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Machine Learning Course in Qatar

A humanoid robot sitting at a desk in a library, reading an open book.
Edoxi’s 60-hour Machine Learning Course in Qatar develops your skills to build and deploy intelligent systems using Python. The course covers foundational Python programming and core machine learning concepts, including regression, classification, clustering, and deep learning. You will learn to use industry-standard tools like Scikit-learn, TensorFlow, Keras, NumPy, and Pandas. The training includes hands-on projects like predictive modelling and customer segmentation. Engage in live coding sessions, debugging exercises, and group activities. Complete the course and gain a recognised Machine Learning Certification. Enrol and grow your AI and machine learning career in Qatar’s booming IT sector. 
Course Duration
60 Hours
Corporate Days
5 Days
Learners Enrolled
20+
Modules
18
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Course Rating
4.9
star-rating-4.9
Mode of Delivery
Online
Classroom
Certification by

What You’ll Learn from the Machine Learning Training in Qatar?

Build a Solid Python Foundation for Machine Learning
You will master fundamental Python skills, including syntax, data structures, and key libraries to support your progression in machine learning.
Learn Core Machine Learning Concepts
You will develop a comprehensive understanding of algorithms such as regression, classification, and clustering, essential for both supervised and unsupervised learning.
Become Proficient in Data Processing Techniques
You will gain skills in data cleaning, transformation, and manipulation using powerful Python libraries like Pandas and NumPy.
Learn Model Development with Scikit-learn
You will learn to build, train, and optimise machine learning models using Scikit-learn, tailored for practical and industry-relevant applications.
Master Advanced Data Visualisation Skills
You will create compelling and meaningful visualisations with Matplotlib and Seaborn to communicate analytical insights.
Develop Expertise in Performance Evaluation
You will master the ability to validate, measure, and fine-tune machine learning model performance to ensure precision in real-world use cases.

About Our Machine Learning Course in Qatar

Edoxi’s 60-hour Machine Learning course in Qatar is designed to equip both beginners and professionals with Essential Python skills to build and train machine learning models. The course is ideal for those aiming to advance in the fields of Artificial Intelligence (AI) and predictive analytics. It offers a balanced combination of theoretical knowledge and hands-on training, enabling learners to develop practical machine learning solutions that address real-world business challenges.

Our Machine Learning course in Qatar covers a wide range of topics, including fundamental Python programming, core machine learning algorithms, and Natural Language Processing (NLP). You will also be introduced to deep learning techniques using industry-standard tools and libraries such as Scikit-learn, TensorFlow, and Keras. To strengthen practical understanding, you will gain experience working with Jupyter Notebook, NumPy, and Pandas. These tools are widely used in the data science and AI community.

A standout feature of Edoxi’s Machine Learning training in Qatar is its focus on real-world applications. The course includes hands-on projects such as predictive modelling and customer segmentation, which provide valuable industry exposure across various sectors, including IT, banking, healthcare, and e-commerce. Interactive sessions involving code walkthroughs, live debugging, and collaborative problem-solving further enhance the learning experience. These exercises are designed to build your expertise in data preprocessing, model optimisation, and advanced machine learning techniques.

To accommodate diverse learning preferences, Edoxi offers the Machine Learning course in both classroom and online formats, allowing you to choose the schedule that best fits your needs. For organisations, we offer a 5-day intensive corporate training program that can be tailored to specific business requirements. Upon completing the expert-led sessions, you will receive a Machine Learning certification that adds value to your professional profile and improves employability in Qatar’s fast-evolving AI landscape.

Enrolling in Edoxi’s Machine Learning course in Qatar is a strategic step toward launching or accelerating your career in AI and machine learning. With a strong curriculum, practical experience, and flexible training options, our Machine Learning training in Qatar is your gateway to becoming a skilled and confident machine learning professional.

Features of the Machine Learning Course in Qatar

Comprehensive AI Curriculum

You will master Python, Machine Learning, NLP, and Deep Learning techniques to solve complex problems with practical, hands-on applications.

Cutting-Edge Development Environment

You will practice coding in Jupyter Notebook and Spyder. Write, debug, and test machine learning solutions with expert guidance.

Proficiency in Data Science Libraries

You will gain expertise in NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, numerical analysis, and advanced visualisation.

Real-World Project Experience

You will work on industry-relevant projects like price prediction and customer segmentation to build a strong professional portfolio.

Engaging Learning Experience

You will participate in live coding, peer discussions, and group projects, applying TensorFlow and Scikit-learn to practical scenarios.

Personalised Training with Flexible Schedules

Our course training aligns with your career goals. You can choose from weekend, evening, or hybrid schedules accordingly.

Industry-experienced Trainers

Learn from certified professionals and AI practitioners who bring real-world insights, mentorship, and up-to-date knowledge into the classroom.

Get hands-on with real-world projects

You will apply your knowledge to real-world case studies and projects, and build a strong portfolio for career advancement.

Who Can Join Our Machine Learning Training in Qatar?

Python and Machine Learning Beginners

Individuals with no programming experience, eager to start careers in Python programming and machine learning.

Students and Fresh Graduates

Aspiring professionals with basic programming skills to enter AI and machine learning roles.

IT Experts and System Administrators

Tech professionals transitioning to machine learning, leveraging their technical foundation to master algorithms and analytics.

Software Engineers and Developers

Programmers specialising in machine learning, enabling seamless integration of algorithms using Python.

E-commerce and Financial Analysts

Professionals handling large datasets to utilise machine learning for predictive analytics and customer insights.

Cybersecurity Specialists and Ethical Hackers

Professionals applying machine learning to improve threat detection and vulnerability analysis.

Python Machine Learning Course Modules

Module 1: Introduction to Python
  • Chapter 1.1: Overview of Python and Its Features

    • Lesson 1.1.1: Introduction to Python programming
    • Lesson 1.1.2: Features and benefits of Python
    • Lesson 1.1.3: Setting up the Python development environment
  • Chapter 1.2: Python Basics

    • Lesson 1.2.1: Python syntax and basic structure
    • Lesson 1.2.2: Writing and running Python programs
Module 2: Variables, Data Types, and Operators
  • Chapter 2.1: Working with Variables and Data Types

    • Lesson 2.1.1: Understanding numbers, strings, and booleans
    • Lesson 2.1.2: Type conversion and casting
  • Chapter 2.2: Python Operators

    • Lesson 2.2.1: Arithmetic and assignment operators
    • Lesson 2.2.2: Logical and comparison operators
Module 3: Control Flow and Loops
  • Chapter 3.1: Conditional Statements

    • Lesson 3.1.1: If, else, and elif statements
    • Lesson 3.1.2: Nested conditions
  • Chapter 3.2: Looping Structures

    • Lesson 3.2.1: For and while loops
    • Lesson 3.2.2: Breaking and continuing loop execution
  • Chapter 3.3: Iterating with Lists

    • Lesson 3.3.1: Iterating over lists and collections
Module 4: Functions and Modules
  • Chapter 4.1: Functions in Python

    • Lesson 4.1.1: Creating and invoking functions
    • Lesson 4.1.2: Function parameters and return values
    • Lesson 4.1.3: Variable scope and global keyword
  • Chapter 4.2: Python Modules

    • Lesson 4.2.1: Importing and using modules
    • Lesson 4.2.2: Creating custom modules
Module 5: Strings and File Handling
  • Chapter 5.1: Manipulating Strings

    • Lesson 5.1.1: Concatenation, slicing, and formatting strings
  • Chapter 5.2: File Operations

    • Lesson 5.2.1: Reading from and writing to files
    • Lesson 5.2.2: Opening, closing, and deleting files
    • Lesson 5.2.3: File permissions and error handling
Module 6: Data Structures
  • Chapter 6.1: Core Data Structures in Python

    • Lesson 6.1.1: Working with lists, tuples, and dictionaries
    • Lesson 6.1.2: Accessing and modifying elements
  • Chapter 6.2: Advanced Data Structures

    • Lesson 6.2.1: Working with sets and frozensets
Module 7: Object-Oriented Programming (OOP) in Python
  • Chapter 7.1: Introduction to OOP

    • Lesson 7.1.1: Understanding classes, objects, and methods
  • Chapter 7.2: Advanced OOP Concepts

    • Lesson 7.2.1: Encapsulation, inheritance, and polymorphism
    • Lesson 7.2.2: Working with objects and class relationships
Module 8: File Input/Output (I/O) and Serialisation
  • Chapter 8.1: File I/O Basics

    • Lesson 8.1.1: Reading and writing data to/from files
  • Chapter 8.2: Data Serialisation

    • Lesson 8.2.1: Serialisation and deserialization of objects (pickle, JSON, CSV)
Module 9: Regular Expressions
  • Chapter 9.1: Introduction to Regular Expressions

    • Lesson 9.1.1: Basics of pattern matching
    • Lesson 9.1.2: Using regex for validating and manipulating text
Module 10: Introduction to Python Libraries and Frameworks
  • Chapter 10.1: Popular Python Libraries

    • Lesson 10.1.1: Overview of libraries like NumPy, Pandas, and others
  • Chapter 10.2: Data Analysis and Web Development

    • Lesson 10.2.1: Introduction to data analysis and scientific computing
    • Lesson 10.2.2: Web development with Python
Module 11: Working with Data
  • Chapter 11.1: Data Analysis with Pandas

    • Lesson 11.1.1: Understanding dataframes
    • Lesson 11.1.2: Creation, manipulation, and analysis of data
  • Chapter 11.2: Data Visualisation

    • Lesson 11.2.1: Introduction to visualisation with matplotlib
Module 12: Introduction to Artificial Intelligence
  • Chapter 12.1: Fundamentals of AI

    • Lesson 12.1.1: Definition and history of AI
    • Lesson 12.1.2: Types of AI: Narrow AI vs General AI
    • Lesson 12.1.3: Applications of AI in various industries
Module 13: Introduction to Machine Learning
  • Chapter 13.1: Basics of Machine Learning

    • Lesson 13.1.1: Supervised and unsupervised learning
    • Lesson 13.1.2: Data preparation and preprocessing
  • Chapter 13.2: Data Cleaning

    • Lesson 13.2.1: Techniques for cleaning and handling missing data
    • Lesson 13.2.2: Normalisation and standardisation
Module 14: Supervised Learning - Regression
  • Chapter 14.1: Regression Techniques

    • Lesson 14.1.1: Simple and multiple linear regression
    • Lesson 14.1.2: Polynomial and decision tree regression
    • Lesson 14.1.3: Random forest regression
  • Chapter 14.2: Model Evaluation

    • Lesson 14.2.1: Regression model selection and evaluation
Module 15: Supervised Learning - Classification
  • Chapter 15.1: Classification Algorithms

    • Lesson 15.1.1: Logistic regression
    • Lesson 15.1.2: K-nearest neighbour (k-NN)
  • Chapter 15.2: Model Selection and Evaluation

    • Lesson 15.2.1: Classification model evaluation techniques
Module 16: Unsupervised Learning - Clustering
  • Chapter 16.1: Clustering Basics

    • Lesson 16.1.1: Introduction to clustering
    • Lesson 16.1.2: k-means and hierarchical clustering
Module 17: Natural Language Processing (NLP)
  • Chapter 17.1: Introduction to NLP

    • Lesson 17.1.1: Types of NLP and classical vs deep learning models
    • Lesson 17.1.2: NLP in Python
Module 18: Deep Learning
  • Chapter 18.1: Neural Networks

    • Lesson 18.1.1: Artificial and Convolutional Neural Networks

Download Machine Learning Course Brochure

Real-World Projects in Machine Learning Course

Edoxi offers hands-on activities and projects tailored for beginners who want to learn Python from scratch, specifically with a focus on machine learning. For those who already have a foundation in Python and wish to dive directly into machine learning, we provide a separate set of specialised projects. The following are some of the projects you will be involved in;

Projects

  • House Price Forecasting

    ou will build a linear regression model to predict housing prices based on features like size, location, and amenities, mastering supervised learning techniques.

  • Customer Behaviour Segmentation

    You will utilise K-Means clustering to categorise customer data by purchasing patterns, gaining practical experience in unsupervised learning algorithms.

  • Titanic Dataset Survival Analysis

    You will develop a logistic regression model to predict survival probabilities, applying classification techniques to real-world datasets.

  • Collaborative Machine Learning

    You will engage in peer reviews and group tasks to design, implement, and refine machine learning models collaboratively.

  • Advanced Data Visualisation

    You get to create impactful visualisations using Matplotlib and Seaborn to present machine learning results clearly and insightfully.

  • Implement Advanced Regression Techniques

    You will build and optimise regression models to predict outcomes effectively, applying these techniques to solve data-driven challenges using real-world datasets.

  • Excel in Classification Algorithms

    You get to design and deploy classification models to categorise data accurately, addressing real-world problems across diverse sectors.

  • Utilise Clustering for Data Insights

    You will leverage clustering algorithms to segment datasets efficiently, enabling actionable insights and informed decision-making in real-world scenarios.

  • Work on Industry-Focused Machine Learning

    Participate in projects like sentiment analysis and predictive modelling, gaining practical experience with real-world machine learning applications.

Machine Learning Course Outcome and Career Opportunities in Qatar

Python is one of the most popular programming languages in AI/ML development, provides powerful libraries like Scikit-learn, TensorFlow, and Pandas, which will be covered throughout the course. Our Machine Learning Training in Qatar is designed to equip learners with the essential skills and practical knowledge needed to build intelligent systems using Python. After completing the Machine Learning Training, you will be able to:

Course Outcome Image
Understand core machine learning concepts including regression, classification, clustering, and dimensionality reduction.
Gain hands-on experience in solving real-world problems by developing models and deploying them efficiently.
Use Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow to build and evaluate ML models.
Perform data preprocessing, feature selection, and data visualisation.
Develop and fine-tune machine learning models using best practices.
Apply machine learning algorithms to solve practical problems and business cases.

Job Roles After Machine Learning Course in Qatar

  • Machine Learning Intern
  • Data Scientist
  • Data Analyst
  • AI/ML Associate
  • Machine Learning Engineer
  • Business Intelligence Analyst
  • Artificial Intelligence Specialist
  • Predictive Modeller
  • Data Engineer
  • NLP Engineer
  • AI/ML Architect
  • Principal Data Scientist
  • AI Research Scientist
  • Director of AI/ML Operations

Top Companies Hiring Machine Learning Professionals in Qatar

  • QatarEnergy
  • Qatar Airways
  • MEEZA QSTP
  • Qatar Computing Research Institute (QCRI)
  • Starlink ME
  • Ooredoo Group
  • SWATX Solutions
  • University of Doha for Science & Technology (UDST)
  • Gulf Overseas HR Consultancy
  • ECCO Gulf Majorel Qatar

Machine Learning Training Options

Classroom Training

  • 60-hour Machine Learning Course in Qatar

  • Hands-on practice with Python tools and machine learning libraries.

  • Direct instructor access for immediate doubt resolution.

  • Collaborative peer reviews to enhance coding quality.

Live Online Training

  • 60-hour online Machine Learning Course in Qatar

  • Flexible scheduling is available from Monday to Sunday.

  • Real-time instructor interaction in a virtual learning environment.

  • Digital course materials and AI tool tutorials provided.

Corporate Training

  • 5-day intensive program tailored to organisational needs.

  • Delivered in classroom or online formats as per preference.

  • Real-world projects aligned with business objectives.

  • Fly Me A Trainer option for tailored on-site training anywhere in the world

  • Full logistics handled, including venue options (hotel, client premises, or our premises)

  • Food and refreshments provided for corporate teams

Do You Want a Customised Training for Machine Learning?

Get expert assistance in getting your Machine Learning Course customised!

How to Get Machine Learning With Python Certified?

Here’s a four-step guide to becoming a certified Machine Learning professional.

Do You Want to be a Certified Professional in Machine Learning?

Join Edoxi’s Machine Learning Course

Why Choose Edoxi for Machine Learning Course in Qatar?

Here are a few reasons why you should choose Edoxi for a Machine Learning course in Qatar;

Globally Accredited Institute

We are a certified training centre delivering a structured curriculum blending theoretical knowledge with practical, industry-relevant applications.

Beginner-Friendly Python Training

Foundational Python lessons covering syntax and libraries prepare learners for advanced machine learning concepts.

State-of-the-Art Development Tools

Master Jupyter Notebook, Spyder IDE, Scikit-learn, TensorFlow, Keras, NumPy, Pandas, Matplotlib, and Seaborn.

Customised and Interactive Learning

You get to participate in coding sessions, debugging exercises, and tailored guidance to develop practical machine learning expertise.

Real-World Project-Based Learning

You will work on projects like price prediction and customer segmentation, building a professional portfolio with real-world datasets.

Industry-Recognised Certification

Earn a validated certification and learn from experienced AI professionals to boost career prospects in Qatar’s booming AI industry.

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Edoxi is Recommended by 95% of our Students

Meet Our Mentor

Our mentors are leaders and experts in their fields. They can challenge and guide you on your road to success!

mentor-image

Jothi Kumar

Jothi is a Microsoft-certified technology specialist with more than 12 years of experience in software development for a broad range of industry applications. She has incomparable prowess in a vast grouping of software development tools like Microsoft Visual Basic, C#, .NET, SQL, XML, HTML, Core Java and Python.

Jothi has a keen eye for UNIX/LINUX-based technologies which form the backbone of all the free and open-source software movement. As a Big data expert, Jothi has experience using several components of the Hadoop ecosystem, including Hadoop Map Reduce, HDFS, HIVE, PIG, and HBase. She is well-versed in the latest technologies of information technology such as Data Analytics, Data Science and Machine Learning.

Locations Where Edoxi Offers Machine Learning Course

Here is the list of other major locations where Edoxi offers Machine Learning Course

FAQ

What is a Machine Learning Course in Qatar?

Edoxi’s Machine Learning Course is designed to teach you how to build and deploy machine learning (ML) models using the Python programming language. Python is widely used in the data science and machine learning fields due to its simplicity, powerful libraries, and strong community support.

What are the prerequisites needed to join our Python Machine Learning course in Qatar?

To enrol in Edoxi's Python Machine Learning course, prior knowledge of Python programming is required. Although a background in calculus, probability, and statistics is not mandatory, it is beneficial for grasping machine learning concepts.


Participants who lack Python knowledge will receive additional classes that cover the basics, including Python syntax and libraries. These sessions aim to build a strong foundation, enabling students to effectively learn and apply machine learning techniques.

What are the job Roles and Salaries of Machine Learning with Python Professionals in Qatar?

The following table showcases the job roles and average salary after the Python Machine Learning Course in Qatar.

Career Stage Roles Average Monthly Salary (QAR)
Entry-Level Machine Learning Intern
Junior Data Scientist
Data Analyst
AI/ML Associate
Junior Machine Learning Engineer
Business Intelligence Analyst
QAR 7,000 – 15,000
Mid-Level Machine Learning Engineer
Data Scientist
Artificial Intelligence Specialist
Predictive Modeller, Data Engineer
NLP Engineer
QAR 15,000 – 25,000
Senior-Level Senior Machine Learning Engineer
Lead Data Scientist
AI/ML Architect
Principal Data Scientist
AI Research Scientist
Director of AI/ML Operations
QAR 25,000 – 40,000
How do I differentiate between Machine Learning, Deep Learning, and AI, and when should I use each?
Machine Learning (ML) trains models using data and is a subset of AI. Deep Learning (DL), a further subset, uses neural networks for complex patterns. Use AI for broad automation, ML for predictive tasks, and DL for applications like image recognition.
What are the key differences between TensorFlow and PyTorch, and how do I decide which to use?
TensorFlow is ideal for production projects due to its scalability, while PyTorch is better suited for research and experimentation thanks to its user-friendly interface.