Build AI Models from Scratch

Artificial intelligence (AI) is transforming industries, from healthcare to entertainment, and is becoming an essential tool in solving complex problems. While AI development once required deep technical skills, that’s no longer the case. Today, anyone can start building their own AI models—whether you have coding experience or not. But which method is easier? Should you use a no-code platform, or should you dive into coding with Python and TensorFlow?

In this video, we compare the two main approaches to AI model creation: no-code platforms and code-based methods. Our goal is to guide you through the pros and cons of each, so you can decide which path suits your needs, skills, and goals.

The Rise of AI and Its Accessibility

AI used to be a field dominated by tech giants, data scientists, and experts with years of experience in mathematics and programming. But as AI tools have evolved, so too have the ways we can interact with them. Today, platforms like Teachable Machine and RunwayML have made AI more accessible to the average person, allowing for the creation of AI models without writing a single line of code.

For those who prefer more control over the inner workings of AI, coding your own AI models using frameworks like TensorFlow or PyTorch is still the gold standard. But with more options available, learners of all levels can now jump into AI, whether they prefer a no-code interface or coding solutions from scratch.

In this video, we’re not only showing you how to create AI models but also diving deep into the advantages and limitations of each method—no-code vs code-based—to help you find the best solution for your AI projects.

No-Code AI Models

No-code AI platforms like Teachable Machine offer a drag-and-drop interface, making it easier than ever for beginners to create AI models. These platforms are perfect for those who want fast results and don't have the time to dive into complex programming.

In the video, we walk you through creating your very first AI model with Teachable Machine. The example we use is an image recognition model, where you upload different images into separate categories, train the AI, and then use it to identify new images. The entire process takes just minutes and requires no programming skills.

Why is this useful?

For beginners and non-technical users, no-code platforms provide the ability to build AI models quickly and efficiently. This can be extremely helpful in industries like education, content creation, or small businesses where there's a need for automation or AI but not enough technical resources.

However, no-code platforms have their limitations. They lack the flexibility and customizability that code-based methods offer. For example, you may not be able to fine-tune the model architecture, adjust hyperparameters, or experiment with new algorithms. But for many simple projects, these platforms provide more than enough capability.

Code-Based AI Models: Flexibility and Precision

If you're looking for more control over your AI models or need advanced functionality, coding your models with Python and TensorFlow offers a world of flexibility. In the second part of the video, we dive into coding your own neural network from scratch.

Starting from setting up your development environment to training and deploying your model, coding allows you to control every aspect of the model’s architecture, data manipulation, and optimization. In our tutorial, we cover how to:

• Set up your AI development environment with Python and TensorFlow.

• Import essential libraries and prepare your data for training.

• Build a neural network with customizable layers.

• Train and evaluate your model, and adjust its performance based on accuracy metrics.

• Deploy the model for real-world use in applications or on the web.

Coding-based methods, while more complex, give you a much deeper understanding of how AI works under the hood. You can experiment with different neural network architectures, implement custom layers, and tweak parameters to achieve better results. For developers or tech learners looking to get serious about AI, this method offers unmatched flexibility.

However, this flexibility comes at a cost: the learning curve is steeper, and it takes more time to build and train models. Beginners may find coding-based methods intimidating at first, but once you get the hang of it, the opportunities for innovation are endless.

Choosing the Right Approach

So, which approach is easier? The answer depends on your goals, current skill level, and the type of AI project you want to work on.

If you’re just getting started or need a fast solution for a small-scale project, no-code platforms like Teachable Machine are the easiest and fastest option. They allow you to build functioning AI models in a matter of minutes, without needing any technical knowledge.

But if you're looking for more control, precision, and the ability to customize your models, coding-based methods using frameworks like TensorFlow will give you the freedom to experiment and build more complex models. While the learning curve is steeper, the possibilities are nearly limitless.

No Code vs Code - Which Is Easier?

At the end of the day, both no-code and code-based approaches have their place in the world of AI. No-code platforms are revolutionizing accessibility and making AI available to more people, while coding remains essential for those who need deeper customization and control.

In this video, we guide you through both methods, showing you the pros and cons of each so that you can make an informed decision on which path to take. Watch the full video to get hands-on experience in both no-code and code-based AI model building, and discover which method is best for your specific needs.