Artificial Intelligence and Machine Learning

Course Introduction

The Artificial Intelligence and Machine Learning course is designed to provide trainees with a comprehensive introduction to the theory, concepts, and practical applications of AI and ML. The course covers a broad range of topics, including supervised, unsupervised, and reinforcement learning, neural networks, deep learning, natural language processing, and computer vision. The course begins by introducing the basics of machine learning, including the different types of machine learning algorithms, such as linear regression, logistic regression, decision trees, and support vector machines. Students learn how to select the appropriate algorithm for a given problem, how to train and evaluate machine-learning models, and how to use machine learning to solve real-world problems. The course also explores advanced topics such as neural networks, deep learning, and reinforcement learning. Participants learn how to build and train these models using popular machine learning libraries and frameworks such as TensorFlow, PyTorch, and sci-kit-learn. Additionally, the course covers ethical and societal issues related to the development and deployment of AI and ML models. Trainees learn about the potential biases associated with machine learning models and how to design models that are fair, transparent, and unbiased.

Objectives

• Understand the fundamental concepts and techniques of Artificial Intelligence (AI) and Machine Learning (ML) and their applications.
• Be familiar with various types of machine learning algorithms, such as supervised, unsupervised, reinforcement learning, and their applications.
• Develop the skills to build, train, and evaluate machine-learning models using popular machine learning libraries and frameworks.
• Gain an understanding of the mathematical foundations of machine learning, including linear algebra, calculus, and probability theory.
• Develop the ability to analyze and interpret machine learning models and results, including model accuracy, overfitting, and under fitting.
• Understand the ethical implications and potential biases associated with developing and deploying AI and ML models.
• Explore cutting-edge research and future directions in the field of AI and ML.

Daily Topics

For registration, course outline & more information please contact

NAYEL Training Centre 

Tel: +971 45587735 | Mob: +971 50 249 6876 | WhatsApp: +971 50 249 6876

Email:  [email protected]