course | Advanced Machine Learning and Artificial Intelligence
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DIGTR-1551 | Advanced Machine Learning and Artificial Intelligence
Course Sector : Digital Transformation and Innovation
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 2025-12-14 | 2025-12-18 | El Doha | $4,250 | Book now |
Course Introduction
In this comprehensive 5-day training course, participants will delve into the fascinating world of AI and equip them with the knowledge and tools to excel in this rapidly evolving domain. Throughout the program, participants will cover the fundamental principles and advanced techniques of machine learning, focusing on neural networks, deep learning, natural language processing, and reinforcement learning. By the end of the course, they will have hands-on experience with building and training state-ofthe-art AI models, and will be prepared to tackle real-world challenges in diverse industries.Moreover, they will examine the ethical considerations surrounding AI development, ensuring that they are well-versed in creating responsible and unbiased AI solutions
Course objective
- Develop a comprehensive understanding of advanced machine learning and artificial intelligence concepts, techniques, and algorithms.
- Gain hands-on experience in building and training sophisticated neural networks for image recognition, natural language processing, and reinforcement learning applications.
- Learn to implement state-of-the-art models, such as Convolutional Neural Networks
- (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and more.
- Explore ethical considerations and best practices in AI development to ensure responsible and unbiased AI solutions.
- Acquire the skills to address real-world challenges and stay up-to-date with future trends
- in the ever-evolving field of AI.
Course Outline | Day 01
Foundations of Machine Learning
- Introduction to Advanced Machine Learning and Artificial Intelligence
- Review of Machine Learning Basics (Supervised, Unsupervised, ReinforcementLearning)
- Advanced Regression Techniques (Ridge, Lasso, Elastic Net)
- Decision Trees and Ensemble Methods (Random Forests, Gradient Boosting)
- Introduction to Neural Networks and Deep Learning
- Optimization Algorithms for Deep Learning (Gradient Descent, Adam, RMSprop)
- Hands-on Session: Implementing a Neural Network from Scratch
Course Outline | Day 02
Advanced Deep Learning
- Convolutional Neural Networks (CNNs) for Image Recognition
- Transfer Learning and Fine-Tuning Pre-trained Models
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
- Generative Adversarial Networks (GANs) for Image Generation
- Variational Autoencoders (VAEs) for Data Generation and Compression
- Advanced Activation Functions (ReLU, Leaky ReLU, Swish, etc.)
- Hands-on Session: Building and Training a CNN for Image Classification
Course Outline | Day 03
Natural Language Processing (NLP)
• Introduction to Natural Language Processing and Text Preprocessing
• Word Embeddings (Word2Vec, GloVe) for Text Representation
• Recurrent Neural Networks for Sequential Data in NLP
• Sequence-to-Sequence Models and Applications (Machine Translation, Text Summarization)
• Attention Mechanisms for Improved NLP Models (Transformer Architecture)
• Sentiment Analysis and Text Classification
• Hands-on Session: Building an NLP Model for Sentiment Analysis
Course Outline | Day 04
Reinforcement Learning and Robotics
• Introduction to Reinforcement Learning (RL) and Markov Decision Processes (MDPs)
• Policy Gradient Methods (REINFORCE, Proximal Policy Optimization - PPO)
• Deep Q-Networks (DQNs) and Deep RL Techniques
• Actor-Critic Methods for Continuous Action Spaces
• Multi-Agent Reinforcement Learning
• Robotics and AI: Applications of RL in Robotics
• Hands-on Session: Implementing a Reinforcement Learning Agent for a Simple Game
Course Outline | Day 05
Reinforcement Learning and Robotics
• Introduction to Reinforcement Learning (RL) and Markov Decision Processes (MDPs)
• Policy Gradient Methods (REINFORCE, Proximal Policy Optimization - PPO)
• Deep Q-Networks (DQNs) and Deep RL Techniques
• Actor-Critic Methods for Continuous Action Spaces
• Multi-Agent Reinforcement Learning
• Robotics and AI: Applications of RL in Robotics
• Hands-on Session: Implementing a Reinforcement Learning Agent for a Simple Game
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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