Java, C, C# Programming for University Students in all levels

From 60 C$ /h

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

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

Skills

Computer programming

Java

Software engineering

Reviews

Availability of a typical week

(GMT -04:00)

New York

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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electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
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RDM
Python,VHDL
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Python fundamentals: variables, loops, functions, data structures
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Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

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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- 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

3- 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

4- 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

5- 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

6- 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

7- 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

8- 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

9- 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

10- 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

11- 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.

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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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Core Fundamentals: Variables, loops, functions, and data structures.Object-Oriented Programming: Building reusable and clean code.Real-World Projects: Creating scripts, data analysis, or web scraping based on your goals.

Problem Solving: Learning how to debug and think like a programmer. Lessons are highly interactive. We will write code together from day one, and you will receive practical exercises after every session to build your confidence.