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Course in Artificial Intelligence and Machine Learning

At Dubai Premier Centre (DPC), we boast of an all-inclusive course in Artificial Intelligence and Machine Learning, designed to take the participants right through the foundational understanding of the core principles of AI and ML. The course is intended to enable learners to develop, implement, and optimize AI algorithms and machine learning models and equip them with the skills necessary to be highly proficient in this fast-growing area.

This Data Science course covers data science concepts, the design and optimization of AI models, and how to apply predictive analytics to real-world problems. Whether your goal is to build machine learning models, integrate AI applications into business solutions, or understand the inner details of AI tools and libraries, this course provides the perfect foundation to start or enhance a career in this field.

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  • Schedule
January 20, 2025
February 17, 2025
March 24, 2025
April 21, 2025
  • Objectives and Target Group

  • The key fundamentals of AI and ML: It should provide the basic understanding of the fundamental principles of Artificial Intelligence and Machine Learning, with an introduction to AI algorithms and key techniques used in the development of models in AI.
  • Practical Application in Machine Learning: The participant will be able to apply the skills of supervised learning, unsupervised learning, and deep learning in order to create and refine models of machine learning and to solve business problems.
  • AI for Decision-Making Optimization: The aim is to enable the participants to make use of AI in decision-making by applying predictive analytics and reinforcement learning to drive business operations, automation, and optimization.

Target Audience

  • This course targets students and practitioners who, besides developing a deeper knowledge of data science and machine learning methods, aim at acquisition of hands-on skills related to the development of AI applications.
  • IT professionals and software developers: These are people with a technical background who want to use artificial intelligence and machine learning in software applications or build an intelligent system.
  • Business professionals and decision-makers looking to understand how AI can help optimize business processes, enhance efficiency, and provide data-driven insights for strategic decision-making.
  • Researchers and academics: Academics who wish to enhance their knowledge in the field of AI algorithms and want to understand the potential of AI for industry and technology applications.
  • Professionals working in AI-driven industries: Individuals working in big data, computer vision, or any other AI-driven sectors who wish to enhance their knowledge in machine learning models and their applications in real life.

  • Content

  • Artificial Intelligence and Machine Learning Overview
    • Overview of AI and ML: Basically, to comprehend the entity of artificial intelligence and machine learning, from history and evolution to the importance of this field in industries.
    • Types of Machine Learning: Exploring supervised learning, unsupervised learning, and reinforcement learning, and understanding their differences, applications, and use cases.
    • AI and ML Algorithms: Deep Dive into Popular AI Algorithms including Decision Trees, K-means Clustering, and Support Vector Machines in Relation to the Solution of Real-World Problems.
  • Deep Learning and Neural Networks
    • Overview of Deep Learning: Deep learning is considered a sub-domain of machine learning. However, it strictly depends on neural networks, with most applications ranging from computer vision to natural language processing.
    • Building Neural Networks: Hands-on experience in designing, training, and testing basic neural networks, including multi-layer perceptrons (MLP) and convolutional neural networks.
    • Advanced Deep Learning Models: A study in depth on the advanced topics in deep learning, including autoencoders, RNNs, and LSTMs, with their respective applications in different domains.
  • Machine Learning Models and Applications
    • Model Building and Training: Feature engineering and ways of data pre-processing are shown in a way to make building and training of machine learning models practical, ensuring high performance and accuracy.
    • Model Testing and Evaluation: Learn how to assess different metrics such as the performance at accuracy, precision, recall, and F1-score of your models and techniques that model tuning may provide.
    • AI Applications: Hands-on real-world applications of AI in business, covering predictive analytics, automation, and AI for decision-making; understand how to deploy AI models to optimize business processes.
  • Data Science, Big Data, AI Tools
    • Data Preprocessing: The importance of data preprocessing within machine learning and AI: learn techniques to clean, normalize, and transform data for use in model training.
    • Big Data and AI: How Big Data and AI meet, and how large data sets are being used to train machine learning models and build intelligent systems.
    • AI Tools and Libraries: Employing widely used AI tools and libraries, such as TensorFlow, Keras, and scikit-learn in the building and optimization of models.
  • Predictive Analytics and AI for Business
    • Predictive Analytics Introduction: The use of predictive analytics techniques, learning ways to forecast trends and make data-driven decisions across diverse business sectors.
    • Business Intelligence with AI: Understand how AI-powered predictive models can help businesses make strategic decisions, improve operational efficiency, and increase profitability.
    • AI for Business Optimization: The concept of how organizations can use machine learning and AI applications to optimize workflows for cost reduction and overall improvement.
  • Ethics, Bias, and AI Responsibility
    • Ethics of AI: The ethical dimension in the area of artificial intelligence regarding bias, fairness, transparency, and accountability in developing AI models.
    • AI Bias Mitigation: Understand the techniques of how to detect and mitigate bias in machine learning models and hence the responsible use of AI across a wide range of industries.
    • Social and Professional Responsibilities: Explain the social and professional responsibilities relevant to deployment and use with AI technologies; apply in practice some ethical principles when implementing AI.
  • AI in Industry and Future Trends
    • AI in Industry: Learn how AI is being applied to transform industries such as finance, healthcare, manufacturing, and retail, and understand specific uses of AI in various sectors.
    • AI-driven Automation: Automation at different levels, from processes and decision-making to customer service with the help of AI; the effect thereof on operational efficiency.
    • Future Trends in AI and ML: Discuss reinforcement learning, AI for decision-making, and the evolution of deep learning as immediate trends coming in AI and machine learning.

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