Data Mining Algorithms Training Course

From 22 € /h

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

The course content 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

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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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I offer personalized support in data analysis, statistics and quantitative methods**, tailored to your academic or professional needs.

Whether you are a student, doctoral candidate, researcher, consultant or professional, I offer my expertise to support you through the various stages of your project:

Database preparation, cleaning and organization**
Statistical analyses with R, Python, Stata and SPSS**
Statistical and econometric modeling**
Analysis and processing of questionnaires**
Creating dashboards and interactive visualizations with R Shiny and Power BI**
Rigorous interpretation of results**
Creation of charts, graphs and visualizations tailored to your results**
Support in the presentation and dissemination of analyses** in the form of reports, dissertations, theses or scientific publications

Support tailored to your project

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I can intervene in the following areas:

🎓 Academic Projects
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💼 Professional projects
Quantitative studies • Data analysis • Modeling • Reporting • Dashboards • Visualization

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Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

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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 — 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
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• 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
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Python Level 1Workshop

**Course Description**

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**What You Will Learn**

* **Lesson 1: Your First Python Program** — Learn the `print()` function, display messages, and create simple programs.
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**Hands-On Projects**

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* Children and teenagers aged 8–16.
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**My Teaching Approach**

I explain concepts step by step, use relatable examples, and encourage students to write their own code instead of simply copying solutions. Lessons are adapted to each student's pace, with coding exercises and challenges to strengthen understanding.

**No prior coding experience is required.** Students need a computer and an internet connection to participate.

By the end of Level 1, students will have a solid foundation in Python basics and the confidence to start creating their own simple programs.