Classic machine learning (ML) methods and deep learning (DL) are two approaches to solving complex problems in data science. Here are some pros and cons for each:
Classic machine learning:
Pros:
- Faster and more efficient for smaller datasets.
- Simpler and more interpretable models.
- Easier to debug and improve upon.
Cons:
- Not suitable for complex, unstructured data like images and videos.
- Limited to supervised and unsupervised learning.
- May require extensive feature engineering.
Deep learning:
Pros:
- Very effective for unstructured data, like images, videos, and natural language processing.
- Can learn complex features and representations automatically, reducing the need for extensive feature engineering.
- Can scale up to large datasets.
Cons:
- Requires large amounts of high-quality data for training.
- Can be computationally expensive and require specialized hardware like GPUs.
- Can produce black-box models that are difficult to interpret.
In summary, classic ML is better suited for smaller, structured datasets where interpretability and simplicity are important, while DL is more suitable for complex, unstructured data where automatic feature learning is crucial, even at the expense of interpretability and compute resources.
