Data Mining Algorithms Training Course

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From 18 £ /h

Data Mining Algorithms and Techniques Training Course - Beginner and Intermediate Level, for Computer Science Professionals and Non-Professionals.

The course content (according to my book published on Google Books), is titled: Advanced Analysis and Data Mining.
The book can be searched for using its name or the author's name.

Table of Contents

Chapter 1: Introduction to Advanced Analysis and Data Mining
1-1 What is data mining, its procedures and tools
1-2 What type of data is mined?
1-3 What are databases?
1-4 Relational Database
1-5 Query Language
1-6 Benefits of Database Mining
1-7 months data mining applications
A - Business Intelligence (Business Intelligence)
B - Internet search engines

Chapter Two: Data Recognition
2-1 Data Types, Characteristics, and Features
2-2 Statistical Description of Data
2-3 Visualization of Data
2-4 Measuring data similarity and difference
Chapter Three: Preparing Data for Analysis and Mining
3-1 The importance of preparing data for analysis and mining
3-2 Data Cleanup
3-3 Data Integration
3-4 Data Reduction
3-5 Data Transformation and Data Individualization

Chapter Four: Pattern Discovery and Exploration, Dependency and Correlation Rules
4-1 Basic Concepts
4-2 Shopping basket analysis (example)
4-3 Evaluating the dependency and correlation rules being explored
4-4 Mining Multi-Level Dependency and Linkage Rules
4-5 Mining multidimensional dependency and correlation rules
4-6 Rules of nominal and quantitative dependency and correlation
4-7 Exploring and identifying rare and negative patterns
4-8 Exploring and Determining the Rules of Dependency and Conditional Linkage
4-9 Evaluating dependency and correlation rules and distinguishing between useful and unhelpful ones
4-10 Measuring the type and strength of the relationship in dependency and correlation rules
4-11 Applications of pattern mining in practical life

Chapter Five: Analysis and Mining Using Classification and Prediction Algorithms
5-1 Basic Concepts
5-2 Classification using decision tree extrapolation
5-3 Classification using probability theory (hypothetical theory)
5-4 Classification using hypothetical network theory
5-5 Classification using correlation rules extrapolation
5-6 Classification using neural network algorithm
5-7 Classification using the nearest neighbor algorithm
5-8 Multi-category classification algorithms
5-9 Evaluating the efficiency and selection of classification algorithms

Chapter Six: Analysis and Mining Using Cluster Hashing Algorithms
6-1 Basic Concepts
6-2 Clustering by Division
6-3 Hierarchical Clustering
A. Hierarchical clustering
b. Hierarchical fission
6-4 Probability Clustering
6-5 High-Dimensional Clustering
6-6 Clustering of graphs and network data
6-7 Conditional Clustering
6-8 Cluster Segmentation Assessment

Chapter Seven: Analyzing and Mining Outliers and Complex Data Types
7-1 Basic Concepts
7-2 Types of extreme values
7-3 Ways to Explore Extreme Values
7-4 Complex Data Analysis and Mining

Chapter Eight: Planning Data Mining Operations and Their Applications in Society
8-1 Planning Data Mining Operations
8-2 Data Mining in the Community
8-3 Data mining applications in vital areas of society
8-4 Practical Application: Recommendation System Usage Scenario

Appendix 1: Database Fundamentals
Appendix 2: Data Warehouse Fundamentals
Appendix 3: Glossary of Data Mining Terms

Extra information

Online with explanations using simple examples that can be applied using Excel.

Location

Online from Palestine

About Me

A scientist and researcher specializing in mathematics and computer science.
A seasoned mathematics teacher, specializing in explaining, simplifying, and developing curricula based on scientific and research foundations.
A researcher specializing in designing and developing aptitude tests and thinking and analytical skills for students.
A part-time trainer and lecturer specializing in training courses in mathematics, statistics, financial mathematics, computer science, data science, systems analysis, database design and development, and data mining algorithms.

Education

Bachelor of Science degree in Mathematics and Computer Science from Ain Shams University in Cairo, 1991. He also holds other professional degrees, a Master's degree and a PhD in Computer Science specializing in Business Intelligence.

Experience / Qualifications

Over 30 years of experience in teaching mathematics, computer science, programming, systems analysis, data mining, and interdisciplinary research. Founder and director of the Interdisciplinary Research and Studies Center and its affiliated testing center.

Age

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Duration

60 minutes

The class is taught in

Arabic

English

Skills

Computer programming

Database

Algorithms

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

Mathematics for the General Secondary Stage, Science Section, All Branches

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Review sessions for specific lessons without long-term commitment. Foundation sessions in specific topics without connection to the rest of the curriculum.
A detailed explanation using examples from everyday life, and delivering information in the best way to suit the student's thinking.
Using the tools and techniques of the Arj Al-Manha, such as mathematical ability tests, intelligence tests, and other tests that I designed, developed, and published on a dedicated website called the Testing Center, affiliated with the Center for Multidisciplinary Research and Studies.
You can read the books I have published, some of which specialize in mathematics, including one titled "Creativity and Excellence in Mathematics 12".
For high school - science section, and published online.

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As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

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In this course, you will learn:

1. Programming & Algorithms
a) Programming fundamentals and problem-solving techniques
b) Algorithm design and data structures
c) Writing efficient and clean code

2. Python Programming (Beginner to Advanced)
a) Python basics, object-oriented programming, and advanced concepts
b) Using Python for real-world applications in:
I) Machine Learning
II) Computer Vision
III) Robotics
IV) Data Science
c) Scientific Python (NumPy, Pandas, Matplotlib)

3. Artificial Intelligence & Machine Learning
a) Core AI concepts and algorithms
b) Deep Learning fundamentals
c) Practical projects using modern AI tools and frameworks

Lessons are adapted to your background and goals, whether you are starting from zero, preparing for university courses, or looking to apply AI in your own projects.

Feel free to contact me if you have any questions. I look forward to helping you explore the world of AI!

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* Learning programming will allow students to develop their skills and will certainly allow them to meet the expectations of the future working world and emerging careers.

* In addition, learning programming allows the development of algebraic, algorithmic and computational thinking. Programming also helps to improve and develop students' sequencing ability, as well as their communication skills. Thus, there are several advantages to teaching programming, but the important thing is to remember that this learning teaches students that digital is not only for entertainment, but that it is possible to become creators. active and creative content.

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While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"

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🐍 Learn Python from Scratch — Think, Solve & Code!

Have you always wanted to learn Python programming but didn't know where to start?

Or maybe you've already started learning Python but some concepts still feel confusing?

Don't worry — you're in the right place! 😊

I'm Reza, a Computer Engineer, teacher, and technology enthusiast. I have a Master's degree in Computer Systems Architecture, I am currently studying Artificial Intelligence, and I have experience teaching programming and computer science to students with different backgrounds and skill levels.

🧠 Before Python: Learn How to Think Like a Programmer

For me, learning Python isn't just about learning commands and syntax. The most important part of programming is learning how to think when you face a problem.

Before jumping into code, we'll learn how to understand a problem, break it into smaller pieces, identify what information we have and what we need to find, and develop a step-by-step solution. Then we'll turn that solution into Python code.

We'll practice logical thinking, reasoning, problem-solving, algorithmic thinking, and debugging along the way. My goal is to help you become someone who can look at a new problem and think, "Okay, how can I solve this?" — not someone who only remembers Python syntax.

💻 What will you learn?

In my Python classes, we can start from the very beginning and gradually build your programming skills. We'll combine programming fundamentals with problem-solving and practical coding, so you understand not only how to write Python, but also how to approach a programming problem.

Depending on your level and goals, we can cover topics such as:

Python fundamentals and programming concepts
Variables and data types
Numbers, strings, and text processing
if statements and decision making
for and while loops
Lists, tuples, dictionaries, and sets
Functions and reusable code
Working with files
Error handling and debugging
Problem-solving, reasoning, and programming logic
Algorithmic thinking and step-by-step solution design
Object-oriented programming
Practical Python exercises and projects
Introduction to Python for Artificial Intelligence and Machine Learning
🎯 My teaching style

I don't want you to simply memorize Python commands.

I want you to understand how programmers think.

During our lessons, I explain concepts in simple language and then we practice them together. You'll learn how to analyze a problem, think about possible solutions, write code, test it, find mistakes, and improve your solution. You'll make mistakes, ask questions, and gradually become more confident.

I believe that learning by doing is one of the best ways to learn programming.

So instead of spending the whole lesson listening to me, you'll actually write Python code and practice what you've learned.

And don't worry if you're a complete beginner. You don't need any previous programming experience to start.

👨‍💻 Who are these Python lessons for?

My classes are suitable for:

Complete beginners who have never programmed before
Students who want to learn Python from the basics
University and school students
People who want to improve their programming skills
Anyone who wants to learn Python for personal or professional development
Students interested in eventually moving toward Artificial Intelligence, Machine Learning, or Data Science

I'll adapt the lessons to your level, your goals, and your learning speed.

🚀 Let's learn Python together!

Learning to program can seem difficult at first, but it doesn't have to be.

With the right explanation, enough practice, and a little patience, you can go from writing your first print() statement to building your own Python programs.

My goal is simple:

Think Clearly. Solve Problems. Learn Python. Build Something.

If you're ready to start learning Python, book your first lesson and let's write some code together! 🐍💻

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Description:
This course is a comprehensive introduction to database management, including design, administration, and integration into applications.

Goals :

Understand relational models and the use of the SQL language.
Create and administer efficient and secure databases.
Integrating foundations into modern applications.
Course methods and format:

Video courses: Guided practice on tools like MySQL or PostgreSQL.
Flexibility: Exercises adapted to your specific projects.
For who ?
Students, developers or professionals wishing to master databases.

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This course is designed to introduce students aged 7 to 16 to the world of programming through two of the most widely used and industry-relevant languages: C++ and Python.

The class provides a structured, age-appropriate pathway into programming, whether the student is a complete beginner or already exploring coding through platforms like Scratch or Code.org. Emphasis is placed on understanding logic, building problem-solving skills, and writing real code in a supportive, project-based environment.

Taught by an engineering student with hands-on experience in both C++ and Python, this course empowers students to explore the power of code and build a strong foundation in computational thinking — essential for future studies in engineering, robotics, AI, or game development.

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A (60-minute) tour for children and teenagers who already use technology, ChatGPT, images, games, and for families who want that curiosity to have method, limits, and a visible result.

No prior assessment is necessary. The first class serves to get to know the student, adapt the pace (also if there are special educational needs) and choose the type of project.

What we do in weeks

Weeks 1–2. How an AI “learns”
With short projects, we observe data, patterns, and biases. The goal is not to memorize definitions, but for students to be able to explain, in their own words, why a tool is right or wrong.

Weeks 3–4. Create with discernment
Art, stories, or game ideas using generative tools. We work on prompts, what can be published, privacy, copyright, and when not to use AI (for example, to copy homework).

Week 5. Final Project
A mini-project of their own: a co-written story, a series of images, a simple classifier, a game idea... Something they can show at home and explain how they did it.

What does the family take with them?
• a 5-week overarching theme, not random, aimless classes
• Safe usage habits that are also useful at school
• Finally, a completed project and a brief note on how to proceed.

If the student has SEN (ADHD, dyslexia, organizational or rhythm difficulties), I break the work down into shorter steps, use visual aids, and measure progress in specific tasks, not speed.

Farouk

4.88

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These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework

Ali

4.88

(85)

I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham.

What I cover:

Python for beginners and intermediate learners
C and C++ programming
GCSE and A-Level Computer Science (all exam boards)
University coursework support, debugging help, and project guidance
Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms

How I teach:
I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English.
If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.

Ammar

4.88

(85)

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.

Abdelmalek

4.88

(85)

This hands-on training pathway is designed to help students kickstart any project, specifically tailored for OT labs and industrial applications. Starting from absolute scratch, students will build a strong foundation in Python programming through practical, industry-relevant concepts.

Curriculum Outline: |
01 - Python Environment Setup & Basics |
02 - Python Variables, Numbers, Bytes & Hex |
03 - Control Flow Logic Functions |
04 - Data Structures (Lists, Tuples, Dictionaries & Sets) |
05 - String Formatting, Comprehensions & Exception Handling |
06 - File IO, Pathlib & Context Managers |
07 - Object-Oriented Programming (Classes & OOP) |
08 - Standard Library, Modules & Networking Basics |

Assessment & Evaluation:
Students will take a mini-test after the completion of each module. Additionally, an Audit & Performance Evaluation report will be sent following the tests.
Duration:
5 days to 15 days (depending on the pace of the cohort)

Baia

4.88

(85)

I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects