AI MeOut

Course 2

AI Terminology

Build a solid vocabulary for AI: data, algorithms, machine learning, deep learning, natural language processing and computer vision.

5 modules4h 20mFree
Start module I

Modules

What's inside

  1. I

    Introduction to AI Terminology

    The abbreviation "AI" is used to refer to "Artificial Intelligence" in English. Artificial Intelligence is a field of computer science that deals with the development of computer systems and programs capable of performing tasks that would otherwise require human intelligence. These tasks may include learning, pattern recognition, speech processing, planning, decision-making, and many others. Artificial Intelligence is divided into several subfields, such as Machine Learning, Deep Learning, Natural Language Processing, and many more. The goal of AI is to create systems that can automate tasks, analyze data, and even adapt to new information and situations, much like a human would, but with the speed and performance of a computer.

  2. II

    Data and Algorithms

    Introduction: Data and algorithms are key components of Artificial Intelligence (AI) that enable AI systems to learn, recognize patterns, and make decisions. Here is the theory about data and algorithms in the context of AI: Data in AI: Definition: Data in AI refers to information or facts that serve as input to AI algorithms. These data can include text, images, sounds, time series, and other formats. Importance of Data: Data is crucial for AI because they enables machines to learn and perform tasks. The more high-quality and diverse data an AI system has access to, the better it can recognize patterns and make accurate predictions.

  3. III

    Machine Learning and Deep Learning

    Introduction:

  4. IV

    Natural Language Processing (NLP)

    Natural Language Processing (NLP), often referred to as Natural Language Understanding (NLU), is a subfield of Artificial Intelligence (AI) that deals with the interaction between computers and human language. The goal of NLP is to enable computers to understand, interpret, and generate human language in its natural form. The main tasks of NLP include: Entity Recognition: Identifying and classifying entities, such as names, places, dates, and more, in text. For example, recognizing a person's name or a location in a news article. Sentiment Analysis: Evaluating the emotional mood or attitude expressed in text, whether it is positive, negative, or neutral. It is used for analyzing reviews, social media comments, and more. Entity Linking: Identifying and classifying entities in text based on their meaning and relationships. This may involve recognizing individuals, organizations, places, and other entities. Speech Processing: Converting speech to text or text to speech. This task is used in speech recognition, generating captions, and voice assistants. Machine Translation: Translating text from one language to another. Examples include automatic translators and software localization. Pattern Recognition: Identifying and extracting patterns or templates in text, such as dates, numbers, email addresses, and more.

  5. V

    Computer Vision and Image Processing

    Computer vision and image processing are fields of computer science and artificial intelligence that deal with the analysis and interpretation of visual data, such as photographs, images, videos, and 3D scenes. These areas have wide-ranging applications and are fundamental technologies in many applications. Here is an overview of computer vision and image processing.