Hey everyone! I have a growing interest in AI and machine learning, especially in understanding how it works behind the scenes. I've come across several suggested learning paths, but they're quite overwhelming; some recommend learning different tools and programming languages. However, I've noticed that most of them suggest starting with Python along with libraries like Pandas, NumPy, Matplotlib, and Seaborn. I've made good progress with Python and the first three libraries, but I'm feeling a bit lost on what to focus on next. I'd love some guidance from experienced developers or professionals in this area to help me figure out the actual next steps to take. Thanks in advance!
5 Answers
You should definitely get familiar with Scikit-learn. It's packed with tons of machine learning algorithms. Start with k-means clustering or linear/logistic regression to get your feet wet. Once you get the hang of the basics, look into pipelines to streamline your projects. Also, for more complex data fits, try out NumPy's polynomial fitting and Scipy for curve fitting. These tools will be invaluable for your practice.
There's a lot to unpack here! You've got to clarify what you mean by 'backend'. Are you referring to GPU libraries, or maybe software frameworks like TensorFlow? Also, if you're looking deep into how AI works behind the scenes, libraries like Pandas or NumPy won't cover that. You’ll likely want to understand how large models like GPT are trained and implemented. Start with some research on that, and maybe experiment with tools like Langchain.
When you say you’re done, what exactly do you mean? Have you applied what you learned by building something? Check out DataTalks' videos on ML Zoomcamp for some practical exposure to using those libraries for training models.
Check out 3Blue1Brown’s YouTube channel for some insightful content. Steve Brunton is also great for learning about neural networks. Start diving into that area, and here are a couple of playlists to help you out: [3Blue1Brown Playlist](https://youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) and [Steve Brunton](https://youtu.be/Vx2DpMgplEM). They make complex topics much easier to grasp!
To truly grasp AI, you'll need to follow a solid learning path like the pros do. It's important to have a strong foundation in math: algebra, calculus, and statistics. Once you nail those down, move to CS fundamentals before tackling courses like CS229 for machine learning. It sounds like a lot, but it’s essential for a deeper understanding!
Thanks for the suggestions! I'll definitely check out Scikit-learn and start with some algorithms.