- Machine Learning (ML) is the foundation of all these concepts. ML is a subset of AI that enables computers to learn from data and make decisions on their own.
- Deep Learning (DL) is a type of ML that uses neural networks to analyze data. DL is a key technique used in many AI applications, including Natural Language Processing (NLP) and Computer Vision.
- Natural Language Processing (NLP) and Computer Vision are both applications of ML and DL. NLP enables computers to understand and generate human language, while Computer Vision allows computers to interpret and understand visual data from images and videos.
- Big Data is the fuel that powers ML, DL, NLP, and Computer Vision. Large amounts of data are needed to train AI models, and Big Data provides the necessary infrastructure to store, process, and analyze this data.
- ML is the overall framework
- DL is a technique used within ML
- NLP and Computer Vision are applications of ML and DL
- Big Data is the foundation that enables the collection, storage, and analysis of large amounts of data, which is necessary for ML, DL, NLP, and Computer Vision to work effectively.
To illustrate the relationships between these concepts, consider the following example:
- Netflix uses ML to recommend movies and TV shows based on your viewing history. To do this, Netflix needs to analyze large amounts of data (Big Data) on your viewing habits.
- Google Photos uses DL to recognize faces and objects in your photos. This is an example of Computer Vision, which is an application of ML and DL.
- Siri and Google Assistant use NLP to understand your voice commands and respond accordingly. This is an example of NLP, which is an application of ML and DL.