Machine learning (ML) is a subfield of AI that focuses on developing algorithms and models that enable computers to learn from data and experience, performing tasks without explicit programming. The core idea behind machine learning is that systems can automatically improve their performance as they accumulate more experience.
For a machine to make decisions, it needs three components:
- Algorithm: A set of instructions for the computer, specifying what to do and how to use the data. For example, a piece of software we write to categorize different types of clothing: "T-shirts", "Shoes", "Pants".
- Dataset: The examples on which the machine learns. These can be images, videos, texts, and more. In the case of clothing, we would need many pictures of different types of apparel. The more examples there are, the better, as it builds up experience, similar to how humans learn from experience.
- Features: These are the attributes the machine pays attention to when making decisions. If we are teaching the machine with supervision, we define the features ourselves, such as the type of clothing and its category. If the learning is unsupervised, we feed all the data to the machine, and it looks for patterns on its own. We can intervene if necessary to help it understand or correct its conclusions.
Deep learning is a subset of machine learning that relies on neural networks. It's particularly useful in image processing, speech recognition, and even autonomous vehicle development.
For instance, an automobile factory working on autonomous cars that can adapt to road conditions and the behavior of other vehicles uses deep learning techniques.
Typical applications include:
- Computer Vision: Object detection, image segmentation, face recognition, etc.
- Natural Language Processing (NLP): Tasks like automatic translation, sentiment analysis, and text generation.
- Recommendation Systems: Providing personalized suggestions in online stores and streaming platforms.
Natural Language Processing (NLP)NLP is an AI discipline that empowers computers to analyze and understand human language. In business, it is utilized for automating customer interactions, analyzing reviews, and generating content.
An example is chatbots that communicate with customers in natural language, answering their questions and improving overall customer service.