• AI, it’s Modern History and current Implementations
• Machine Learning
• Neural Networks and Deep Learning
• The relationship between Machine Learning, Neural Networks and Deep Learning
• The theory of Artificial General Intelligence (AGI)
• Intelligent agents
• Natural Language Processing (NLP)
• Computer Vision
• AI Ethics
• AI In Robotics
• Search Algorithms
• Knowledge Representation and Reasoning (KRR)
• Explain-ability and Transparency in AI (XAI)

Welcome to course: AI Essentials: A Simple Introduction to Artificial Intelligence Technologies by MTF Institute

Course provided by MTF Institute of Management, Technology and Finance

MTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance.

MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things.

MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry, and resident of the incubator “The Fintech House of Portugal”.

MTF is present in 208 countries and has been chosen by more than 380,000 students.

Hello everyone, and welcome to our course in Artificial Intelligence (AI)! My name is Mohamed Elfateh, I have been working in the Information Technology field for over a decade. I am interested in learning about modern technologies and sharing my knowledge with others.

What is AI?
AI is a field of computer science that studies how to create machines that can process information, make decisions, and perform specific tasks.

The History of modern AI

The field of artificial intelligence (AI) has a long and rich history, dating back to the early days of computing. However, the field as we know it today was founded in 1956 at a conference at Dartmouth College in New Hampshire. This conference brought together the leading researchers in AI at the time, including Alan Turing, John McCarthy, and Marvin Minsky. The conference is credited with helping to define the field of AI and to set the agenda for future research.

Artificial Intelligence “AI” is a complex field that needs the ability of people from diverse disciplines to work together. As an example, manufacturing autonomous vehicles like self-driving cars requires the work of people from different fields, such as AI Researchers, Automotive Engineers, and Computer Vision Researchers.

AI Researchers develop algorithms that enable self-driving cars to perceive their surroundings, make decisions, and control their movements. Automotive Engineers develop the hardware and software systems that are needed to implement these algorithms, and Computer Vision Researchers develop new techniques for enabling self-driving cars to see and understand the world.

What is Machine Learning?

Machine Learning is a type of Artificial Intelligence (AI) that allows computers to learn from data without explicit programming. In other words, machine learning algorithms can recognize patterns and make predictions based on data, without getting a command. This enables computers to learn new tasks and improve their performance over time without human intervention.

Where is Machine Learning used?

Machine learning is in use by a wide range of applications, including email filtering, Social Media Personalization, Image Recognition, Speech Recognition, fraud detection, Text prediction, Product recommendation, medical diagnosis, Healthcare Personalization, Traffic Prediction.

Neural networks are a type of machine learning algorithm that is inspired by the structure and function of the human brain.

Neural networks can learn complex patterns from data.

Neural networks have been successfully used in areas such as natural language processing.

Deep Learning is a type of machine learning that uses neural networks with multiple layers.

Each layer consists of multiple nodes that can perform diverse tasks. This allows deep learning models to learn more complex patterns from data.

Deep learning has been used to achieve significant results in a wide range of tasks, including image recognition, speech recognition, and machine translation.

Who this course is for:

• No specific requirements. The course is for any individual who want to build a career in AI and data science or improve their knowledge.
• What is AI? AI is a field of computer science that studies how to create machines that can process information, make decisions, and perform specific tasks.
• The History of modern AI. The field of artificial intelligence (AI) has a long and rich history, dating back to the early days of computing. However, the field as we know it today was founded in 1956 at a conference at Dartmouth College in New Hampshire. This conference brought together the leading researchers in AI at the time, including Alan Turing, John McCarthy, and Marvin Minsky. The conference is credited with helping to define the field of AI and to set the agenda for future research.
• Artificial Intelligence “AI” is a complex field that needs the ability of people from diverse disciplines to work together. As an example, manufacturing autonomous vehicles like self-driving cars requires the work of people from different fields, such as AI Researchers, Automotive Engineers, and Computer Vision Researchers. AI Researchers develop algorithms that enable self-driving cars to perceive their surroundings, make decisions, and control their movements. Automotive Engineers develop the hardware and software systems that are needed to implement these algorithms, and Computer Vision Researchers develop new techniques for enabling self-driving cars to see and understand the world.
• What is Machine Learning? Machine Learning is a type of Artificial Intelligence (AI) that allows computers to learn from data without explicit programming. In other words, machine learning algorithms can recognize patterns and make predictions based on data, without getting a command. This enables computers to learn new tasks and improve their performance over time without human intervention.
• Where is Machine Learning used? Machine learning is in use by a wide range of applications, including email filtering, Social Media Personalization, Image Recognition, Speech Recognition, fraud detection, Text prediction, Product recommendation, medical diagnosis, Healthcare Personalization, Traffic Prediction.
• Neural networks are a type of machine learning algorithm that is inspired by the structure and function of the human brain. Neural networks can learn complex patterns from data. Neural networks have been successfully used in areas such as natural language processing.
• Deep Learning is a type of machine learning that uses neural networks with multiple layers. Each layer consists of multiple nodes that can perform diverse tasks. This allows deep learning models to learn more complex patterns from data. Deep learning has been used to achieve significant results in a wide range of tasks, including image recognition, speech recognition, and machine translation.