Feature engineering vs Feature Learning (tips tricks 46 )

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_for_microscopists/tree/master/tips_tricks_46-Feature%20engineering%20vs%20feature%20learning

All other code:
https://github.com/bnsreenu/python_for_microscopists

Feature engineering refers to the process of selecting and designing relevant features from raw data to improve the performance of machine learning algorithms. It involves domain expertise and creativity to identify informative features that capture the underlying patterns in the data.

On the other hand, feature learning, also known as representation learning, is a technique that enables a machine learning model to automatically learn relevant features from raw data. It involves using neural networks to discover useful features that can be used for downstream tasks.

This video tutorial demonstrates that with enough knowledge, features can be engineered from images using handcrafted algorithms. However, the tutorial also shows that pre-trained networks such as VGG16, which were trained on large datasets, can automatically learn rich features from images with no prior knowledge. This illustrates the power of feature learning, where pre-trained models can be leveraged to extract informative features, making it a more efficient and effective method than feature engineering.

Related tutorials:
https://youtu.be/9GzfUzJeyi0
https://youtu.be/IuoEiemAuIY
https://youtu.be/5ct8Yqkiioo
https://youtu.be/vgdFovAZUzM