There are a number of different AI techniques that are available for data analytics. Some of the most common include:
Machine learning: Machine learning is a type of AI that allows computers to learn without being explicitly programmed. This can be used for a variety of tasks in data analytics, such as identifying patterns in data, classifying data, and predicting future outcomes.
Natural language processing: Natural language processing (NLP) is a field of AI that deals with the interaction between computers and human language. This can be used for a variety of tasks in data analytics, such as extracting text from documents, understanding the meaning of text, and generating text.
Computer vision: Computer vision is a field of AI that deals with the ability of computers to see and understand the world around them. This can be used for a variety of tasks in data analytics, such as identifying objects in images, classifying images, and understanding the scene of an image.
Deep learning: Deep learning is a type of machine learning that uses artificial neural networks to learn from data. This can be used for a variety of tasks in data analytics, such as identifying patterns in data, classifying data, and predicting future outcomes.
Here are some examples of how techniques can be used in data analytics:
Machine learning can be used to identify patterns in data that would be difficult or impossible for humans to find. This can be used to make better decisions and predictions. For example, machine learning can be used to identify fraud in financial transactions or to predict sales or customer churn.
Natural language processing can be used to extract text from documents, understand the meaning of text, and generate text. This can be used for a variety of tasks in data analytics, such as summarizing documents, classifying documents, and generating text reports.
Computer vision can be used to identify objects in images, classify images, and understand the scene of an image. This can be used for a variety of tasks in data analytics, such as identifying products in images, classifying images of people, and understanding the scene of a traffic accident.
Deep learning can be used to identify patterns in data that are too complex for traditional machine learning algorithms to find. This can be used for a variety of tasks in data analytics, such as identifying fraud in financial transactions or to predict customer churn.
This is a friendly reminder that Machine learning is one of the mostly used techniques in AI. So, if you already know how to build machine learning models, you are already using AI technology and you should start showcasing your work.
