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Since November 2025
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Advanced Programming with Python – OOP, Data & Algorithms
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From 15 $ /h
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This advanced course builds on programming fundamentals using Python (programming language) and is designed for students who want to deepen their programming knowledge.

The course continues from functions and introduces advanced programming concepts including object-oriented programming principles such as polymorphism, inheritance, abstraction, and encapsulation.

Students will also learn data handling techniques, working with Python libraries, and developing structured programs using complex loops and data collections.

The course covers practical implementation of nested loops, nested lists, tuples, and dictionaries, as well as an introduction to data structures and algorithmic thinking.

Additional topics include graphical user interface development using libraries such as Tkinter, along with introductory concepts in data science and machine learning using Python.

Teaching combines theoretical explanation with real coding exercises to help students develop strong practical programming skills.

• Review of programming fundamentals and functions
• Object-Oriented Programming (OOP) concepts
• Polymorphism, inheritance, abstraction, and encapsulation
• Data structures basics
• Nested loops and complex data handling
• Lists, tuples, and dictionaries
• Introduction to algorithms
• Working with Python libraries
• GUI development using Tkinter
• Introduction to data science and machine learning concepts
Extra information
💻 Students need a laptop for coding practice.
✨ Suitable for students who completed beginner programming courses.
Location
location type icon
Online from Lebanon
About Me
With experience delivering more than 2500 coding and technology online training sessions across the UK, USA, Egypt, and Gulf countries, I have worked with students from different backgrounds and skill levels, helping them improve their technical knowledge and practical skills.

I am a university instructor specializing in computer science and information technology education. I have experience teaching and explaining complex technical concepts in a clear, structured, and practical manner.

I offer tutoring and training in programming languages including C++, Java, Python, and C#, as well as web development. I also teach computer networking, data communication, cybersecurity fundamentals, artificial intelligence (machine learning), robotics and introductory IoT concepts.

My teaching approach focuses on understanding core principles, solving practical problems, and applying knowledge through examples and exercises. I adapt my lessons according to the student’s level, whether beginner or advanced.

I am committed to helping students build strong technical foundations, improve their problem-solving skills, and gain confidence in working with technology.

All sessions are delivered in a supportive learning environment where questions and discussion are encouraged.
Education
• Bachelor’s Degree in Computer Science – GPA: 3.81
• Master’s Degree in Computer Science – GPA: 3.90
• Certified in Machine Learning Fundamentals and Engineering from IBM.
• Certified in Computer Fundamentals from ICDL program.
Experience / Qualifications
• University instructor with extensive experience in teaching computer science and information technology subjects.
• Delivered more than 2500 coding and technology training sessions to students across the UK, USA, and Gulf countries.
• Experienced in teaching programming languages including C++, Java, Python, and C#.
• Knowledgeable in computer networking, data communication, cybersecurity fundamentals, artificial intelligence basics, IoT, and robotics concepts.
• Skilled in explaining complex technical topics in a simple, structured, and practical manner.
• Focused on helping students develop problem-solving skills and strong technical foundations.
Age
Preschool children (4-6 years old)
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
This course provides a comprehensive and professionally structured introduction to computer networking and data communications. It is designed for students who aim to build a strong technical foundation aligned with industry standards such as the Cisco Certified Network Associate (CCNA).

Course Topics Include:

Network architecture and reference models (OSI and TCP/IP)

IPv4 addressing, subnetting, CIDR, and basic IPv6 concepts

Switching fundamentals (MAC address tables, VLANs, trunking)

Routing principles and static routing configuration

TCP and UDP protocols, ports, and socket communication

Core network protocols (ARP, ICMP, DNS, DHCP, HTTP/HTTPS, FTP)

Network infrastructure devices (routers, switches, firewalls, wireless access points)

Introduction to network security fundamentals

Basic network troubleshooting methodologies and CLI analysis

The teaching methodology combines structured theoretical explanation with practical examples to ensure students understand how real-world networks operate in enterprise and campus environments. Packet flow analysis and scenario-based exercises help bridge theory with practical implementation.

Target Audience:

High school and university students in Computer Science, Information Technology, or Engineering

Beginners preparing for CCNA certification

Individuals seeking a strong networking foundation before advancing to cybersecurity, cloud computing, or advanced infrastructure studies

By the end of the course, students will confidently understand how data travels across networks, how routing and switching function, and how modern network infrastructures are designed, implemented, and managed.
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This course is designed for complete beginners who want to build strong basic computer skills and understand essential computing concepts.

Students will learn computer fundamentals including hardware components, input, processing, and output concepts, as well as basic operating system operations such as file management and system handling.

The course also introduces practical productivity skills using Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneDrive.

The teaching approach combines explanation and practical exercises to help students confidently use computers for academic and everyday tasks.

This course is ideal for beginners who want to start learning computer science concepts and improve their computer productivity skills.
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Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

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• Research and quantitative applications
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MACHINE LEARNING
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verified badge
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This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

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• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
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• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
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My courses are aimed at students of all levels and objectives: refresher courses, preparation for tests, exams or in-depth study of concepts.

Each session is tailored to the student's needs. We begin by identifying difficulties, then review the concepts covered in the lesson before applying them through exercises of increasing difficulty. I place great importance on understanding concepts and developing reasoning skills rather than rote learning.

The courses take place exclusively online, which allows for efficient work on the course material, exercises and corrections in real time.

As an engineering student at ENSIMAG and a former student of MPSI-MP preparatory classes, I leverage my strong background in mathematics and computer science to guide students with rigor, patience, and effective teaching methods. My goal is to help them make lasting progress, gain confidence, and become independent in their work.
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