Quickest Way to Learn AI in 2025
Why go long when short works?
This blog is here to guide you on a clear, shortcut path to getting started in the AI world, while also let you explore the new AI products at the same time, to keep you motivated. I wont include overwhelming suggestions, such as
“hey, you should start learning Python because it is the most common and popular programming language…”
or any diagrams, like this:
Most people who search “How to learn AI” are immediately confronted with terms like ML and DL, which can be discouraging and make AI seem difficult. However, consider this: if learning AI through mastering full-stack tech (theory/programming) are the right approach, then it would not be possible for someone like Pranjali Awasthi, who at 16 founded Delv.AI and raised close to $500,000 in investment, or for Alexandr Wang, who by age 24 became the world’s youngest self-made billionaire through AI and created a successful startup. Many young individuals under 18 or 20 are cracking the code and earning good money, not by focusing on how to learn AI, which can lead to an endless cycle of studying new products, tools, or projects released every day or week, but by focusing on learning
how to use AI which is the key question here!
Even if you start learning AI through a general approach, most of us begin with the Transformer architecture, especially since we’re all into AI because of ChatGPT. Soon after, we get lost in the technical complexities of the theory, and even if we understand it, we wonder how we can replicate it, which, as you already know, is not possible. Then, we often shift toward exploring AI tools available online instead of studying them, and we begin thinking about business use cases for them.
To truly learn AI, it’s about how to use these AI products in ways just other than their main purpose, using and implementing them to stay one step ahead, taking advantage of their endless possibilities.
Table of Contents
Open Table of Contents
Start with APIs, Not Python
Tons of blogs will tell you that start with Python because it is the most used and preferred language, and while that’s true, Python itself is an entire ecosystem. You’d need at least 6 months just to learn Python, and by then, there’s likely to be a new trending AI topic. However, most AI tools today are offered or function through APIs, which is a very small part of programming. This gives us an advantage: you don’t need a powerful laptop or to start learning Python to work with AI. APIs are just a small, quicker, and easier part of Python to learn. So, APIs should be the first thing you focus on.
For those who don’t understand what an API is, think of it like a waiter in a restaurant. Here’s how it works:
- You (the customer) want something (like food).
- The kitchen (the system) has the food, but you can’t go there and get it yourself.
- The waiter (the API) takes your request to the kitchen and brings back the food you asked for.
In terms of technology, someone else is running the AI product for you, and you use their API to interact with the product. This API-learning approach not only reduce your learning burden but also eliminates the need for a high performance laptop or PC.
Don’t Install Anything
Yes, the title isn’t misleading, you don’t need to install anything. As I mentioned, we’ll be working with APIs to learn AI, which allows you to bypass the need to load AI products onto your PC or laptop. But what about Python and other tools? There are three famous environments available online that solve this problem for you:
- Colab: https://colab.research.google.com/
- Kaggle: https://www.kaggle.com/
- Lightning AI: https://lightning.ai/
There are many other platforms that allow you to work without installing anything, and these three are among the best. Of these, Colab is the top choice. But what exactly are they?
Colab, Kaggle, and Lightning AI are online tools that let you work with AI and coding without needing to download or install anything on your computer. They run entirely in your web browser, making them incredibly easy to use.
HuggingFace as Search Engine
Right now, the AI domain is progressing insanely every day, and it’s easy to feel like you’re being left behind. As I’ve already mentioned, every new day brings an AI startup, which is nothing more than using APIs from the giants. Meanwhile, the giants themselves are leading the market by launching groundbreaking products weekly or even daily. There’s so much happening in AI that you need a special search engine dedicated to AI products. There are many options, but Hugging Face is the “Google” of AI and the easiest and most popular source to stay updated with AI products while staying focused on them.
If you go to Hugging Face’s models page (https://huggingface.co/models), even a person with zero knowledge can search for text-to-image models. You’ll find plenty of these models, sorted by trending.
Let’s say you come across the “LLaMA-3.3 70B” model, which is similar to the OpenAI model behind ChatGPT. The next step is to go to the Hugging Face spaces tab (https://huggingface.co/spaces), where most AI products are hosted. You can search for the model you find interesting and test it, all without installing or running anything, just using Hugging Face as your search engine!
Additionally, there is the option to use models via API, which is the first step we’ll look at on the Hugging Face platform with zero knowledge, before implementing our own strategies.
However, one important thing to note is that what if the model you liked does not exist on Hugging Face spaces? This is where your next chapter begins: you need to become an intelligent “freebie” seeker.
Be an Intelligent Freebie Seeker
Most of the time, the model you love might not exist on Hugging Face Spaces, but then you can use Google to help solve this issue. For example, if “LLaMA-3.3 70B” does not exist on Hugging Face Spaces, the next thing you should do is search for it online to see if it’s available via API. For instance, when I was in need of LLaMA-3.3 70B, I came across Nebi.us, which offers $100 in free credits to provide this AI product through their API. Those $100 are enough for you to explore the product and see how well it works for you.
You should find plenty of other platforms, but there’s a very high chance that you’ll end up loving the products that already exist on Hugging Face Spaces.
Theory When Needed
This is the most important part where many self-learners struggle, and most of you will likely face this challenge. However, my approach to tackling this issue is that you should first search for the product you’re interested in, rather than going straight into learning the theory. There are so many products out there, and most of them won’t interest you or lead to any real use, meaning learning them has no advantage. In AI products, the same models often trend. For example, the LLaMA-3.3 model I’m interested in is among the most popular, and you’ll easily find a YouTube video explaining it. But what if your interest lies in a non-trending AI product?
If you have zero knowledge, the first step is to search for a technical blog or information on that model. For instance, I found this blog on LLaMA-3.3 by DataCamp: LLaMA-3.3 70B Blog. Copy the technical content from the blog and paste it into this chat room: Gemini on AI Studio. You may have heard of Gemini, which is a competitor to ChatGPT from Google. The reason I suggest using Gemini is that it works similarly to ChatGPT, but with a larger memory to remember older messages.
Start a chat with Gemini, using the technical blog content of the model you’re interested in, and make sure to ask things in your own way, not just reading someone else’s style. This way, you won’t overwhelm yourself. When you ask, “Hey, can you explain how LLaMA-3.3 was created?” Gemini will respond, and this will raise new questions in your mind, which you can continue to explore until you feel you have enough information about the AI product. Afterward, you can watch YouTube videos or dig deeper into the model.
The key here is not only to learn the theory of your favorite product, but also to keep chatting with Gemini using the model’s content that you find online. This approach will help guide you and deepen your understanding.
https://aistudio.google.com/app/prompts/new_chat
Quick Google Searches
One thing I experienced when I finished my master’s thesis was that by the time I completed it, a new alternative AI invention had already introduced in the market, making the approach I used in my thesis suddenly outdated overnight. I feel it’s important to share this because even people who have been working in the AI domain for years, like me, often find themselves in the same position, learning things that quickly become obsolete. At some point, we all need to learn something new again. That’s when I learned that whenever you’re stuck in the AI domain, the best approach is to directly start implementing it using Google. See if it already exists and is available for free. If it does, reuse it, or find a shortcut to learn just enough of it quickly, which will give you enough confidence to search for the right keywords about that product.
I did this myself in the last few days when my thesis was winding up. I searched for the new alternative that was making my thesis outdated and checked if its AI tech code already existed. This allowed me to reuse it and learn just a portion of it using ChatGPT so my thesis could incorporate the latest technology. This is how you can stay updated with the models you’re interested in.
Let’s Implement This Blog
So, let’s implement the previous steps and see how far we can go. I will assume that I have zero knowledge but a strong interest in AI after watching YouTube Shorts!
As I mentioned, Hugging Face is the search engine, and I’m interested in image-chatting models. I saw something like this on YouTube, where I can chat with images. I found it interesting, so I want to search for it in the models section of Hugging Face.
I went to Hugging Face and searched using the filter (Text-to-Image) and found the Flux.1-dev model interesting as a newbie because of its downloads. However, you might be more interested in a different model for a valid reason.
I went to the model page and wanted to see how I can use it as an API. The first thing I observed was that we can run it directly on the website to see how it works. This simplifies things by eliminating the need to use APIs in this case. Let’s check if the website is working correctly by creating an image of a “nature view.”
Now that you find it interesting and performing well using the website demo, let’s see how we can use it as an API. Let’s search for it on Hugging Face Spaces and see if it exists via API.
We found it on Spaces as an API too. If it hadn’t been available on Spaces, we would have used Google to search for a free API provider for it. But luckily, we have it available and running on the same model page.
So, let’s click on “Use as API” and see what pops up.
This tutorial might not be challenging if you’re familiar with APIs, but if you don’t know anything about APIs, what we can do is simply copy the content and paste it into ChatGPT. So, let’s do that.
You can either ask ChatGPT to explain it, but since we’re in a hurry, we should ask ChatGPT how we can implement it on Colab to run and draw an image of nature.
ChatGPT tells you to go to Google Colab and create a new notebook. In the first code block, run !pip install gradio_client. I did that, and then in the next block, I just pasted the exact code with the prompt being the word “nature.” I did that, but I got this:
I then went to the image path to see my first AI API-generated image, and this was the result.
This is how you can connect API learning with exploring models. The more types of models you explore, the more ways you can explore Python code for APIs. You can further use ChatGPT to understand how it defines the code, what changes you can make, and how it will affect the images. There’s a lot to explore.
Now that we’ve developed an interest in the FLUX model, let’s say we want to learn how it was created and the theory behind it. For that, we’ll do a quick search on Google for a technical blog about the Flux model. I found this Medium article that goes into some details about the model, so I copied all its content and pasted it in the AI Studio link I shared earlier.
Then, I started chatting with Gemini regarding its content until I had enough knowledge about how the model was created, and so on.
This is a simple and not very comfortable cycle for exploring while learning AI, but it also helps you stay updated with the latest models out there. I hope this helps you guys!
Happy reading!
