COURSE OVERVIEW
IT0033 : Machine Learning Basics - Understanding Supervised, Unsupervised & Reinforcement Learning

OVERVIEW
COURSE TITLE | : | IT0033 : Machine Learning Basics - Understanding Supervised, Unsupervised & Reinforcement Learning |
COURSE DATE | : | Apr 27 - May 01 2025 |
DURATION | : | 5 Days |
INSTRUCTOR | : | Dr. Abedallah Al-Oqaili |
VENUE | : | Dubai, UAE |
COURSE FEE | : | $ 5500 |
Request For Course Outline |
Course Description
This hands-on, highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops.
This course is designed to provide participants with a detailed and up-to-date overview of Machine Learning Basics - Understanding Supervised, Unsupervised and Reinforcement Learning. It covers the importance of machine learning and its applications in various industries; the types of machine learning covering supervised learning, unsupervised learning and reinforcement learning; the data preprocessing for machine learning and regression and classification in supervised learning; the structure of decision trees, entropy and information gain in decision trees, overfitting and pruning techniques as well as the advantages of random forests over decision trees; the support vector machines (SVM) and neural networks in supervised learning, model evaluation and validation techniques; and the hyperparameter tuning in supervised learning.
Further, the course will also discuss the differences between supervised and unsupervised learning including its advantages and disadvantages; the types of clustering covering hierarchical, partitioning and density-based; the similarity measures comprising of euclidean, manhattan and cosine; the dimensionality reduction techniques covering principal component analysis (PCA), t-SNE for non-linear dimensionality reduction; and the practical applications of dimensionality reduction.
During this interactive course, participants will learn the basics of reinforcement learning, markov decision processes (MDP), Q-learning algorithm and deep reinforcement learning (DRL); the challenges and limitations of reinforcement learning, the concept and benefits of transfer learning; pretrained models in supervised learning and transfer learning in reinforcement learning; and the generative models, explainability and interpretability in ML models and deploying machine learning models.
link to course overview PDF
This course is designed to provide participants with a detailed and up-to-date overview of Machine Learning Basics - Understanding Supervised, Unsupervised and Reinforcement Learning. It covers the importance of machine learning and its applications in various industries; the types of machine learning covering supervised learning, unsupervised learning and reinforcement learning; the data preprocessing for machine learning and regression and classification in supervised learning; the structure of decision trees, entropy and information gain in decision trees, overfitting and pruning techniques as well as the advantages of random forests over decision trees; the support vector machines (SVM) and neural networks in supervised learning, model evaluation and validation techniques; and the hyperparameter tuning in supervised learning.
Further, the course will also discuss the differences between supervised and unsupervised learning including its advantages and disadvantages; the types of clustering covering hierarchical, partitioning and density-based; the similarity measures comprising of euclidean, manhattan and cosine; the dimensionality reduction techniques covering principal component analysis (PCA), t-SNE for non-linear dimensionality reduction; and the practical applications of dimensionality reduction.
During this interactive course, participants will learn the basics of reinforcement learning, markov decision processes (MDP), Q-learning algorithm and deep reinforcement learning (DRL); the challenges and limitations of reinforcement learning, the concept and benefits of transfer learning; pretrained models in supervised learning and transfer learning in reinforcement learning; and the generative models, explainability and interpretability in ML models and deploying machine learning models.
TRAINING METHODOLOGY
This interactive training course includes the following training methodologies:
LecturesPractical Workshops & Work Presentations
Hands-on Practical Exercises & Case Studies
Simulators (Hardware & Software) & Videos
In an unlikely event, the course instructor may modify the above training methodology for technical reasons.
VIRTUAL TRAINING (IF APPLICABLE)
If this course is delivered online as a Virtual Training, the following limitations will be applicable:
Certificates | : | Only soft copy certificates will be issued |
Training Materials | : | Only soft copy materials will be issued |
Training Methodology | : | 80% theory, 20% practical |
Training Program | : | 4 hours per day, from 09:30 to 13:30 |
RELATED COURSES

IT0016 : AI Natural Language Processing
- Date: Jun 15 - Jun 19 / 3 Days
- Location: Dubai, UAE
- Course Details Register

IT0019 : How to Build Your Own Chatbot Using Python
- Date: Jun 30 - Jul 04 / 3 Days
- Location: Abu Dhabi, UAE
- Course Details Register

IT0037 : Machine Translation
- Date: Jul 06 - Jul 10 / 3 Days
- Location: Dubai, UAE
- Course Details Register

IT0036 : Big Data & AI
- Date: Jun 23 - Jun 27 / 3 Days
- Location: Abu Dhabi, UAE
- Course Details Register