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Since October 2023
Instructor since October 2023
Python Course for Anyone Who Want to Learn Python Programming!
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From 24 £ /h
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Python is a popular programming language for data science because it is easy to learn and has a wide range of libraries and tools that are specifically designed for data science tasks.

In this course, you will learn the basics of Python programming.

What you will learn:
• The fundamentals of Python programming, such as variables, data types, functions, and control flow.
• How to use Python libraries to manipulate and analyze data, such as NumPy, Pandas, and Matplotlib.
Why you should take this course:
• The course is taught by an experienced data scientist and engineer.
• The course is hands-on and project-based, so you will learn by doing.
• The course is affordable and flexible. You can learn at your own pace, on your own schedule.

Additional information:
• The course is suitable for anyone of all experience levels, from beginners to experienced professionals.
• The course is offered online, so you can learn at your own pace, on your own schedule.
• The course includes a variety of resources, such as example codes, code examples, and assignments.
• The course offers a money-back guarantee, so you can try it risk-free.

Extra information
- The course will start on October 23, 2023, and will be held every Monday and Wednesday from 18:00 to 21:00 for 6 weeks.
- The hourly price is for the whole program.
Location
location type icon
Online from Switzerland
About Me
Self-disciplined, systematic, productive, analytical, adaptable, and focused software engineer who gained all these abilities as a military electronics communication officer in multinational areas, offering tutorship experience and providing high-impact web development solutions.
Education
08.10.2018 – 18.01.2022
MASTER’S DEGREE, NATIONAL DEFENCE UNIVERSITY
Computer Engineering, Cyber Security Program, 3,61
30.08.2007 – 05.06.2008
POST-BACHELOR’S DEGREE, COMM. ELECTR. & INFO. SYS. SCHOOL AND TRAINING CENTER
Education and training of Communication Officer 87,619 / 100
19.09.2003 – 30.08.2007
BACHELOR’S DEGREE, NATIONAL DEFENCE UNIVERSITY (TURKISH MILITARY ACADEMY)
System Engineer 2,947
Experience / Qualifications
28.07.2022 – NOW
CYBER SECURITY ANALYST, DJANGO DEVELOPER, TUTOR KIRAZ SOFTWARE (MY PRIVATE COMPANY)
03.09.2021 – 27.07.2022
FULL STACK DEVELOPER, INFORMAGIC SRL.
06.04.2021 – 02.09.2021
WEB DEVELOPER, CYBER SECURITY CONSULTANT KIRAZ SOFTWARE
02.03.2020 – 05.04.2021
CO-OWNER - IT MANAGER, VARMI INC.
10.12.2019 – 02.03.2020
CYBER SECURITY CONSULTANT, ROOTCON IT COMPANY
08.10.2018 – 21.11.2019
COMMUNICATION INFORMATION SYSTEM PLAN OFFICER - PROJECT & PROGRAM MANAGER, SOFTWARE DEVELOPER, NATO
11.07.2016 – 07.10.2018
COMMUNICATION ELECTRONICS & IT COMPANY COMMANDER - PROJECT & PROGRAM MANAGER - SOFTWARE DEVELOPER, TR. ARMY
28.09.2012 – 10.07.2016
COMMUNICATION CENTER COMMANDER - PROJECT & PROGRAM MANAGER - SOFTWARE DEVELOPER, TR. ARMY
06.08.2008 – 27.09.2012
TASMUS PLATOON COMMANDER - PROJECT & PROGRAM MANAGER, SOFTWARE DEVELOPER TR. ARMY
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
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
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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

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• 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

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• 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

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• 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

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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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Online Microsoft Office Classes – Master Word, Excel & PowerPoint

Improve your digital skills with personalized online Microsoft Office lessons designed for beginners, students, professionals, and anyone who wants to work more efficiently.

Learn how to use:

✅ Microsoft Word – create professional documents, format texts, prepare reports, CVs, and academic projects.
✅ Microsoft Excel – manage data, create spreadsheets, use formulas, organize information, and analyze results.
✅ Microsoft PowerPoint – design attractive presentations, organize slides, and present ideas with confidence.

With step-by-step guidance, practical exercises, and lessons adapted to your level, you will develop essential computer skills for study, work, and everyday tasks.

📚 Learn from anywhere
💻 Flexible online classes
🎯 Personalized support and practical training

Start building your Microsoft Office skills and become more confident with Word, Excel, and PowerPoint!
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