Introduction to AI and Machine Learning with…

Are you a Go developer ready to explore the exciting world of AI and machine learning? 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.

What's inside

Learning objectives

Learn the basic principles of artificial intelligence
Learn ai search algorithms (bfs, dfs, gbfs, dijkstra & a* search)
Learn the basic principles behind machine learning
Learn about creating worlds with rules for artificial intelligence
Learn how to manage probability with artificial intelligence

Learn how to train a model using linear regression and multiple linear regression
Learn how to implement and use a neural network
Learn how to connect to and use remote models on services like hugging face
Learn how to integrate a go application with llms like chatgpt, and locally hosted llms

Learn the basic principles of artificial intelligence
Learn ai search algorithms (bfs, dfs, gbfs, dijkstra & a* search)
Learn the basic principles behind machine learning
Learn about creating worlds with rules for artificial intelligence
Learn how to manage probability with artificial intelligence
Learn how to train a model using linear regression and multiple linear regression
Learn how to implement and use a neural network
Learn how to connect to and use remote models on services like hugging face
Learn how to integrate a go application with llms like chatgpt, and locally hosted llms

Syllabus

Let's go over what we are going to cover in the course, and ensure that our development environment is set up properly.

Introduction

A bit about me

Career center

Learners who complete Introduction to AI and Machine Learning with Go (Golang) will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer is central to designing, building, and deploying intelligent models and systems. This course provides a comprehensive foundation for a Machine Learning Engineer by diving into fundamental principles and practical applications in Go and Python. Learners will gain experience building various models, from linear regression for predicting housing prices to advanced neural networks and Convolutional Neural Networks for image classification, directly applicable to real-world problems. The emphasis on Go for high-performance applications and the integration with Python is particularly beneficial, helping learners develop production-ready solutions and understand how to construct robust, scalable machine learning systems.

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Deep Learning Engineer

A Deep Learning Engineer specializes in designing, training, and deploying neural network models to solve complex problems in areas like image recognition, natural language processing, and predictive analytics. This course provides a solid foundation for a Deep Learning Engineer by teaching how to build neural networks from scratch. You will learn to implement classic neural networks for tasks like housing price prediction and develop Convolutional Neural Networks (CNNs) for image classification. The practical, hands-on approach using Go and Python helps you understand the underlying mechanisms of deep learning architectures, enabling you to construct and optimize high-performance, production-ready models.

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Artificial Intelligence Developer

An Artificial Intelligence Developer creates intelligent systems that can perceive, reason, and act within complex environments. This course is an excellent starting point for an aspiring Artificial Intelligence Developer, focusing on core AI concepts and practical implementation. You will explore various search algorithms like A* and Dijkstra's for problem-solving, develop intelligent agents utilizing propositional logic and model checking for decision-making, and learn to manage uncertainty by building AIs for games like Battleship and Blackjack. The hands-on projects, specifically the robot vacuum and game AIs, provide invaluable experience in designing and implementing agents that operate with sophisticated logic and adapt to dynamic situations.

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Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that enable computers to understand, interpret, and generate human language. This course offers highly relevant skills for a Natural Language Processing Engineer, covering the fundamentals of NLP and their practical application. You will gain hands-on experience by creating an extractive summarization program in Go, which is a direct application of NLP. Furthermore, the course teaches you how to interface with external models from Hugging Face and harness the power of Large Language Models (LLMs), including connecting Go programs to LLMs like ChatGPT and locally hosted alternatives using Ollama. This prepares you to build hybrid summarization systems and integrate advanced language capabilities into various applications.

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Robotics Software Engineer

A Robotics Software Engineer develops the intelligence and control systems for autonomous robots and automated machinery. This course is highly beneficial for a Robotics Software Engineer, providing practical experience with algorithms directly applicable to robotic navigation and decision-making. You will learn and implement various search algorithms, including A* and Dijkstra's, by solving mazes, and explore intelligent agent design through a comprehensive robot vacuum project. This project covers algorithms like Random Walk, SLAM, Spiral, and Snaking, along with obstacle recognition and propositional logic for decision intelligence, which are crucial for developing sophisticated robotic behaviors and efficient navigation strategies.

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AI Solution Architect

An AI Solution Architect designs and oversees the implementation of AI-driven solutions within an organization, translating business requirements into technical blueprints that integrate various AI and machine learning components. This course is highly relevant for an AI Solution Architect, providing a deep understanding of core AI and machine learning principles and their practical application. You will learn to integrate Go applications with cutting-edge tools like Hugging Face and Large Language Models (LLMs), including both remote and locally hosted options. The emphasis on building production-ready AI applications that leverage Go's performance helps in designing scalable and efficient AI architectures, understanding the tradeoffs, and effectively guiding development teams.

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Backend Developer specializing in AI

A Backend Developer specializing in AI designs and builds the server-side logic and APIs that power intelligent applications, often integrating machine learning models and AI capabilities into a larger system. This course is specifically tailored for Go developers, equipping them with the skills to efficiently implement AI and machine learning features within robust backend services. You will learn to leverage Go's performance and concurrency for AI applications, including integrating with external models from Hugging Face and Large Language Models (LLMs). This practical knowledge of connecting Go programs to state-of-the-art AI services is crucial for building scalable, production-ready backend systems that incorporate intelligent functionality.

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Game Artificial Intelligence Programmer

A Game Artificial Intelligence Programmer designs and implements the intelligence and behaviors of non-player characters and game systems. This course offers highly relevant skills for a Game Artificial Intelligence Programmer, focusing on creating intelligent agents and managing uncertainty. You will gain direct experience by building a Battleship AI that handles randomness and a card-counting Blackjack player, demonstrating practical application of AI in game scenarios. Furthermore, the course covers fundamental search algorithms like A* and Dijkstra's, which are essential for pathfinding and decision-making in game environments. The hands-on projects provide a strong foundation for developing engaging and realistic AI behaviors for games.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer focuses on streamlining the lifecycle of machine learning models, from development and training to deployment, monitoring, and maintenance in production environments. This course is highly relevant for a Machine Learning Operations Engineer because it emphasizes building production-ready AI and machine learning applications using Go, a language known for performance and concurrency. Understanding how to integrate Go applications with external services like Hugging Face and Large Language Models (LLMs) is crucial for MLOps. The practical insights into model building, deployment considerations, and scalable application development help in designing efficient MLOps pipelines and ensuring reliable model performance in production.

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Data Scientist Machine Learning Specialist

A Data Scientist Machine Learning Specialist focuses on leveraging machine learning techniques for data analysis, pattern recognition, and predictive modeling, often working with large datasets to extract insights and build deployable models. This course provides a strong practical foundation for a Data Scientist Machine Learning Specialist. You will learn the basic principles behind machine learning, including how to train models using linear regression in both Python and Go for tasks like housing price prediction. Furthermore, the course covers deep learning, with the implementation of neural networks and Convolutional Neural Networks. This hands-on experience in building and understanding diverse ML models helps in selecting, developing, and evaluating effective solutions for data-driven problems.

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Software Engineer Machine Learning Infrastructure

A Software Engineer Machine Learning Infrastructure builds and maintains the foundational systems, tools, and platforms that enable the development, training, and deployment of machine learning models. This course is quite relevant for a Software Engineer Machine Learning Infrastructure, as it focuses on building production-ready AI and machine learning applications, particularly leveraging Go's performance and concurrency. You will gain invaluable experience in integrating various AI components, including connecting Go programs to external services like Hugging Face and Large Language Models (LLMs), and understanding how to structure code for high-performance ML workloads. This practical approach helps in designing robust and scalable infrastructure for machine learning pipelines.

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Artificial Intelligence Research Engineer

An Artificial Intelligence Research Engineer bridges the gap between theoretical AI research and practical implementation, often building prototypes, running experiments, and optimizing algorithms. This role typically requires an advanced degree. This course may be useful for an Artificial Intelligence Research Engineer as it provides a deep dive into implementing various AI and machine learning algorithms from fundamental principles. You will learn to build neural networks from scratch, develop intelligent agents using propositional logic and model checking, and implement advanced search algorithms. The hands-on projects in Go and Python help in understanding the practical challenges of bringing research concepts to life and developing high-performance implementations.

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Computer Vision Engineer

A Computer Vision Engineer develops algorithms and systems that enable computers to interpret and understand visual information from images and videos. For someone aspiring to be a Computer Vision Engineer, this course may be useful as it introduces a critical component of modern computer vision: Convolutional Neural Networks (CNNs). You will gain practical experience by building a CNN for image classification, understanding how these powerful deep learning models process visual data to identify patterns and categorize images. This foundational knowledge in implementing CNNs, especially within the context of a high-performance language like Go, helps build a basis for developing more advanced computer vision applications.

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Quantitative Developer AI Focus

A Quantitative Developer AI Focus applies mathematical and computational models, often incorporating artificial intelligence and machine learning, to financial markets, risk management, or scientific research. This role often requires an advanced degree. This course may be useful for a Quantitative Developer AI Focus, particularly due to its practical application of predictive modeling and uncertainty management. You will learn to build linear regression models for predictions and explore AI agents that handle randomness, such as a card-counting Blackjack player. The emphasis on Go for high-performance applications is valuable in fields like algorithmic trading where execution speed is critical. The course also helps build a foundation in implementing core AI/ML algorithms that could be adapted for quantitative strategies.

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AI Ethics and Governance Specialist

An AI Ethics and Governance Specialist focuses on ensuring that AI systems are developed and used responsibly, addressing issues of fairness, bias, transparency, and accountability. This course may be useful for an AI Ethics and Governance Specialist as it delves into foundational concepts related to knowledge-based AI agents and decision-making, including model checking. You will gain practical exposure to setting up and evaluating properties like "fairness" and "risk" in AI decision processes, as demonstrated with applicant evaluation. While not directly an ethics course, understanding the internal workings of intelligent agents and the logic behind their decisions is fundamental to identifying potential ethical challenges and developing robust governance frameworks for AI systems.

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