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COURSE OVERVIEW

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

TRAINING METHODOLOGY

This interactive training course includes the following training methodologies:

Lectures
Practical 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

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