This course is your comprehensive guide, designed specifically for Gophers who want to add powerful AI skills to their toolkit. Much of the code in this course is written in Go, but some of it is written in Python, where it makes sense to do so, and this means that before taking this course you should have a basic understanding of both languages.
We’ll start with fundamental AI concepts, building a strong foundation with practical, hands-on projects. Then, we’ll dive into the world of machine learning, tackling everything from classic regression models to modern neural networks. You’ll learn how to leverage Go for high-performance AI applications, and discover how to integrate it with Python and cutting-edge tools like Hugging Face and LLMs for state-of-the-art solutions.
What You’ll Learn:
- Search Algorithms & Intelligent Agents: Master core AI search algorithms like A* and Dijkstra’s by solving mazes and building a robot vacuum.
- Propositional Logic & Model Checking: knowledge based AI agents often need to make decisions based on available information in the world they operate in. Propositional logic and model checking are two different approaches to solving this problem.
- Uncertainty: Learn how AI agents handle randomness by creating a Battleship AI and a card-counting Blackjack player.
- Machine Learning Fundamentals: Get a practical understanding of linear regression by building models in both Python and Go to predict housing prices.
- Deep Learning & Neural Networks: Build a neural network from scratch for housing price prediction and a Convolutional Neural Network (CNN) for image classification.
- Natural Language Processing (NLP): Discover the power of NLP by creating an extractive summarization program in Go. You’ll also learn to interface with external models from Hugging Face and harness the power of Large Language Models (LLMs) to create hybrid summarization systems.
- Large Language Models (LLMs): Learn how to connect your Go programs to Large Language Models like ChatGPT. We’ll use a locally hosted LLM using Ollama, but the code we write will be 100% compatible with OpenAI, which is used to connect to most LLMs.
Course Requirements: This course is for intermediate to advanced Go developers. You should be comfortable with Go syntax and core concepts. A basic understanding of data structures like graphs and trees is also helpful, but not required. You should also have a basic understanding of Python. All you need is a computer running Windows, macOS, or Linux. While a GPU will speed up certain deep learning tasks, it is not essential; everything will run on a CPU.
Why This Course?
This isn’t just another machine learning course; it’s tailored for Go programmers. You’ll learn how to build production-ready AI and machine learning applications that leverage Go’s performance and concurrency. By the end, you’ll have a portfolio of projects and the skills to confidently build your own intelligent applications.
Ready to build the future of AI with Go? Enroll now and start your journey!
