Machine Learning Training in Riyadh

Machine Learning Training in Riyadh, Saudi Arabia

Learn to develop predictive models and uncover patterns in data using machine learning algorithms.

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Machine Learning Training is a professional training program delivered by ProgNXT in Riyadh, Saudi Arabia. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Machine Learning Training course with hands-on, instructor-led sessions. Machine Learning training equips participants with the knowledge and skills to develop algorithms that enable computers to learn and make predictions...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PRO03
21 Hrs
Last Updated: April 2026

Course Rating5/5

Machine Learning training equips participants with the knowledge and skills to develop algorithms that enable computers to learn and make predictions from data. The course covers essential topics such as supervised and unsupervised learning, regression, classification, clustering, and neural networks, using tools like Python, Scikit-learn, and TensorFlow. Participants learn to preprocess data, select appropriate models, evaluate performance metrics, and optimize algorithms for real-world applications. Hands-on exercises involve building and deploying machine learning models for tasks such as recommendation systems, fraud detection, and predictive analytics. By the end of the training, attendees will be proficient in applying machine learning techniques to solve complex problems and drive data-driven innovations.

Basic programming knowledge (preferably Python)
Familiarity with basic mathematical concepts like linear algebra, probability, and statistics

Upon completing the Machine Learning course, participants will:

Understand core machine learning concepts and algorithms.
Gain practical experience with supervised, unsupervised, and reinforcement learning.
Develop skills to preprocess data for effective model training.
Build, evaluate, and optimize machine learning models.
Apply machine learning techniques to real-world problems.

Data Preprocessing: Ability to clean, preprocess, and transform raw data for analysis.
Supervised Learning Mastery: Proficiency in algorithms like linear regression, decision trees, and support vector machines.
Unsupervised Learning: Skills to implement clustering and dimensionality reduction techniques.
Model Evaluation: Expertise in using metrics to assess model performance and refine models.
Advanced Techniques: Hands-on experience with ensemble methods, hyperparameter tuning, and deep learning integration.

Participants will be assessed on:

Algorithm Understanding: Knowledge of supervised and unsupervised learning algorithms.
Data Preprocessing: Ability to prepare and clean datasets for analysis.
Model Development: Proficiency in designing and optimizing machine learning models.
Real-World Application: Skills to solve practical problems using machine learning techniques.

Certificate of Completion: Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion for the Machine Learning Training course.

Foundations of Machine Learning

Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Applications in various industries

Data Preprocessing and Feature Engineering

Handling missing data and outliers
Feature scaling and encoding
Practical exercise: Preparing a dataset for training

Introduction to Supervised Learning

Linear regression and logistic regression
Evaluation metrics: RMSE, R^2, accuracy, precision, and recall
Practical exercise

Advanced Machine Learning Techniques

Unsupervised Learning

Clustering techniques: K-means, hierarchical clustering
Dimensionality reduction with PCA
Practical exercise: Implementing clustering and PCA on datasets

Ensemble Methods

Bagging, boosting, and random forests
Introduction to Gradient Boosting and XGBoost
Practical exercise: Comparing ensemble models

Hyperparameter Tuning

Grid search and randomized search
Cross-validation techniques
Practical exercise

Real-World Applications and Deployment

Introduction to Reinforcement Learning

Basics of Q-learning and Markov Decision Processes
Practical applications of reinforcement learning
Practical exercise: Understanding RL concepts through simulations

End-to-End Model Development

Building a machine learning pipeline
Handling imbalanced datasets
Practical exercise: Developing an end-to-end ML project

Model Deployment and Integration

Saving and exporting models
Deployment using Flask and cloud platforms
Practical exercise: Deploying a machine learning model

Hands-On Exercises

Solving a real-world problem using multiple ML techniques
Presenting the solution and results

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants will be evaluated based on: Daily quizzes, mock questions and query resolution. Group Discussion and Case Study Discussion. Assessment will be through hands-on labs, quizzes, and a hands-on exercises. ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

Machine Learning Training Events in saudi-arabia

Online Events

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More Upcoming Session Dates

November 2026

📍 Riyadh, Saudi Arabia

6th Nov 2026→8th Nov 2026

🌐 Online Instructor-Led

10th Nov 2026→12th Nov 2026

📍 Riyadh, Saudi Arabia

23rd Nov 2026→25th Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

📍 Riyadh, Saudi Arabia

3rd Dec 2026→5th Dec 2026

🌐 Online Instructor-Led

7th Dec 2026→9th Dec 2026

📍 Riyadh, Saudi Arabia

28th Dec 2026→30th Dec 2026

🌐 Online Instructor-Led

28th Dec 2026→30th Dec 2026

Events for other courses in Riyadh, Saudi Arabia

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Machine Learning Training in Riyadh