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Game-playing AI with Swift for TensorFlow
Course
Game-playing AI with Swift for TensorFlow
Accelerate your machine learning model development with Google's Swift for TensorFlow framework. Build AI agents that learn to play games like Tic Tac Toe, Cartpole, and 2048.
Flexible Schedule
Beginner Level
Mentor Support
Estimated Time
3–4 hours/week
Machine learning technology is one of the most exciting innovations of the past few years. It enables cars to drive themselves, and oncologists to diagnose cancer faster. However, from an implementation standpoint, machine learning has traditionally been difficult. This is because machine learning simply has different needs from other types of technologies - and programming languages, the infrastructure with which technology is built, weren't designed with these special needs in mind.
However, Swift is the perfect language for the future of machine learning. The compiler is developed in a modular paradigm, and it has 2 intermediate stages in which code can be modified or injected. With Swift for TensorFlow, Google has showed their commitment to the world of machine learning. They've integrated machine learning capabilities directly into the Swift language compiler. This enables you to write code naturally, and run tasks like automatic differentiation. You can even use Swift's Control Flow (if, if-let, and guard statements, for/while loops, etc).
All in all, this means that you'll be able to train machine learning models, faster, with less and simpler code, enabling a lower barrier of entry into this world. You'll build a minimax agent for Tic Tac Toe, a reinforcement learning agent for Cartpole, and a Monte Carlo Tree Search agent for 2048! Upon completing this course, you'll be able to understand the ideas behind Swift for TensorFlow, the basics of machine learning, and how AI agents are built to play games.
This course comprises 5 purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules.It is anticipated that if you work 3-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Understand the fundamentals of machine learning and artificial intelligence.
- Learn the basics of Swift and Swift for TensorFlow.
- Apply reinforcement learning to build AI agents.
- Implement Minimax and Monte Carlo Tree Search algorithms.
- Build AI agents to play games such as Tic Tac Toe, Cartpole, and 2048.
- Beginners interested in artificial intelligence and machine learning.
- Learners interested in Swift and TensorFlow.
- Aspiring AI and machine learning professionals.
- Developers interested in building game-playing AI agents.
- Basic Mathematics
Course Outline
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Why Learn with SkillUp Online?
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Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
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FAQs
This course explores how artificial intelligence can learn to play games through practical examples. Using Swift for TensorFlow, learners examine concepts related to machine learning, game environments, and AI decision-making while seeing how intelligent systems can improve through experience.
You will explore how AI systems can make decisions within game environments. The course introduces ideas related to learning, strategy, rewards, and experimentation, helping you understand how game scenarios can be used to study broader concepts in artificial intelligence.
A reinforcement learning agent learns by interacting with an environment and receiving feedback based on its actions. Over time, it can improve its decision-making by identifying which actions lead to better outcomes, much like learning through trial, feedback, and experience.
Games provide controlled environments where AI systems can test decisions and receive measurable feedback. They make complex ideas easier to observe because actions have clear consequences, helping learners understand how intelligent systems learn strategies and adapt their behavior.
Yes, Tic Tac Toe can serve as a useful game environment for exploring AI decision-making. Simple games make it easier to understand how an intelligent agent evaluates choices, learns outcomes, and develops strategies before applying similar concepts to more complex problems.
CartPole is a popular reinforcement learning environment where an agent learns to balance a pole by taking appropriate actions. It provides a practical example of how AI can learn from feedback and improve performance through repeated interaction with an environment.
Monte Carlo Tree search is an approach used to explore possible decisions by simulating outcomes. It can help an AI evaluate potential moves and make informed choices. The concept is particularly useful when an intelligent system needs to explore multiple possible paths.
The course introduces concepts related to building AI agents that interact with game environments. You explore how agents observe situations, make decisions, receive feedback, and improve their strategies, providing useful insight into intelligent systems and machine learning behavior.
Machine learning model development involves creating systems that can learn patterns or improve decisions from experience. In this course, the focus is on applying these ideas to game-playing scenarios, making abstract machine learning concepts easier to understand through practical examples.
This course can benefit learners interested in AI, machine learning, reinforcement learning, and game-based experimentation. It is especially useful for those who want to explore how intelligent systems learn through interaction rather than focusing only on traditional datasets and predictions.
Type of certificate
IBM Certificate
Credly Badge
About this course
05 Modules
05 Skills
Includes
Discussion Space
04 Hands-on lab
05 Graded quizzes
01 Final exam
Create
IBM Cloud Account
AI-powered 2048 web app
Exercises to explore
Minimax in Swift
RL for Cartpole in Swift with OpenAI Gym
MCTS for 2048 in Swift
This course has been created by
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