In the world of machine learning and artificial intelligence, there's a spectrum of models available, each with its strengths, weaknesses, and areas of application. Two **major classes** of models are **traditional models**, like linear regression, decision trees, and support vector machines, and **neural networks**, which include deep learning models. But how do these differ? Let’s dive in.

## Differences

## How to Choose Between Them

1. **Dataset Size:** for smaller datasets, traditional models might be more suitable, while larger datasets might benefit from neural networks;
2. **Complexity of a Problem:** for simpler patterns, a traditional model might suffice. But for more complex patterns, like image recognition, a neural network might be necessary;
3. **Interpretability:** if you need to explain your model's decisions, traditional models are usually more interpretable;
4. **Resources:** if computational resources or training time are a concern, traditional models might be a better starting point.

## Conclusion

While there's no one-size-fits-all answer, understanding the **strengths** and **limitations** of both traditional models and neural networks can help you make informed decisions based on your specific **needs** and **constraints**. As always, practical **experimentation** is a great way to determine the best approach for your unique problem.

Which model type is more interpretable by design?

For a large dataset with complex, non-linear patterns, which model type might be more suitable?

In which scenario might you prioritize using a traditional model over a neural network?

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Introduction to Neural Networks

Neural Networks or Traditional Models

Differences

Limitations

How to Choose Between Them

Conclusion

1. Which model type is more interpretable by design?

2. For a large dataset with complex, non-linear patterns, which model type might be more suitable?

3. In which scenario might you prioritize using a traditional model over a neural network?