Honestly, skip the video rabbit holes. Over the years, I have seen way too many people waste time on broken tutorials. Since you have a deadline in Chicago and a tight budget, just grab a sub for DeepLearning.AI Deep Learning Specialization on Coursera. It keeps the fundamentals rock solid without the dependency nightmares you get from random creators. Honestly, dont try to build a custom stack yet either. Just use Google Cloud Vertex AI for your prototype; its got pre-built APIs so you aren't fighting your own code for a month. Get the basics down, deploy it fast, and quit stressing over setup. You'll save yourself so much grief by letting the platform handle the heavy lifting while you focus on your actual project features.

Man, I totally get the frustration with YouTube tutorials, they are a nightmare when things break! Regarding what #1 said about learning AI via YouTube being notoriously unreliable, they are spot on. Since you are in a crunch, honestly, stop watching videos and jump into interactive coding environments. You need to get your hands dirty with real code right now. Check these out:

• Fast.ai. Their courses are legendary, free, and they get you building prototypes immediately.
• Kaggle Learn. This is incredible for quick, bite-sized lessons that actually work in their kernels.
• O'Reilly Media Learning Platform subscription. It is slightly over your budget but has the best access to high-quality code snippets that dont rot. Just pick a project, load up a Lenovo ThinkPad X1 Carbon Gen 11, and start tweaking existing code. You will learn faster by breaking stuff intentionally than watching someone else do it!

Learning AI via YouTube is notoriously unreliable because libraries update constantly, making legacy code break. If you have a one-month deadline for a prototype, I would suggest looking into DataCamp Data Scientist with Python Track.

• The platform uses browser-based sandboxes, which prevents dependency version issues that ruined your tutorial experience.
• Focus on the AI fundamentals course first; it explains the underlying math rather than just importing black-box libraries.
• Make sure to keep your environment isolated using virtual environments if you move to local builds later, otherwise your system-wide Python will get messy fast. Be careful not to rely solely on high-level APIs. You might want to consider mastering NumPy and Scikit-Learn first to ensure your prototype is actually functional and scalable.

Re: Honestly, skip the video rabbit holes. Over the years, I have seen way too many people waste time on broken tutorials. Youre totally right about the dependency hell. If you need a prototype done in a month, stop trying to build your own infrastructure from scratch. In my experience, you should look into Hugging Face Transformers for the actual model implementation. Its way easier than writing everything from the ground up and their documentation is actually updated. If you need a solid environment without messing with your local machine, just run everything through Google Colab Pro for like ten bucks a month. It saves so much headache with libraries because the environment is pre-configured for ML tasks. Just dont get distracted by the bells and whistles, stay focused on the API calls you need for your specific project and youll hit that deadline no problem.