensuring that this programme is accessible to a broad community. As a result, we're providing a limited number of partial scholarships for individuals facing financial difficulties, making it possible for them to participate in the program.
To apply for this financial hardship scholarship, simply apply via the "Apply for Scholarship" link below.