Artificial Intelligence for Simple Games

### Course Overview: This course is designed for individuals looking to delve into artificial intelligence, particularly focusing on algorithms such as genetic algorithms, Q-learning, and deep learning. The course structure begins with foundational concepts and progresses towards more complex topics like deep learning in AI games. It emphasizes hands-on tutorials and provides a platform for understanding how to create custom environments for leveraging the power of AI. ### Pros: - **Effective Learning**: Several reviewers reported gaining a solid understanding of genetic algorithms and Q-learning, which they applied to their own projects post-course. - **Comprehensive Content**: The course covers a range of topics from basics to deep learning, providing a thorough exploration of AI in games. - **Quality Instruction**: Instructors were commended for their clear explanations and the well-organized nature of the course material, especially appreciated in the coding sections. - **Responsive Support**: Instructors responded to queries promptly, helping learners overcome any difficulties they encountered. - **Engaging Teaching Style**: The teaching approach was described as beginner-friendly, with concepts being explained using visual examples and "bite-sized" video tutorials. - **High Value**: The course was deemed to be worth much more than its cost, offering great value for the price. ### Cons: - **Deep Learning Complexity**: As the course advances into deep learning, some found the explanations less clear and the material more challenging to follow. - **Bonus Material Concerns**: At least one reviewer questioned the value of bonus materials included with early sign-up, feeling they were not substantive additions. - **Language and Pace Challenges**: A few learners mentioned difficulty in understanding the instructor due to a fast speech pace and challenges with English proficiency, which could potentially hinder comprehension. - **Practical Application Lack**: One reviewer highlighted a significant issue where the theoretical heavy approach made it difficult to grasp the practical application of Q-Learning algorithms, emphasizing that the course's explanation was not accompanied by practical examples or clear explanations of the "why" behind the formulas. - **Algorithm Complexity**: The complexity of the algorithm presented seemed to be underestimated, with an hour spent writing a simple algorithm that could have been summarized in minutes. ### Final Thoughts: Overall, this course is highly regarded for its ability to teach complex AI concepts effectively, especially when it comes to genetic algorithms and Q-learning. The course structure, which starts with foundational knowledge and builds up to more advanced topics, seems to be a strong point. However, the course loses some points for not offering clear guidance on applying the theoretical aspects of deep learning to practical problems. The instructor's teaching style, while generally effective, may pose challenges to some learners due to pace and language barriers. Despite these drawbacks, the positive feedback significantly outweighs the negative, indicating that this course is a valuable resource for those interested in AI and its applications in games. It's recommended to closely follow along with the tutorials and seek clarification when needed to maximize learning outcomes.