The primary difference between machine learning and deep learning is the ability to automatically extract features from raw data.

The main difference between machine learning and deep learning lies in their approach to data analysis.

1. Machine Learning: In machine learning, the algorithm needs to be told what kinds of patterns to look for in the data. This is done by providing the algorithm with a set of features extracted from the raw data. These features are essentially variables that the algorithm uses to create a model that can make predictions or decisions without being explicitly programmed to perform the task.

2. Deep Learning: On the other hand, deep learning algorithms are a subset of machine learning algorithms that are based on artificial neural networks, specifically Convolutional Neural Networks (CNN). These algorithms are capable of learning to extract features from raw data automatically. This is done by passing the raw data through layers of artificial neurons, each of which processes the data and passes it on to the next layer. As the data passes through these layers, the algorithm learns to identify increasingly complex patterns in the data.

In summary, while both machine learning and deep learning involve creating models that can learn from data, the key difference is that machine learning algorithms require manual feature extraction from the raw data, while deep learning algorithms can automatically learn to extract features.