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Since December 2017
Instructor since December 2017
Java, C, C# Programming for University Students in all levels
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From 60 C$ /h
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Learning computer programming for university students in all levels. Could be very basic or for advanced courses. Suitable for reinforcement for university courses of: Java. Object Oriented, Data Structure, Advanced Java, Software Engineering, Database, Web Development.. etc. Can be given in: Java, C, C#, Javascript

Course Outline:

Section 1 - Getting Started

Java Basics
Data Types
Arrays and References
Operators and Constructs
Java Objects
Dynamic Memory Allocation
Java Methods
Java Strings


Section 2 - Cla1sses and Objects

Class Design
Fields and Access Control
Constructors
Method Overloading
Static Methods
Inheritance
Method Overriding
Using final and super
Abstract Classes and Methods
Dynamic Binding
Polymorphism


Section 3 - Working with Classes

Using instanceof
Interfaces
Exception Handling
Exception Objects
throw points, throws clause
try, catch, finally


Section 4 - User Interfaces

Window Applications
Layout Managers
Event Handlers and Listeners
Anonymous Classes and Lambdas
Java Swing APIs
Basic GUI Controls
Menus and MenuBars


Section 5 - Generics and Collections

Why Use Generics?
Generic Classes and Interfaces
Generic Iterators
Collections
ArrayList, LinkedList, HashMap


Section 6 - Threads

Thread States
Extending the Thread class
Timer Thread
Implementing the Runnable interface


Section 7 - File I/O

Input and Output Streams
Binary and Text Files
Files and Directory Methods
Extra information
Lessons will be in English
Location
location type icon
Online from Israel
About Me
Led software projects and machine learning algorithms that solve real-life problems from scratch into production.

8 years of experience with software engineering , algorithm development and customer-facing experience.

Professional mentor and technical consultant; completed 220+ mentoring sessions with 5.0/5.0 rating on codementor.io; have been selected for 7 times as featured mentor of the week.

Developed ML algorithms for prediction of customer purchase behavior, customer segmentation, and future purchase status.

Masters graduate in computer vision and machine learning; introduced a new method of Multiple Object Tracking using Kernelized Correlation Filters which increased tracking accuracy by 4%.
Education
Bahçeşehir Üniversitesi
M.A. of Computer Engineering
2014 – 2017
Areas of Study: Computer Vision. Artificial Intelligence. Machine Learning. Cyber Security. Network Cryptography

Birzeit University
B.A. of Computer Engineering
2007 – 2012
Experience / Qualifications
Lead Software Engineer

Nov 2020 - Present (3 years 1 month)
Leading the technical design and implementation of a SAAS AI-based IT support tickets automatic
routing service. In addition to data ETL process and customer-facing follow up and support.
Technologies: Python, PostgreSQL, Node.js, Google Cloud GCP, Containers, Kubernetes,
Microservices, Airflow, Git


R&D Team Lead

2019 - Aug 2020 (1 year)
Leading a team of 4 developers in full-stack development and architecture of an e-commerce live
solution. In addition, developing machine learning algorithms for prediction of customer behavior.
Technologies: Node.js, Python, AWS, MongoDB, Redis, MySQL


Mentor
Codementor
Aug 2018 - Jul 2020 (2 years)
Providing software and technical consultancy, mentorship and support through one-to-one live sessions
for tasks in various technologies and programming languages.
Achieved more than 220 sessions with a rating of 5.0/5.0, and have been selected as a "Featured
Mentor" for six times.


Senior Software Developer

2017 - Jun 2018 (1 year)
Design, development and maintenance at 3 e-commerce projects. Starting from system design of
entities and components to implementation and maintenance.
Technologies: .NET, C#, SQL, Entity Framework, Blockchain Network, JavaScript, HTML, CSS
Achievements:
- Design and implementation of a Bitcoin mining web platform and Blockchain transaction
- Implementation and maintenance for e-commerce website


Full Stack Developer

2015 - 2017 (2 years)
Development and maintenance for e-commerce web site. Implementing payment methods API’s and all
other forms and views.
Technologies: .NET, C#, SQL,Javascript, HTML, CSS
Achievements:
o Integrated multiple payment methods and services: Papara, Wirecard Mobile Payment, Inininal
o Created new Coupon system for discounts and mailing and SMS messaging system for automated
daily messages


Software Engineer

May 2011 - Feb 2014 (2 years 10 months)
Worked on Cisco’s classification engine that recognizes a wide variety of applications, including webbased and other difficult-to-classify protocols that utilize dynamic TCP/UDP port assignments
Built Web applications in JavaScript. The application was used by company’s customers to display and
monitor web traffic and show different details in charts and tables
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
30 minutes
60 minutes
The class is taught in
English
Arabic
Hebrew
Turkish
Reviews
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
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Master pseudo-code algorithms in this hands-on course with dozens of different algorithms

In this course, you will learn the basics of computer programming through the fundamental subject taught in all higher schools of computer science: algorithms.

This is the initial stage of your learning to become a computer scientist (programming)


First we will see a broad introduction to computer programming, and we will explain what algorithms are.

Then, you will learn the language of computer scientists by studying "pseudo-code", and you will learn all the concepts of computer science through a multitude of practical exercises.

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Introduction
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- ...


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These algorithms are applicable in all programming languages.


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With more than 8 hours of e-courses, quizzes, and an assessment, you will have what you need to continue your learning of computer programming and advance towards your future profession.

Who is this course for?
Beginner in programming
Retraining
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Ammar
A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

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• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.
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Nuria
Do you want to learn Artificial Intelligence from scratch or do you need support with a subject, practice or project related to AI?

The classes are online, one-on-one, and fully tailored to your level and goals. We can work from the fundamentals to practical applications using Python, generative AI tools, language models, and APIs.

We can work on content such as:

fundamentals of Artificial Intelligence;
Python applied to AI and data processing;
data preparation, cleaning and analysis;
NumPy, pandas and data visualization;
Introduction to Machine Learning;
classification, regression and model evaluation;
Generative AI and Language Models (LLM);
use of ChatGPT, Gemini and other AI tools;
design and improvement of prompts;
consumption of AI model APIs;
task automation using AI;
AI integration in applications;
search and work with information and documents;
development of small projects and prototypes;
internships, projects and exam preparation.

The goal is not only to learn how to use AI tools, but to understand how they work, when to use them, and how to practically integrate them into your own projects.

We can start from scratch, work on the syllabus of your subject, or develop a specific application or project step by step.

In addition to the classes, you will have access to our educational platform with its own documentation, exercises, examples, practices and other resources to continue working between sessions.

Additional information for the student

You can bring your own syllabus, practical exercises, data, or project. We will adapt the classes to your prior knowledge and the objective you want to achieve.
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Contact Ibrahim
repeat students icon
1st lesson is backed
by our
Good-fit Instructor Guarantee
Similar classes
arrow icon previousarrow icon next
verified badge
Enrique
Don't settle for anything less than excellence.
I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python.

With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas:
- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.
verified badge
Imad
Master pseudo-code algorithms in this hands-on course with dozens of different algorithms

In this course, you will learn the basics of computer programming through the fundamental subject taught in all higher schools of computer science: algorithms.

This is the initial stage of your learning to become a computer scientist (programming)


First we will see a broad introduction to computer programming, and we will explain what algorithms are.

Then, you will learn the language of computer scientists by studying "pseudo-code", and you will learn all the concepts of computer science through a multitude of practical exercises.

The topics covered are very broad and comprehensive:

Introduction
- Algorithm Syntax
- data type and Variables
- The operators
- The instructions
- Conditions
- The repetitive structure (loops)
- The tables
- Research techniques
- Sorting algorithms

- dichotomous search
- Functions
- The procedures
- Recursion
-complexity
- Introduction to the C language

- ...


Your first programs...

Finally, you will start programming by creating several algorithms in a specific programming language (here, C language).

These algorithms are applicable in all programming languages.


The goal...

With more than 8 hours of e-courses, quizzes, and an assessment, you will have what you need to continue your learning of computer programming and advance towards your future profession.

Who is this course for?
Beginner in programming
Retraining
Computer science students or future students

Thanks and see you soon !

IMAD
verified badge
Ammar
A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.
verified badge
Nuria
Do you want to learn Artificial Intelligence from scratch or do you need support with a subject, practice or project related to AI?

The classes are online, one-on-one, and fully tailored to your level and goals. We can work from the fundamentals to practical applications using Python, generative AI tools, language models, and APIs.

We can work on content such as:

fundamentals of Artificial Intelligence;
Python applied to AI and data processing;
data preparation, cleaning and analysis;
NumPy, pandas and data visualization;
Introduction to Machine Learning;
classification, regression and model evaluation;
Generative AI and Language Models (LLM);
use of ChatGPT, Gemini and other AI tools;
design and improvement of prompts;
consumption of AI model APIs;
task automation using AI;
AI integration in applications;
search and work with information and documents;
development of small projects and prototypes;
internships, projects and exam preparation.

The goal is not only to learn how to use AI tools, but to understand how they work, when to use them, and how to practically integrate them into your own projects.

We can start from scratch, work on the syllabus of your subject, or develop a specific application or project step by step.

In addition to the classes, you will have access to our educational platform with its own documentation, exercises, examples, practices and other resources to continue working between sessions.

Additional information for the student

You can bring your own syllabus, practical exercises, data, or project. We will adapt the classes to your prior knowledge and the objective you want to achieve.
Good-fit Instructor Guarantee
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Contact Ibrahim