Artificial Intelligence is broader than both Machine Learning and NLP. Machine Learning is a mechanism for building intelligence into a machine so that it can search patterns and make predictions from raw data. ML is the use of algorithms tolearn from the data and them making some prediction.
is one kind of machine learning that’s very popular now. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
Natural language processing (NLP)
It is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.
Applications of NLP
- Machine Translation
- Fighting Spam
- Information Extraction
- Question Answering
Machine Learning (or ML) is an area of Artificial Intelligence (AI) that is a set of statistical techniques for problem solving. These techniques can be applied to a wide variety of problems which are not limited to – vision based research, fraud detection, price prediction, and even NLP. In order to apply ML techniques to NLP problems, we need to usually convert the unstructured text into a structured format.
Applications of Machine Learning
- Virtual Personal Assistants
- Predictions while Commuting
- Videos Surveillance
- Social Media Services
- Email Spam and Malware Filtering
- Online Customer Support
- Search Engine Result Refining
- Product Recommendations
- Online Fraud Detection
Deep Learning (which includes Recurrent Neural Networks, Convolution neural Networks and others) is a type of Machine Learning approach. It is an extension of Neural Networks. Deep Learning is used quite extensively for vision based classification (e.g. distinguishing images of airplanes from images of dogs). Deep Learning can be used for NLP tasks as well. However it is important to note that Deep Learning algorithms do not exclusively deal with text.
Applications of Machine Learning
- Colorization of Black and White Images.
- Adding Sounds To Silent Movies.
- Automatic Machine Translation.
- Object Classification in Photographs.
- Automatic Handwriting Generation.
- Character Text Generation.
- Image Caption Generation.
- Automatic Game Playing.
Relationship between NLP, ML and Deep Learning
The image below shows graphically how NLP is related ML and Deep Learning.
Deep Learning is one of the techniques in the area of Machine Learning – there are several other techniques such as Regression, K-Means, and so on.
ML and NLP have some overlap, as Machine Learning is often used for NLP tasks. LDA (Latent Dirichlet Allocation which is a Topic Modeling Algorithm) is one such example of unsupervised machine learning.
However, NLP has a strong linguistics component (not represented in the image), that requres an understanding of how we use language. The art of understanding language involves understanding humor, sarcasm, subconscious bias in text, etc. Once we can understand that is means to to be sarcastic (yeah right!) we can encode it into a machine learning algorithm to automatically discover similar patterns for us statistically.
To summarize, in order to do any NLP, you need to understand language. Language is different for different genres (research papers, blogs, twitter have different writing styles), so there is a strong component of looking at your data manually to get a fell of what it is trying to say to you, and how you- as a human would analyze it. Once you figure out what you are doing as a human reasoning system (ignoring hash tags, using smiley faces to imply sentiment), you can use a relevant ML approach to automate that process and scale it.