Let's break this down, and start with some definitions!

Machine learning is the science of getting computers/models to learn from the data without being explicitly programmed.

What does this mean? It means that we have some data and we want to learn from this data and extract knowledge and insights.

Traditional machine learning algorithms include Decision trees, Random forest, Boosting, XGBOOST, AdaBoost, CatBoost, Logistic Regression, Linear Regression, Support Vector Machine, K-Nearest Neighbor, ....and much more machine learning algorithms.

The reason I called these machine learning algorithms (Traditional machine learning algorithms) is that they have fixed architectures and they can not handle complex problems/data (Such as images, speech, natural language, ....etc). That's why we have deep learning models which are more advanced than traditional machine learning and they are based on artificial neural networks inspired by the way human brain neurons process the information.

Deep learning models: as mentioned earlier, they are inspired by human brain neurons, and they have different architectures that can handle more complex tasks, that's why they are well-suited for tasks that involve large amounts of data, such as image and speech recognition, natural language processing, and other complex pattern recognition tasks, these tasks can not be done with the use of classical/traditional machine learning algorithms.

In deep learning, there are different architectures designed for different problems. If you are trying to build artificial intelligence that can classify images, then you have to use Convolutional Neural Networks (CNN). If you are trying to build models to recognize speech or to process natural language (for example translation), then you have to use Recurrent Neural Networks (RNN) and LSTM-based RNN. There is another "basic" architecture called Artificial Neural Network (ANN) also called feed-forward neural network, we can say it is the simplest one among the aforementioned architectures, and it can be used for classification and regression tasks.

Ensemble models combine multiple base models to produce a more accurate and robust prediction. One classic example of an ensemble method is Random Forest, which combines multiple decision trees to make predictions. Each tree is trained on a random subset of the data, and the final prediction is based on the majority vote or averaged outputs from individual trees. This helps to reduce overfitting and improve generalization.

I hope this can make it a little bit clear for you, and do not hesitate if you have any further questions!

Sure thing! In a nutshell, here's the classification: Machine learning is like a friendly neighborhood where models learn from data and make predictions. Deep learning is the cool kid on the block, using neural networks to learn from even more complex data. Think of machine learning as a wise owl, and deep learning as its trendy, sunglasses-wearing cousin. So, in summary, machine learning is awesome, but deep learning takes it to a whole new level with its neural network wizardry! Hope that helps, happy learning! 🚀

In the list provided, the following methods are considered deep learning:

Now, let's identify the methods that are machine learning but not deep learning:

Finally, Ensemble is a machine learning technique that combines the predictions of multiple models to improve overall performance and reduce overfitting. An example of an ensemble model is Random Forest, which combines multiple decision trees to make predictions. In Random Forest, each tree's prediction is combined to produce the final prediction, leading to more accurate and stable results than individual decision trees.