AI Concepts & Terminology Course

AI Concepts & Terminology by Oxford Training Centre introduces the very important basics of Artificial Intelligence and Deep Learning. This one-week course is designed for professionals, students, and enthusiasts wanting to deeply understand the foundations of AI: Neural Networks, Artificial Neural Networks, and Deep Learning Algorithms. Participants will learn basic terminologies and concepts like supervised and unsupervised learning, CNN, RNN, and advanced deep learning methods in use in most modern AI applications.

Objectives and target group

Objectives

By the end of the AI Concepts & Terminology course, participants will be able to:

1. Understand the key concepts and terminologies of AI and Deep Learning, including Neural Networks and Artificial Neural Networks (ANN).
2. Develop proficiency in using deep learning frameworks such as TensorFlow and Keras for building AI models.
3. Learn about the various types of deep learning models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
4. Understand the distinction between supervised and unsupervised learning and their applications in deep learning.
5. Gain hands-on experience in creating and training deep learning models for practical applications.
6. Explore the applications of AI and deep learning in areas such as computer vision, natural language processing (NLP), and big data.
7. Master AI fundamentals and understand how deep learning fits into the broader field of artificial intelligence and machine learning.
8. Apply deep learning techniques to real-world problems using Python for deep learning projects.

Target Group

This course is designed for a wide range of individuals:

• AI Professionals: Those working in AI and machine learning can refine their understanding of deep learning concepts and explore advanced techniques.
• Students and Beginners: Individuals with little to no experience in deep learning will benefit from an accessible introduction to key AI concepts, neural networks, and deep learning algorithms.
• Non-Coders: If you are interested in understanding AI solutions but do not have a coding background, this course provides an introduction to deep learning that is easy to follow.
• Data Scientists and Engineers: Professionals in data science can enhance their skills by learning how to use deep learning models and frameworks such as TensorFlow and Keras to solve real-world problems.
• Researchers and Technologists: Those looking to explore deep learning for advanced applications in computer vision, NLP, and more will gain valuable insights from this course.

Course Content

The AI Concepts & Terminology course offers a comprehensive exploration of deep learning and its many applications in AI. The course content is structured as follows:

1. Introduction to Artificial Intelligence and Deep Learning
   • Overview of Artificial Intelligence (AI) and its evolution.
   • Introduction to Deep Learning and its relationship with machine learning.
   • Key differences between AI, machine learning, and deep learning.
2. Neural Networks and Artificial Neural Networks (ANN)
   • What are neural networks and how do they mimic the human brain?
   • Introduction to artificial neural networks (ANN) and their basic architecture.
   • How ANNs are used in deep learning models for various applications.
3. Deep Learning Algorithms and Techniques
   • Understanding the algorithms behind deep learning.
   • Overview of deep learning models: CNN, RNN, and more.
   • How deep learning techniques are applied in computer vision and NLP.
4. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
   • Introduction to CNN and its applications in image recognition.
   • Understanding RNN for time-series analysis and sequence processing.
5. Training Deep Learning Models
   • How to train deep learning models with TensorFlow and Keras.
   • Exploring different types of learning: supervised and unsupervised learning.
   • Hands-on deep learning tutorials to reinforce learning.
6. AI and Deep Learning Applications
   • Applications of deep learning in real-world scenarios such as computer vision and NLP.
   • How deep learning models are applied to big data for smarter AI solutions.
   • Exploring deep learning applications in AI solutions for industries like healthcare, finance, and tech.
7. Deep Learning Frameworks and Tools
   • Introduction to TensorFlow, Keras, and other popular deep learning frameworks.
   • Hands-on exercises with Python for deep learning model creation.
   • Exploring the role of Python in AI solutions and how it helps in building deep learning models.
8. Advanced Deep Learning Topics
   • Exploring advanced deep learning techniques and architectures for experienced learners.
   • Understanding how deep learning can be used in complex tasks like natural language processing (NLP) and computer vision.
   • The future of deep learning and its potential applications in emerging technologies.